danial.ai
Orcest.AI
Conduct the Future
We're not building another chatbot. We're composing AI orchestras. Orcest.AI turns specialist AI agents into coordinated teams that plan, act, and learn—and MAF gets the first chair before the concert begins.
Your Once-in-a-Decade Shot
Some moments are defined by who dared to go first. This is one: invest ahead of the platform giants, not after them. You're being offered the kind of access most investors only see once—the opportunity to shape an entire category before it explodes.
The question isn't whether agentic AI will transform enterprise operations. The question is whether MAF will lead that transformation or watch from the sidelines as competitors seize the advantage.
Why Now: The Tempo Has Changed
Consumer AI is mainstream—and accelerating at a pace that rewrites decades of technology adoption patterns. Funding, talent, and data are concentrating around agentic systems with unprecedented velocity. The capital markets have spoken: investors poured $220M into a single agentic AI seed round in Paris, and Cognition's Devin captured Silicon Valley's imagination by demonstrating autonomous software engineering at scale.
The window is open—and narrowing. Every quarter that passes allows larger platform players to consolidate market position, build moats around their ecosystems, and lock enterprise customers into proprietary architectures. First movers in agentic orchestration will set the standards, accumulate the training data, and establish the customer relationships that become nearly impossible to displace.
This isn't hyperbole. This is the same inflection point we saw with cloud computing in 2008, mobile in 2010, and conversational AI in 2022. The difference? This cycle is moving faster.
Beyond Moore: From 24 Months to Months
01
Moore's Law (Historic)
Transistors doubled every ~24 months throughout the semiconductor era—a predictable, steady march that shaped technology planning for fifty years.
02
AI Training Compute (2012–2018)
AI training compute doubled every ~3.4 months, accelerating progress by an order of magnitude compared to traditional hardware evolution.
03
Current AI Era (2010–Present)
Updated datasets show ~6-month doubling for AI capabilities. This tempo drives strategic urgency—what takes a year today may take weeks tomorrow.
This acceleration fundamentally changes investment calculus. Traditional "wait and see" approaches that worked when technology evolved over years now guarantee obsolescence. The enterprises that commit to agentic infrastructure today will compound advantages while others are still forming committees.
The Law of Accelerating Returns
Ray Kurzweil's Law of Accelerating Returns proves that technological change is exponential on exponentials. New paradigms don't wait for old ones to plateau—they stack and compound, creating S-curves that nest inside larger S-curves.
We've witnessed this pattern repeatedly: mainframes gave way to PCs before reaching theoretical limits. Client-server architecture emerged while mainframes still dominated. Cloud displaced on-premise before full saturation. Mobile transformed computing while desktop remained relevant.
Agentic AI is the next paradigm shift—and it's arriving while conversational AI is still in early adoption. Those who recognize the pattern invest before the inflection point becomes obvious to everyone else.
Adoption Lightning: The ChatGPT Signal
100M
Users in 2 Months
ChatGPT achieved the fastest consumer app growth on record (UBS analysis)—demonstrating that mass behavior can pivot in weeks, not years.
23%
MAF Digital Growth
MAF's H1 2025 digital retail revenue jumped 23% YoY, proving that regional enterprises are ready to embrace AI-driven transformation.
4X
EBITDA Expansion
MAF's digital retail EBITDA multiplied 4× in H1 2025—evidence that technology investments translate directly to bottom-line performance.
These aren't abstract statistics—they're proof points that the market has fundamentally shifted. Consumer and enterprise audiences alike have demonstrated willingness to adopt AI solutions at unprecedented speed when those solutions deliver tangible value. MAF can ride the second wave: agentic automation at scale, building on the awareness foundation that ChatGPT established.
Unicorns, 2024–2025: The Agentic Thesis Goes Prime Time
H Company raised $220M in a seed round to build agentic foundation models—one of the largest seed investments in European AI history. The Paris-based team, led by former DeepMind researchers, attracted capital from Accel, Bpifrance, and other top-tier investors based purely on their vision for agents that can plan, execute, and learn autonomously.
This watershed moment signals that capital is chasing teams that act, not just chat. The investment thesis has matured beyond language models to orchestration systems that transform how enterprises operate. H Company's valuation premium reflects investor conviction that the next decade belongs to platforms enabling autonomous digital labor.
Orcest.AI is the orchestration layer that operationalizes action—we don't just build agents, we conduct them into symphonies of coordinated work that deliver measurable business outcomes. While others focus on individual instrument builders, we're creating the conductor that makes the entire orchestra perform as one.
Another Signal: Cognition/Devin
Cognition Labs' Devin—billed as the world's first AI software engineer—captured Silicon Valley's imagination and capital by demonstrating that agents can handle complex, multi-step engineering workflows with minimal human intervention. The "AI software engineer" narrative drove Cognition's valuation into unicorn territory as agentic coding matured from research concept to production reality.
Devin's success validates the core thesis: enterprises will pay premium prices for agents that can autonomously execute knowledge work. But coding is just one domain. We generalize beyond coding—Orcest.AI orchestrates cross-functional agent teams that span research, analysis, content creation, customer service, operations, and strategy.
Where Devin focuses on one vertical, we enable the entire enterprise to operate with autonomous intelligence.
MENA Precedent: Unicorn Scale is Real
Careem Exit: $3.1B
Uber acquired Careem in 2019 for $3.1 billion—the region's largest tech exit—proving that MENA can produce unicorn outcomes when founders solve regional problems with global-class execution.
Infrastructure Shifts
Careem capitalized on mobile penetration and digital payments infrastructure arriving in MENA. Today, AI infrastructure is arriving—and early movers can set global terms.
Regional Leadership
When infrastructure shifts, regional leaders can command premium valuations. Orcest.AI aims to be the region's agentic platform with MAF as co-builder and anchor customer.
The lesson is clear: timing and execution matter more than geography. Careem's founders didn't wait for Silicon Valley to solve ride-hailing for the Middle East—they built it themselves and captured extraordinary value. Orcest.AI follows the same playbook for agentic AI.
From Chat to Teams: What Changes
1
Single LLM
One model attempting to handle all tasks—jack of all trades, master of none, with limited context and no specialization.
2
Multi-Agent Crews
Specialized agents with defined roles, tools, and oversight—each excelling at specific functions while coordinating seamlessly.
3
Autonomous Teams
From "answering" to planning, executing, verifying. From assistants to autonomous digital teams that deliver outcomes, not just responses.
