Data Room / Risk Factors

Risk Factors

7 min read Investment

1. Pre-Revenue Risk

The risk. Brunelly launched in February 2026 and has not yet generated product revenue. The business is pre-revenue at the time of this raise.

Mitigation. While there is no revenue today, the early signals are unusually strong for a freshly launched seed-stage product:

  • Publicis Sapient, a $5B+ consultancy, has independently validated Brunelly against internal efforts, EPAM, and Google and found nothing comparable. They are actively preparing to present Brunelly to Deutsche Bank.
  • ~200 sign-ups in the first week with zero marketing spend.
  • 20+ serious leads from STEP conference requesting demos and follow-ups.
  • The founding team has a proven track record of enterprise revenue generation - Pina Vida grew to 7-figure consultancy revenue under the same leadership.

Enterprise sales cycles are inherently long (6--12 months), but the pipeline is real and the validation is independent, not self-reported.

2. Market Risk: AI Dev Tools is a Crowded Space

The risk. GitHub Copilot has $2B+ ARR. Cursor is valued at $29B. Devin at $10B. Dozens of well-funded AI coding tools are competing for developer attention and enterprise budgets.

Mitigation. Every one of these tools is a point solution focused on code generation - roughly 30% of the software development lifecycle. None covers planning, estimation, architecture review, testing, deployment, or monitoring with AI native throughout. Brunelly addresses the full lifecycle. This is not a marginal differentiation; it is a fundamentally different product category.

This positioning has been independently validated. When Publicis Sapient evaluated options for Deutsche Bank, their conclusion was unambiguous:

"We've looked at this internally, spoken to EPAM and Google and nothing matches this."

The fragmented tools market ($3,000--$7,000 per developer per year) is ripe for consolidation. Brunelly is not competing with Copilot for code completions - it is replacing the entire fragmented stack.

3. Execution Risk: Small Team

The risk. The core technical capability is concentrated in one founder. The team is lean by any standard.

Mitigation. The small team is simultaneously a risk and a strength:

  • Capital efficiency is exceptional - current burn is $80K/year, and the $1.5M raise provides 3+ years of runway.
  • Less dilution for early investors compared to companies burning $200K+/month pre-product.
  • The Sri Lanka talent pool is deep and proven. The existing team members are already operational, and senior developers can be hired at approximately $500/month fully loaded.
  • Team expansion is a primary use of proceeds. A product owner and additional developers are the first hires planned.
  • The CEO manages all non-technical operations independently, meaning the CTO is focused entirely on product and architecture.

The lean team got the product to launch. The raise is specifically designed to expand it.

4. Platform Risk: LLM Dependency

The risk. Brunelly's AI capabilities rely on third-party large language models from providers such as OpenAI and Anthropic. Changes in pricing, availability, or terms of service could impact the product.

Mitigation. This risk is structurally mitigated by the Maitento AI OS architecture:

  • Provider abstraction. Maitento abstracts all LLM interactions behind a unified interface. Brunelly is not locked to any single provider and can switch or blend providers without application-level changes.
  • Proprietary orchestration. The Cogniscript VM and The Loom orchestration layer are wholly proprietary. These are the components that deliver Brunelly's differentiated capabilities - the underlying LLM is a commodity input.
  • Own infrastructure. Hybrid cloud architecture (5 HP servers, 200 CPU cores, 1.28TB RAM) reduces dependency on cloud-only API access and provides cost control.
  • Multi-model support. The architecture is designed to use the best model for each task, not a single model for everything. This creates natural resilience against any one provider's changes.

5. Enterprise Sales Cycle Risk

The risk. Enterprise deals are slow. Typical sales cycles run 6--18 months. Pilots do not always convert to paid contracts. Revenue timing is inherently unpredictable.

Mitigation.

  • The 60% pilot-to-paid conversion target (5 pilots, 3 converting) is a conservative industry benchmark, not an optimistic projection.
  • A dual revenue stream model means 30% of targeted revenue comes from self-serve customers, providing income while enterprise deals mature.
  • Founder-led sales with deep technical credibility accelerates trust-building with engineering stakeholders - the actual decision influencers in AI tooling purchases.
  • The Publicis Sapient channel is a significant accelerant. Systems integrators selling to their existing clients dramatically shortens the cycle compared to cold outbound.
  • The team has direct enterprise sales experience from Pina Vida, including multi-year contract renewals.

6. Geographic Risk: UAE Focus

The risk. The UAE startup ecosystem is less mature than the US or UK. The local market is smaller. Some investors may view a UAE-incorporated entity as unconventional.