This architectural shift mirrors the evolution from mainframes to distributed computing. Single, monolithic systems give way to specialized, coordinated components that collectively outperform any individual unit. The future of enterprise AI isn't bigger models—it's smarter orchestration.
Why MAF Is the Ideal Co-Builder
Digital Scale Momentum
H1 2025: digital retail revenue +23%; digital EBITDA ×4. MAF is already winning with technology—Orcest.AI accelerates that trajectory with autonomous operations across the value chain.
Precision Media Synergy
Precision Media—MAF's AI-enabled retail media network—provides the perfect orchestration beachhead. Agents can optimize creative, audience targeting, and bid strategies in real-time, compounding ROAS.
Ecosystem Reach
Platform reach across Carrefour, VOX, 29 malls, 700k daily customers = instant deployment canvas. MAF's integrated ecosystem provides unique test-and-scale opportunities unavailable to standalone enterprises.
MAF isn't just an investor—you're the ideal design partner. Your operational complexity, data richness, and digital ambition make you the perfect environment to prove agentic orchestration at enterprise scale.
Orcest.AI in One Line
An operating system for AI teams
We orchestrate specialist agents that compose, coordinate, and self-improve. Just as an orchestra conductor transforms individual musicians into a cohesive performance, Orcest.AI transforms isolated AI capabilities into synchronized business outcomes.
Every enterprise will eventually need this layer. The question is whether they build it themselves over years of trial and error, license it from a platform giant with lock-in terms, or partner with a specialized orchestration platform that gives them control, flexibility, and sovereignty.
We are that specialized platform—and MAF has the chance to shape it from the ground up.
Eight-Plus Years in the Making
2016–2025: cognitive systems and multi-agent R&D matured from academic exploration into production-ready technology. Built in a university research center focused on intelligent systems and cognitive science, our team spent nearly a decade solving the fundamental challenges of agent coordination, memory architectures, and autonomous planning.
This is deep tech with working code and an MVP—not vaporware or slideware. We've already navigated the hard problems: how agents communicate, how they maintain context across complex workflows, how they recover from failures, and how they learn from experience without expensive retraining cycles.
The commercial launch moment arrives now because the enabling infrastructure—LLMs, vector databases, cloud compute—has finally caught up to our architectural vision.
The Team: Founders & Core Leads
Danial Samiei
Founder & CEO · Associate Professor, Qazvin Islamic Azad University · Founder and Director, Center for Intelligent Systems & Cognitive Sciences (2019-2025) · Former CEO, Gilan Research & Technology Fund
Omid Asgari
Co-Founder & Chief Science Officer · Faculty Member, Tokyo International University · AI & Machine Learning specialist with deep expertise in multi-agent systems and decision science
Meysam Shaygan
Chief Development Officer · CTO, Avid (Bayreuth, Germany) · Product Director driving AI innovation and business growth · Oversees technical development and product roadmap
Babak Asheri
Chief Technology Officer · CTO, Harkat Aval (Hamrah Aval investment arm, Iran) · Proven experience building scalable enterprise systems and infrastructure
Touba Hamidi
Co-Founder & Business Development Lead · Secretary, BRAINVEST National Investment Program · Board Member overseeing business development and capital strategy
Mostafa Borhan
Board Member & Strategic Advisor · Serial entrepreneur with multiple successful startup exits · Contributes commercial strategy and scaling expertise
Roxanne Varza
Strategic Mentor - Director @ STATION F / Scout @ Sequoia Capital / Innovation lead for AI Summit
Pouya Paknejad
Media Advisor - Venture builder specializing in Impactful and Innovative solutions | Striving to maximize value creation for both business and society by decoding decisions through applied neurobranding
Academic + industry blend with deep expertise in agentic systems, product development, and go-to-market execution. Full relocation readiness to Dubai and build-for-region mindset. This is an operator-researcher team with shipping discipline—not pure academics or pure coders, but professionals who understand both the science and the business.
Architecture: The Conductor Pattern
Manager Agent
Plans mission steps, allocates tasks to specialists, audits progress, and makes strategic decisions about workflow routing.
Research Agent
Gathers information from documents, databases, APIs, and web sources—synthesizing findings for downstream agents.
Coding Agent
Writes, tests, and debugs software components—handling technical implementation tasks with version control and quality checks.
Data Agent
Analyzes datasets, runs queries, generates insights, and produces visualizations—turning raw data into actionable intelligence.
Customer Agent
Handles inquiries, resolves issues, personalizes communications, and escalates when human judgment is required.
Orchestrator Core
Enforces policies, manages retries, routes tool access, maintains state, and ensures coordinated execution across all agent roles.
This architectural pattern enables true specialization—each agent masters its domain while the orchestrator ensures they work as a cohesive unit. Like a conductor leading an orchestra, the system transforms individual capabilities into harmonious performance.
Memory & Continuous Learning
Short-term state management keeps agents aware of immediate context—what just happened, what's in progress, what's queued next. But the real power comes from vectorized long-term memory that persists across tasks and time. Agents can recall relevant patterns from previous missions, apply lessons learned to new challenges, and recognize when past solutions are relevant to current problems.
This architecture enables improvement without expensive fine-tuning. Traditional AI systems require retraining on massive datasets every time you want to update their behavior. Our agents learn from their execution history, building up a knowledge base of what works, what fails, and what context matters for each type of task.
Compound quality over time is our wedge. While competitors start from zero with each deployment, our system gets smarter with every mission. An agent that has orchestrated 1,000 customer service workflows knows patterns that a newly deployed agent cannot match. This creates a moat that widens with adoption—your MAF deployment becomes progressively more valuable as the agents accumulate domain expertise.
Human-in-the-Loop, No/Low-Code
Visual Workflow Design
Drag-and-drop flow builder with auditable steps and dependency visualization. Business users design processes without writing code—defining agent roles, decision points, and success criteria through intuitive interfaces.
Approval Gates & Oversight
Configure which decisions require human approval, set spending limits, define escalation paths. Trust by design: visibility, controls, and overrides ensure humans remain in command of critical workflows.
Technical Extension Points
Engineers can extend agent capabilities by plugging in custom tools, APIs, or logic modules. The platform balances accessibility for business users with flexibility for technical teams.
The no-code interface doesn't limit power—it democratizes access. Marketing teams can design campaign orchestration workflows. Operations managers can automate exception handling. Customer service leads can tune response policies. Meanwhile, IT retains full control over security, data access, and system integration.
This dual nature—accessible yet powerful—accelerates adoption because stakeholders across the organization can contribute without bottlenecking on engineering resources.
Tools, Models, Freedom of Choice
Plug any LLM, vision model, or speech-to-text system into the orchestration layer. Bring your own APIs, databases, and private data sources. Deploy in public cloud, private cloud, or on-premise for sovereignty-sensitive workloads. This is your stack, our orchestra.
Unlike platforms that lock you into proprietary models or cloud providers, Orcest.AI treats compute and models as swappable components. Want to use OpenAI for creative tasks, Anthropic for reasoning, and an on-premise model for sensitive data? Done. Need to switch providers based on cost or performance? Configure it in minutes.
This architectural independence protects your investment. As the AI landscape evolves—new models emerge, prices shift, regulations change—you adapt by swapping components rather than rebuilding your entire automation infrastructure.
Antifragility as a Feature
"Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors."
— Nassim Nicholas Taleb, Antifragile
Most systems resist shocks and aim for robustness—they try not to break when things go wrong. Ours gets better when challenges arise. Self-healing flows detect failures and try alternative approaches. Retry trees explore multiple solution paths when initial attempts fail. Model switching enables agents to escalate to more capable (or more cost-effective) models based on task difficulty. A/B learning continuously tests variations to identify optimal strategies.
This conceptual foundation in antifragility means that production use in messy, real-world environments actually strengthens the system. Edge cases become training signals. Failures become opportunities to expand the solution space. The more your business throws at Orcest.AI, the more capable it becomes—not through manual updates, but through built-in learning mechanisms that treat stress as growth fuel.
Enterprises that adopt early benefit from this compound advantage—their agents mature faster because they're exposed to more complexity sooner.
Retail CX (Carrefour): What Changes Day-1
01
24/7 Agentic Triage
Agents field customer inquiries around the clock—answering product questions, checking order status, resolving common issues—with escalation to humans only when judgment calls are needed.
02
Ticket Deflection & Resolution
Automated first-contact resolution for 50-70% of inquiries. Faster response times. Lower cost per interaction. Higher customer satisfaction from immediate, accurate answers.
03
Insights Loop
CX agents detect patterns in customer questions and sentiment, feeding insights back to merchandising, marketing, and operations teams for continuous improvement.
For MAF's Carrefour operations—handling hundreds of thousands of customer touchpoints daily—this translates to millions in annual savings while improving service quality. The agents don't just cut costs; they create better customer experiences that drive loyalty and repeat purchases.
Digital Commerce: Precision Media Boost
Creative Optimization
Agents A/B test copy, imagery, and messaging continuously—learning which creative resonates with each audience segment and time of day.
Audience Targeting
Real-time analysis of customer behavior, purchase patterns, and contextual signals to serve hyper-relevant ads and offers at optimal moments.
Bid & Budget Management
Automated budget pacing and bid strategies across channels. Media ops that used to take hours now take minutes—and ROAS compounds through continuous optimization.
MAF's Precision Media network becomes dramatically more effective when orchestrated by agents that never sleep, continuously learn, and optimize across hundreds of campaigns simultaneously. Brands see better results. MAF captures more ad spend. Customers receive more relevant offers. Everyone wins.
In-Mall Experience
Agents coordinate wayfinding, event information, and concierge responses across MAF's 29 malls. A shopper asks about parking? The agent knows current availability by location. Looking for a specific store? The agent provides personalized directions. Want dinner recommendations? The agent suggests options based on preferences and current wait times.
Hyperlocal offers orchestrated across tenants—if a customer lingers near a fashion store, an agent might trigger a time-sensitive offer from that retailer. If someone buys movie tickets at VOX, an agent could suggest a nearby restaurant with pre-cinema dining deals.
All interactions feed back into tenant analytics, providing mall operators and retailers with insights about customer flow, preferences, and conversion patterns that were previously invisible.
Supply Chain & Inventory
Demand Sensing
Agents analyze sales velocity, seasonal patterns, external signals (weather, events, social trends) to forecast demand with higher accuracy than traditional statistical models.
Dynamic Replenishment
Adjust reorder points, safety stock levels, and transfer quantities in real-time. Reduce out-of-stocks while minimizing excess inventory and waste.
Intelligent Markdowns
Agents recommend markdown timing and depth based on inventory age, demand trajectory, and competitive pricing—maximizing sell-through while protecting margins.
Supplier Negotiation
Autonomous agents conduct routine supplier communications with policy-bounded autonomy—handling RFQs, order confirmations, and exception resolution while escalating strategic decisions.
Shrink and stockout reductions via proactive exceptions—agents catch anomalies before they become problems. A shipment delay triggers automatic rerouting. Unexpected demand spikes activate backup suppliers. Expiring inventory prompts promotional campaigns. The supply chain becomes self-correcting and adaptive.
Finance, HR, IT—Shared Services
Finance Operations
AR/AP bots reconcile invoices, flag discrepancies, and escalate with evidence packets. Month-end close accelerates by 40%. Audit trails are automatic and comprehensive.
HR Automation
Agents handle policy Q&A, onboarding workflows, benefits enrollment, and compliance documentation. New hire experience improves while HR teams focus on strategic talent initiatives.
IT Support
Runbook automation for common incidents—password resets, access provisioning, system health checks. L1 support tickets resolved instantly. IT staff liberated for innovation projects.
Across all shared services, the pattern repeats: agents handle routine, rule-based work with perfect consistency and audit trails, while humans focus on judgment calls, relationship management, and strategic initiatives. The result is faster processes, lower error rates, and higher employee satisfaction on both sides of the equation.
Competitive Landscape (At a Glance)
Open Frameworks
LangChain, LangGraph, CrewAI—excellent builder tools for developers, but require extensive integration work and lack enterprise governance features.
Point Solutions
Devin, AutoGPT, specialized coding or research agents—solve specific problems well but don't orchestrate cross-functional workflows.
DIY Approaches
Enterprises building proprietary agent systems—high initial investment, long time-to-value, no ecosystem benefits from shared learning.
Platform Giants
Microsoft, Google, Amazon building agent capabilities into their clouds—powerful but with lock-in risks, limited customization, and sovereignty concerns.
Orcest.AI occupies the strategic middle ground: enterprise-grade platform with sovereignty, customization, and cross-function orchestration. We're the layer business leaders can adopt, not assemble.
CrewAI vs. Orcest.AI
CrewAI
  • Open-source framework for building agent crews
  • Developer-first: requires coding expertise
  • Great for prototypes and custom implementations
  • Limited governance, audit trails, or enterprise controls
  • Each deployment starts from scratch
Orcest.AI
  • Production platform with built-in orchestration
  • Business-user accessible with technical extensibility
  • Enterprise-grade security, compliance, and audit
  • Memory and learning across deployments
  • On-premise and hybrid cloud options
We're the layer business leaders can adopt, not assemble. While CrewAI serves the developer community well, Orcest.AI provides the production-ready infrastructure that enterprises need to deploy agents at scale with confidence.
Devin vs. Orcest.AI
Devin focuses brilliantly on autonomous software engineering—demonstrating that agents can write code, debug issues, and implement features with minimal human guidance. It's a powerful point solution for development teams.
We orchestrate entire initiatives. Orcest.AI can embed a coding agent (including Devin-like capabilities) as one specialist within a larger team. But we add research agents that gather requirements, analysis agents that evaluate technical approaches, QA agents that verify outputs, documentation agents that maintain knowledge bases, and growth agents that monitor adoption and gather feedback.
The workflow goes: Program → Product → Launch → Optimize. Devin excels at the first step. We coordinate all of them, ensuring that great code becomes successful products that customers actually use.
AutoGPT vs. Orcest.AI
AutoGPT Challenges
Early agent demos suffered from loops and brittleness—agents would get stuck in repetitive cycles, make unreliable decisions, or fail without graceful recovery mechanisms.
Our Guardrails
Bounded loops, review checkpoints, persistent memory, automatic fail-safes—every execution path has circuit breakers and recovery logic built in from the start.
Enterprise-Grade
We are production-ready where hobbyist stacks stall. Reliability, audit trails, policy enforcement, and human oversight aren't afterthoughts—they're architectural foundations.
The difference between a research demo and production system is immense. AutoGPT proved the concept was possible. Orcest.AI makes it practical for enterprises with real money, real data, and real consequences on the line.
Safety, Guardrails, and Audit
Access Control
Role-based access, tool authorization levels, policy sandboxes that define what each agent can and cannot do. No agent operates without defined boundaries and permissions.
Security Testing
Red-teaming and evaluation protocols built into the development cycle. P0 kill-switches allow instant shutdown of any agent or workflow exhibiting unexpected behavior.
Full Provenance
Every agent action logged with timestamps, inputs, outputs, and decision rationale. Complete mission provenance enables compliance audits, debugging, and continuous improvement.
Trust isn't assumed—it's engineered. Every architectural decision prioritizes safe, controlled autonomy. We enable agents to act independently within defined boundaries, with humans always retaining ultimate authority and visibility.
The Product Today
MVP Status: Multi-step planning, code generation and execution, data analysis, content creation—all working today. Not vaporware. Not slideware. Actual software processing real workflows.
Components: Graphical user interface for workflow design. Agent library with pre-built specialists. Connector framework for APIs and data sources. Mission monitoring dashboard with real-time visibility.
Readiness: Ready for controlled pilots with design partners like MAF. The platform handles production workflows today—we're focused on expanding domain coverage, hardening enterprise features, and accelerating time-to-value.
We're not selling futures—we're selling capability that exists now and will compound rapidly through deployment learning.
90-Day MAF-Accelerated Plan
1
Day 0–30: Pilot Definition
Finalize pilot scopes (Precision Media + CX). Deploy sandbox environment. Integrate with MAF systems. Train stakeholders. Establish success metrics and governance cadence.
2
Day 31–60: Instrumentation
Instrument workflows with monitoring and logging. Set KPI baselines from current operations. Run shadow-mode where agents observe and recommend without acting, building confidence.
3
Day 61–90: Go-Live
Phased production rollout with human oversight. Weekly governance meetings with MAF stakeholders. Iterate based on early results. Prepare expansion roadmap for next use cases.
Time-to-Market under 3 months—aggressive but achievable with MAF's organizational support and our proven MVP. This pace sets the foundation for competitive advantage before others even start their evaluation cycles.
6–12 Months: From Pilots to Scale
Expand Use Cases
Roll out to supply-chain optimization and in-mall concierge services. Demonstrate value across different operational domains to build enterprise-wide confidence.
Geographic Expansion
Begin UAE → KSA rollout. Complete Arabic localization for optimal regional performance. Leverage MAF's regional footprint as deployment canvas.
External Validation
Target 3 signed LOIs → 3 production logos beyond MAF. Convert MAF success stories into replicable playbooks that accelerate sales cycles with other enterprises.
By the end of Year 1, Orcest.AI becomes a proven platform with multiple production deployments, regional presence, and a pipeline of enterprise prospects attracted by MAF's lighthouse success.
12–18 Months: The Profit Sprint
01
Vertical Playbooks
Package retail, supply chain, and CX orchestration as repeatable solutions. Reduce implementation time from months to weeks through templated workflows and best practices.
02
Partner Channel
Launch system integrator partnerships in UAE and KSA. Enable partners to deliver Orcest.AI implementations, multiplying go-to-market capacity without linear hiring.
03
Regional Lighthouse
MAF case studies become go-to-market anchors. "How MAF transformed CX with AI orchestration" attracts enterprise prospects across retail, hospitality, and logistics sectors.
04
Breakeven Achievement
Cumulative ARR crosses breakeven line in accelerated scenario. Strong unit economics and efficient go-to-market drive path to profitability ahead of base-case timeline.
Base Case & Accelerated Breakeven
Base case (current plan): Breakeven ≈ late Y2/early Y3 based on organic enterprise sales and measured scaling. Conservative customer acquisition assumptions and standard implementation timelines.
Accelerated case (with MAF access): Breakeven ≤ 18 months driven by faster pilots, consolidated procurement through MAF ecosystem, in-portfolio scale advantages, and amplified credibility from lighthouse customer success stories.
The difference isn't just capital—it's strategic acceleration through partnership. MAF provides deployment canvas, design feedback, reference architecture, and market credibility that compress typical SaaS scaling timelines by 12–18 months.
5-Year Financial Trajectory
$13M
Year 5 Revenue Target
~AED 50M annual revenue by 2030, driven by 10–15 enterprise contracts with expanding usage across departments and geographies.
40%
Net Margin
Software economics with 80%+ gross margins. Operating leverage drives ~40% net margin by Year 5 as revenue scales faster than costs.
5–10×
Value Creation
Enterprise valuation from ~AED 18M post-money to AED 90–200M by 2030, creating extraordinary returns for seed investors.
High-margin software economics after Year 2. Blend of top-down market sizing and bottom-up customer unit economics. Sensitivities and risk factors detailed in later sections, but core trajectory reflects proven SaaS scaling patterns for enterprise AI platforms.
Unit Economics & Pricing
Revenue Model
  • Platform subscription: Annual or multi-year licenses for orchestration infrastructure and agent libraries
  • Usage fees: Consumption-based pricing for compute and API calls beyond base allocations
  • Vertical packages: Pre-configured solutions for retail, CX, supply chain with faster time-to-value
  • On-premise premium: Enhanced pricing for sovereign cloud deployments with additional support
Cost Structure
  • Gross margins: 80–90% as agent libraries mature and deployment efficiency improves
  • CAC payback: <12 months for enterprise deals through direct sales and partner channels
  • LTV/CAC ratio: >5:1 in steady state, indicating efficient customer acquisition and strong retention
  • R&D investment: ~30% of revenue reinvested in product development to maintain technology leadership
Pricing follows value-based methodology—customers pay based on operational impact and automation value delivered, not just seats or compute consumption. This aligns incentives and enables expansion revenue as orchestration spreads across departments.
GTM Motions
Direct Enterprise Sales
UAE/GCC focus initially with senior sales talent targeting retail, logistics, hospitality, and financial services. Executive-level engagement, proof-of-concept methodology, and multi-year contract structuring.
MAF Channel Leverage
Co-selling opportunities through MAF's ecosystem—suppliers, partners, tenants. Joint case studies and reference architecture. MAF acts as credibility anchor and lighthouse customer.
System Integrator Partnerships
Enable regional SIs to deliver Orcest.AI implementations. Partner certification program, co-marketing, and revenue sharing. Multiplies reach without linear scaling of internal resources.
Category Design
"AI Orchestra" narrative + proof through MAF deployment. Thought leadership content, workshops, hands-on pilots. Own the category definition before competitors establish their positions.
Sovereign-Cloud & UAE Fit
Abu Dhabi AI Strategy
Abu Dhabi's AI-native government strategy includes AED 13B investment program. UAE aims to be first AI-enabled government by 2027—creating demand for sovereign AI solutions.
Data Sovereignty Requirements
Government and financial services demand on-premise or national cloud deployments. On-prem options are first-class citizens in Orcest.AI architecture, not afterthoughts.
Regulatory Alignment
Compliance with UAE data protection frameworks, DIFC regulations, and sector-specific requirements. Data gravity and sovereignty aligned by design.
Regional fit isn't coincidental—it's strategic. UAE's AI-first ambitions create perfect market conditions for sovereign agentic platforms. MAF's investment positions both organizations at the center of this regional transformation.
Security & Compliance
Identity & Access
SSO/SAML integration, multi-factor authentication, role-based access control. Tenant isolation ensures complete data separation in multi-tenant deployments.
Data Protection
Key management systems, encryption at rest and in transit. PII controls with automatic redaction capabilities. Data minimization and retention policies.
Audit & Compliance
Comprehensive audit trails for every agent action. Complete provenance enables regulatory compliance, forensic analysis, and continuous improvement.
Certification Roadmap
DPIAs (Data Protection Impact Assessments) and SOC 2 Type II certification on roadmap. GDPR compliance by design. Industry-specific certifications as needed.
Carrefour Now: Customer Service Pilot
Pilot Scope
CX triage and resolution for Carrefour's digital channels—e-commerce inquiries, delivery issues, product questions, loyalty program support. Start with 50 intent categories, expand to 200+ based on results.
Success Metrics
  • Response latency: <30 seconds average vs current 5+ minutes
  • First-contact resolution: ≥50% increase in FCR rate
  • CSAT deltas: Maintain or improve customer satisfaction scores
  • Cost per interaction: 40–60% reduction in fully-loaded costs
Weekly governance: Review performance dashboards, discuss edge cases, refine agent policies. Publish A/B uplift results to build organizational confidence and justify expansion to additional use cases.
Precision Media: Campaign Orchestration Pilot
1
Week 1–2: Creative Optimization
Agents test copy variations, imagery choices, and call-to-action language. Learn audience preferences through real-time A/B testing across Precision Media inventory.
2
Week 3–4: Audience Targeting
Refine targeting parameters based on performance data. Identify high-value micro-segments. Personalize messaging by customer journey stage and behavior signals.
3
Week 5–6: Bid Management
Agents optimize bid strategies and budget pacing across channels every hour. Adjust for time-of-day patterns, competitive dynamics, and conversion performance.
Guardrails: Brand safety filters, ROAS floor thresholds, spend caps. Dashboard provides live optimization visibility for marketing stakeholders. Success measured by statistically significant ROAS improvement vs control campaigns.
VOX Cinemas: Concierge Experience Pilot
Unified in-theatre and app experience orchestrated by agents. Customer interactions span ticketing, food & beverage ordering, seat upgrades, parking assistance, and personalized recommendations—all coordinated through a single conversational interface.
Use Cases:
  • Booking tickets with automatic seating optimization
  • Pre-ordering F&B for pickup at precise showtimes
  • Dynamic promotions based on viewing history
  • Cross-sell to mall dining or shopping
  • Post-visit feedback and loyalty rewards
"Moments that matter" analytics identify friction points and delight opportunities, feeding continuous improvement loop.
Use of Funds (AED 3M Request)
Product Development (~30%)
Core platform engineering, agent library expansion, eval harness, memory systems. Engineering talent acquisition and retention.
Infrastructure & Operations (~15%)
Cloud hosting, AI API costs, monitoring tools, security infrastructure. Scales with usage but architected for efficiency.
Go-to-Market (~10%)
Sales hiring, marketing content, event presence (GITEX, industry conferences), pilot support. Focus on UAE/GCC initially.
Legal & IP (~5%)
Company formation, IP protection, contract templates, regulatory compliance. Foundation for scaling across jurisdictions.
Buffer & Working Capital (~40%)
Prudent runway management with reserves for unexpected costs or opportunities. Ensures 18–24 month runway to Series A inflection point.
Hiring Plan (Lean, Senior-Heavy)
Dubai nucleus + global contributors: Core team relocates to Dubai for proximity to MAF and regional customers. Specialized roles may remain distributed for talent access and cost efficiency.
Year 1 Hiring Priorities:
  • Platform Engineers (3–4): Orchestration core, agent runtime, tool integration. UAE software engineer salary ~$54k average, but we target senior talent at $80–120k range.
  • Data Scientists (2): Evaluation frameworks, learning systems, analytics. Critical for continuous improvement and customer success metrics.
  • Enterprise Delivery Lead (1): Manages pilot implementations, customer success, and technical account management. Bridge between product and customers.
  • Sales/BD (1–2): Enterprise sales professional with GCC network. Compensated with base + commission on closed deals.
Benchmarked against UAE comp ranges via Levels.fyi and regional market data. Focus on quality over quantity—senior practitioners who can ship fast and operate autonomously in ambiguous environments.
Technology Roadmap (12 Months)
Q1–Q2: Core Hardening
Mission graphs evolve to policy-aware planners. Tool router becomes cost-aware and model-agnostic. Observability adds mission diffs and replay capabilities.
Q3: Enterprise Features
SSO/SAML integration. Advanced RBAC. Audit export APIs. On-premise deployment packaging. Multi-tenant isolation hardening.
Q4: Intelligence Layer
Self-healing workflow recovery. Automated A/B experimentation. Cross-mission learning transfer. Predictive resource allocation.
Roadmap balances innovation with production reliability. Every quarter delivers customer-facing value while advancing the underlying intelligence and autonomy of the system.
Data Strategy
Privacy by Design
Tenant-scoped memories: Each customer's data and learning isolated cryptographically. Private embeddings: Vector representations never leave customer boundary without explicit consent.
Redaction & minimization: Automatic PII detection and redaction. Data retention policies with configurable purge schedules. GDPR "right to be forgotten" automated.
Deployment Options
Cloud-native: Multi-tenant SaaS in AWS/Azure with regional data residency options.
On-premise: Containerized deployment for banks, government, and enterprises with sovereignty requirements. On-premise index option enables fully air-gapped operations.
Hybrid: Sensitive data on-premise, orchestration in cloud. Best of both worlds for many enterprises.
IP & Moats
Technical IP
Orchestration patterns, eval harness methodologies, memory schema designs. Agent coordination protocols that emerge from 8+ years of R&D.
Industry Playbooks
Vertical-specific orchestration recipes for retail, CX, supply chain, financial services. Templatized workflows that encode best practices and accelerate deployment.
Reference Deployments
MAF implementations become competitive moats—case studies, architectural patterns, integration templates. First-mover advantage compounds through deployment learning.
True moats aren't just patents—they're the accumulation of production learnings, customer relationships, and ecosystem effects that take years to replicate even with unlimited capital.
Scale & Reliability
Queue-Backed Execution
Asynchronous task processing with guaranteed delivery. Idempotent step design enables safe retries without side effects.
Failure Domains
Circuit breakers prevent cascading failures. Graceful degradation when external services are unavailable. Automatic fallback to simpler agents or human escalation.
SLOs by Role
Each agent role has defined service level objectives. Monitoring alerts when performance degrades. Continuous optimization based on real usage patterns.
Enterprise-grade systems aren't about never failing—they're about failing gracefully, recovering automatically, and learning from every incident. We architect for resilience from day one.
Customer Success Motions
Implementation Playbooks: Pre-built templates for common use cases reduce time-to-value. Guided onboarding walks customers through configuration, testing, and launch.
Health Scoring: Automated monitoring of usage patterns, performance metrics, and satisfaction signals. Proactive outreach when health scores decline.
Escalation Calendars: Defined touchpoints at 30/60/90 days and quarterly thereafter. Executive business reviews with ROI quantification and expansion planning.
Academy & Certifications: Training programs for customer teams—administrators, power users, business analysts. Certification validates proficiency and builds internal champions.
Operating Model
Product Guilds
Cross-functional teams organized around capabilities (orchestration, agents, integrations). Autonomy with alignment—teams own outcomes, not tasks.
Security & Policy Council
Regular governance forum for safety, compliance, and ethical AI considerations. Standing agenda items ensure these concerns never become afterthoughts.
Dual-Track Agile
"Ship small, scale fast" philosophy. Discovery track explores new capabilities while delivery track ships production features every sprint.
Board Cadence
Monthly operational dashboards for investors. Quarterly deep dives on product, market, and strategy. Transparent communication builds trust and enables strategic guidance.
Metrics That Matter
30D
Time-to-Value
Days from contract signature to first production workflow. Tracks implementation efficiency and customer success effectiveness.
85%
Task Completion Rate
Percentage of agent missions completed successfully without human intervention. Primary indicator of system reliability and capability.
<15%
Human Override Rate
Frequency of human corrections or interventions. Should decrease over time as agents learn. Spikes indicate areas needing attention.
Business Impact Metrics: Cost-per-outcome (comparing agent cost to human equivalent labor). ROAS/CSAT/NPS deltas by domain (quantifying business value, not just technical performance). Safety incidents = zero tolerance—any violation of policy boundaries triggers immediate review and remediation.
Proof Plan: 30-Day CX Triage Sprint
1
Week 1: Scope & Deploy
Select 50 intents from Carrefour's most common inquiries. Deploy agent in shadow mode. Begin collecting baseline metrics from current operations.
2
Week 2–3: Test & Tune
Run A/B comparison—agent responses vs human responses rated by quality team. Iterate on prompts, knowledge base, and escalation logic.
3
Week 4: Measure & Expand
Publish results: ≥X% FCR lift with compliant tone. If successful, expand scope to 200 intents and begin phased production rollout.
Success criteria: Statistical significance in FCR improvement. Customer satisfaction maintained or improved. Zero policy violations or brand-damaging responses.
Proof Plan: Precision Media 6-Week Lift
Control vs Treatment Design
Two comparable campaigns running simultaneously. Control uses current manual optimization. Treatment uses agent orchestration for creative, targeting, and bidding.
Continuous Optimization
Agents iterate hourly on bid pacing, creative rotation, and audience segment performance. Human marketers monitor dashboards but don't intervene unless safety issues arise.
Success: Stat-Sig ROAS Delta
After 6 weeks, measure statistically significant ROAS improvement in treatment vs control. Document cost savings from reduced manual effort. Publish results as Precision Media capability proof point.
Proof Plan: Supply Chain Exceptions
Agent Capabilities
Detect anomalies in inventory levels, shipment delays, demand spikes. Trigger corrective actions automatically within defined policy boundaries.
Human approval required for price changes, markdown decisions, or supplier contract modifications. Agent prepares recommendations with supporting data; human makes final call.
Success Metrics
Lower stockouts: 20–30% reduction in out-of-stock incidents through proactive reordering.
Reduced waste: 15–25% decrease in spoilage and markdown costs through better demand sensing.
Faster response: Average exception resolution time drops from days to hours.
Expected Impacts (Illustrative)
1
Cycle Time Compression
Processes that took days → hours → minutes. Marketing campaign setup, customer inquiry resolution, inventory adjustments—all accelerate dramatically.
2
Elimination of Handoffs
Manual handoffs shrink; agents coordinate across systems and teams. Fewer delays, fewer errors, fewer balls dropped between departments.
3
Human Focus Shift
Staff liberated from routine work to focus on creativity, judgment, strategy. Higher job satisfaction. Better business outcomes from applying human intelligence where it matters most.
The goal isn't replacing humans—it's amplifying human capability by removing the tedious, repetitive work that drains energy and creates errors. Agents handle the routine. Humans handle the exceptional.
Category Evangelism (with MAF)
"AI Orchestra" Events
Host invite-only workshops and demos with MAF pilots on stage. Operator stories > model specs—let business leaders see the transformation, not just the technology.
Regional Playbooks
Publish implementation guides for MENA retailers and enterprises. Position Orcest.AI + MAF as the blueprint for AI-driven operations in the region.
Thought Leadership
CEO speaking at GITEX, Arabian Business features, industry podcasts. Build personal and company brands as authorities on agentic AI and enterprise transformation.
Category creation isn't about being louder than competitors—it's about defining the terms of the conversation before competitors realize there's a conversation to have.
Strategic Deal Options for MAF
Lead Seed Investment
AED 3M for ~20% equity. Clean, founder-friendly terms. Post-money valuation ~AED 15M reflects deep-tech pedigree and near-term commercialization.
Governance Rights
Board seat (or observer rights). Information rights for financial and operational visibility. Pro-rata rights in future rounds to maintain ownership percentage.
Commercial Partnership
Optional co-sell agreements. Pilot credits for internal deployment. Preferential pricing for MAF group companies. Revenue sharing on joint customer wins.
This isn't just capital—it's a strategic partnership where both parties contribute and benefit beyond the cap table. MAF gets access and influence. Orcest.AI gets deployment canvas and credibility. Both win.
Optional Rights (Time-Boxed)
12-month preferred pilot access in retail media/CX domains. MAF gets first look at new agent capabilities before general release. Ensures competitive advantage window.
Co-marketing and case study rights: Joint press releases, conference presentations, co-branded content. Amplifies both brands' market positions.
First-look at Series A: Performance-linked option to lead or participate in next round at favorable terms. Rewards early-stage risk and relationship investment.
All optional rights are time-bound and conditional on mutual agreement—flexible framework that adapts as relationship and business evolve.
The Ask (Clean Terms)
AED 3,000,000 for ~20% Equity
Valuation
Pre-money ≈ AED 12M. Post-money ≈ AED 15M. Reflects 8+ years R&D, working MVP, clear commercialization path, and strategic MAF partnership value.
Instrument
Equity or SAFE/convertible note with valuation cap. Founder-friendly terms. No participating preferred or liquidation preferences beyond standard 1× protection.
Use of Funds
18–24 month runway to Series A inflection point. Staged deployment aligned with pilot milestones and customer acquisition targets.
Simple, clean deal structure that aligns long-term interests. We're building a company, not optimizing for short-term valuation games. Transparency and partnership over legal complexity.
Why This Valuation Is Fair
Deep-tech R&D sunk: 8+ years of academic research de-risked the core technology. We're not starting from zero—we're commercializing proven concepts with working code.
MVP live and functional: This isn't a pitch deck company. The platform handles real workflows today. Pilots can begin immediately upon investment close.
Clear GTM with design partner: MAF provides deployment canvas, reference architecture, and market credibility that would take competitors 12–24 months and significant capital to replicate.
Category heat with agentic comps: H Company's $220M seed at substantially higher valuation validates investor appetite for agentic platforms. Cognition's unicorn status for vertical agent solution suggests orchestration platforms command premium multiples.
Room for 10× value creation: AED 15M entry point leaves substantial upside ahead of Series A (target: AED 150–200M valuation in 18–24 months). Early investors capture the full growth trajectory rather than entering at inflated late-stage valuations.
Governance & Reporting
Monthly Operations Dashboard
Revenue, burn rate, customer metrics, product milestones. Transparent KPI reporting enables proactive course correction and strategic guidance from board.
Quarterly Product Risk Review
Technical debt assessment, security posture, scalability bottlenecks. Safety and ethics as standing agenda items—never relegated to "if we have time" status.
Audit-Ready Financials
Professional bookkeeping from day one. Annual audits as company scales. Financial controls appropriate for venture-backed enterprise software company.
Strong governance isn't bureaucracy—it's the foundation for scaling quickly while maintaining trust with investors, customers, and regulators.
Timeline to Close & Kickoff
T+30 Days
Term sheet negotiation and signature. Legal documentation (SHA, IP assignments, corporate structure). Close financing round.
T+45 Days
Pilots staffed and staged. Technical integration with MAF systems begins. Stakeholder training and governance framework established.
T+90 Days
First go-live. Shadow-mode transitions to production for initial use cases. Weekly governance meetings assess performance and plan expansion.
Aggressive but achievable timeline. Momentum matters in emerging categories—every quarter of delay allows competitors to close the gap. We're ready to move fast with committed partners.
Exits: Multiple Paths
Strategic Sale
Acquisition by platform company (Microsoft, Google, ServiceNow) or enterprise software stack (SAP, Oracle, Salesforce). Likely path if category consolidates quickly.
IPO
Public listing in regional market (DFM, Tadawul) or global exchange (NASDAQ, LSE) in high-growth scenario. Requires sustained revenue growth and profitability.
Secondary
Staged liquidity through secondary transactions in later rounds. Enables early investors to derisk while maintaining upside participation.
Multiple viable paths to liquidity give investors flexibility. We're building to win, not building to sell—but when the right opportunity emerges at the right valuation, we'll pursue it decisively.
Return Modeling
5×
Base Case
5× value increase in ~5 years (company value AED 15M → AED 75–90M). IRR ~38%. Conservative scaling with organic growth and measured international expansion.
10×
Upside Case
10× value increase in ~5 years (company value AED 15M → AED 150–200M). IRR ~58%. Accelerated adoption, category leadership, and potential strategic premium.
15×
Best Case
15× or greater if Orcest.AI captures regional market leadership and expands globally. Comparable to Careem's trajectory when infrastructure shifts enable breakout winners.
Assumes prudent dilution (20–30% in Series A, 10–15% in Series B) and execution on commercialization milestones. Even conservative scenarios deliver venture-scale returns.
Sensitivities
Compute & API Costs
Risk: AI model costs increase faster than pricing power. Mitigation: Model-agnostic router enables switching to most cost-effective providers. On-premise option bypasses third-party API costs entirely.
GTM Velocity
Risk: Enterprise sales cycles longer than projected. Mitigation: Partner SI channel multiplies reach. MAF lighthouse customer accelerates credibility and shortens cycles.
Regulatory Shifts
Risk: AI regulations impose compliance burdens. Mitigation: Audit-by-design architecture. On-premise deployment option addresses sovereignty concerns. Active engagement with regulators.
Talent Competition
Risk: War for AI talent drives compensation higher. Mitigation: Dubai base with competitive packages. Equity upside attracts mission-driven talent. Remote options for specialized roles.
Conservative runway management with 10% buffer protects against downside scenarios. Upside scenarios enable acceleration without additional dilution.
Risks We Name Now
"Risk comes from not knowing what you're doing." — Warren Buffett
Agent reliability: Complex workflows may fail in unexpected ways. Continuous eval coverage and shadow-mode testing mitigate this, but edge cases will emerge in production.
Data governance: Customer data sprawl across agent memories requires vigilant controls. Our architecture isolates tenant data, but operational discipline matters as much as technical safeguards.
Market noise: "Demo-ware fatigue" as every AI startup claims agentic capabilities. Differentiation through proven production deployments and measurable business outcomes crucial.
Technology pace: Foundation model capabilities evolving rapidly. Could make some orchestration patterns obsolete or require architectural pivots. Model-agnostic design provides insurance.
We acknowledge these risks transparently because managing risk requires naming it first. Investors should be clear-eyed about challenges alongside opportunities.
How We De-Risk
Eval-First Development
Every agent capability ships with evaluation harness. Mission replays enable regression testing. Quality gates prevent degradation.
Policy Sandboxes
Staged autonomy: Start with human-in-loop, expand boundaries based on proven reliability. Never grant full autonomy without evidence of safety.
Evidence-Before-Expansion
Pilot results drive investment decisions. Don't scale what hasn't been proven. Metrics-driven culture prevents wishful thinking.
De-risking is operational discipline, not just technical architecture. We build systems that can be trusted because we earn trust through repeated demonstrations, not through promises.
Ethics & Sustainability
Bias audits: Regular evaluation of agent outputs for demographic or systemic biases. Diverse evaluation datasets and human review panels.
Explainability packs: Every agent decision includes provenance—what data was considered, what reasoning was applied, what alternatives were evaluated.
Power draw monitoring: AI compute is energy-intensive. We measure and optimize for efficiency. Sober deployments—only automate where ROI justifies environmental cost.
Humans stay in command: Agents augment, never replace, human judgment on consequential decisions. Override mechanisms always available and prominently surfaced.
Why We'll Win
R&D Depth + Enterprise Practicality
Academic foundation ensures technical rigor. Commercial focus ensures business relevance. Rare combination of deep expertise and shipping discipline.
Antifragile by Design
Each shock trains the system. Production use strengthens rather than stresses the platform. Compound learning advantage widens moat over time.
Early MAF Proof → Regional Category Leadership
Lighthouse customer provides proof points that would take competitors years to replicate. First-mover advantage in defining regional agentic AI category.
We win by being first, being best, and being fastest to learn. The combination of technical sophistication, operational maturity, and strategic partnership with MAF creates a position that's very difficult to displace.
The "Before Intel/Microsoft/Apple" Moment
"The best time to plant a tree was 20 years ago. The second best time is now."
History remembers the ones who moved before the platforms consolidated. The investors who backed Intel before it became synonymous with computing. The enterprises that partnered with Microsoft before Windows dominated. The visionaries who saw Apple's potential before the iPhone redefined mobile.
This is that moment for MAF in agentic AI. The category is forming. The technology is proven but not yet mainstream. The leaders haven't been crowned.
We're inviting you to set the pace, not follow it. To be the case study others cite, not the company that cites others' success. To capture the value of platform emergence rather than paying platform rents.
Five years from now, agentic orchestration will be as fundamental to enterprise operations as cloud computing is today. The only question is whether MAF will be remembered as the company that saw it coming and acted decisively—or the one that watched it happen.
If You Invest Today (Month 6)
Pilots live across multiple MAF business units: CX agents handling thousands of customer interactions daily. Precision Media agents optimizing campaigns continuously. Supply chain agents preventing stockouts and reducing waste.
First public case studies: Joint MAF-Orcest.AI presentations at GITEX. Press coverage in Arabian Business and regional tech media. Industry recognition for innovation leadership.
Agent libraries maturing: Each deployment teaches the system. Performance metrics improving week over week. Measurable ROI deltas documented and expanding.
Category story owned: When regional enterprises think "agentic AI," they think MAF + Orcest.AI. Your brand associated with the cutting edge of operational intelligence.
Six months of progress that would take competitors 18–24 months to replicate without your strategic advantages.
If You Pass (Month 6)
A competitor writes this story. Maybe a regional rival who sees the same opportunity. Maybe a global platform that decides the Middle East is strategic. Maybe Orcest.AI finds another anchor customer—one of your peers who was willing to move first.
You become a reference customer, not an owner. You pay licensing fees instead of collecting returns. You implement someone else's roadmap instead of shaping your own.
The orchestra plays on—without your baton. The music is just as powerful, the outcomes just as transformative. But you're in the audience, not on stage.
There's no shame in being a follower—most companies are. But that's not MAF's DNA. You've built an empire by seeing opportunities before others and executing with excellence. This is another one of those moments.
Cultural Fit
Builders, Not Barkers
We ship code, not press releases. Demonstrate value through working systems, not marketing hype. Substance over style, always.
Transparent, Measurable, Ship-First
Honest about challenges. Rigorous about metrics. Biased toward action. Fix problems by shipping solutions, not by forming committees.
Aligned with MAF's Digital Scale Agenda
Your H1 2025 results show what's possible when technology meets execution excellence. We're the next chapter of that story—AI-driven operations at MAF scale.
The best partnerships aren't just financial transactions—they're alignments of values, ambitions, and operational philosophies. We see in MAF a kindred spirit: ambitious, pragmatic, results-driven, unafraid of the frontier.
Term Sheet Snapshot
Clean, founder-friendly terms focused on alignment and partnership. No exotic preferences or gotchas. Build trust through simplicity.
Close: Conduct with Us
Orcest.AI turns AI into teams that deliver
Majid Al Futtaim becomes the patron of the agentic era in MENA. Your investment doesn't just fund a company—it positions your entire organization at the vanguard of the most significant operational transformation since the internet.
You gain a technology platform that compounds in value with every deployment. You gain a partner committed to your success. You gain the ability to shape an entire category from its inception.
Let's open the season—together. The orchestra is tuned. The score is written. We're ready to perform.
All we need is your baton.