Mitigation. The UAE strategy is deliberate, not a default:

  • "Big fish, small pond" advantage. Less competition for investor attention, government support, and enterprise relationships compared to the saturated US/UK ecosystems.
  • Enterprise clients are global. Deutsche Bank, De Beers Group, and Publicis Sapient are not UAE companies. Enterprise customers care about product capability and security, not where the company is incorporated.
  • Tax structure preserves capital. 9% corporate tax - significantly lower than most Western jurisdictions - means more of the raise goes to building the business.
  • DIFC/ADGM legal frameworks are well-understood by international investors and provide robust governance structures.
  • Growing regional tech investment. UAE government-backed technology initiatives are increasing the depth of the regional investor and talent pool.
  • YC optionality. If accepted to Y Combinator, the company would parent in the US, providing a dual-structure option.

7. IP Transfer Risk

The risk. Brunelly's intellectual property (the Brunelly platform and Maitento AI OS) currently resides in Pina Vida, the predecessor consultancy. This IP needs to transfer to the new Brunelly entity.

Mitigation.

  • The legal process for IP transfer is underway and will be completed before investment closes.
  • A temporary rights transfer contract will be executed immediately to ensure Brunelly has full operational rights during the transition period.
  • There are no third-party claims on the IP. The technology was built entirely in-house by the founding team.
  • Pina Vida is being wound down. The IP transfer is a clean separation, not a carve-out from an ongoing business.
  • Legal confirmation of full ownership transfer will be a condition precedent to investment closing.

8. Competitive Response Risk

The risk. Large incumbents - Microsoft, Atlassian, or a well-funded startup - could decide to build a full-SDLC AI platform that competes directly with Brunelly.

Mitigation.

  • Large companies move slowly in AI. Microsoft's Copilot took years from research to general availability. Atlassian spent $2.6B on acquisitions in 2025 trying to bolt AI onto 20-year-old tools. Retrofitting AI across a full lifecycle product requires fundamental architectural decisions that cannot be added incrementally.
  • Brunelly is AI-native from the ground up. Maitento was purpose-built as an AI operating system. Competitors would need to rebuild from scratch to match this architecture - they cannot patch their way there.
  • 18--24 month head start. By the time a credible competitive response materialises, Brunelly will have enterprise customers, proven workflows, and accumulated domain-specific training data.
  • The moat deepens with usage. Enterprise customers with integrated workflows, custom configurations, and team-specific AI training are unlikely to rip and replace for a 1.0 competitor product.

9. Key Person Risk

The risk. The technical architecture is heavily dependent on founder Guy Powell. An extended absence would significantly impact product development.

Mitigation.

  • The codebase is well-structured with clear architecture patterns, comprehensive documentation, and consistent conventions. It is not a one-person knowledge silo - it is engineered to be maintainable by a team.
  • The CEO operates the business independently. Commercial operations, customer relationships, and GTM execution do not depend on the CTO.
  • Hiring a product owner and expanding the development team are the top priorities from this raise, specifically to distribute technical knowledge and capability.
  • The Maitento AI OS and Brunelly platform follow established architectural patterns (Angular SPA, ASP.NET WebApi, RabbitMQ message bus, MongoDB) - the technology choices are industry-standard, not esoteric.

10. Regulatory Risk

The risk. AI regulation is increasing globally. The EU AI Act, emerging US frameworks, and sector-specific rules (financial services, healthcare) could impose requirements on AI-assisted software development tools.

Mitigation. Brunelly's architecture is naturally aligned with the direction of AI regulation:

  • Human-in-the-loop by design. Every stage of Brunelly's workflow includes human review and approval. This is not a bolt-on compliance feature - it is core to the product philosophy.
  • Enterprise-grade audit trails. Full traceability of AI-assisted decisions is built in, supporting compliance requirements in regulated industries.
  • "The opposite of vibe coding." Brunelly's structured, quality-focused approach positions it well for environments where AI governance is required. Regulated enterprises (Deutsche Bank is the proof point) need exactly this approach.
  • Regulatory tailwind, not headwind. As AI regulation tightens, unstructured AI coding tools face restrictions. Brunelly's controlled, auditable approach becomes more valuable, not less.

Summary

The risks outlined above are typical of a well-prepared seed-stage company. Brunelly is pre-revenue, has a small team, and faces the inherent uncertainties of enterprise sales cycles and a fast-moving competitive landscape. None of these are unusual for the stage.

What is unusual is the quality of early validation (independent confirmation from a $5B+ consultancy), the capital efficiency of the operation ($80K/year burn with 3+ years of runway from this raise), and the depth of the technical moat (2+ years of AI-native architecture that cannot be replicated quickly). The founding team has identified each of these risks, built mitigations into the business plan, and has direct experience navigating enterprise sales, team scaling, and technology delivery.

The risk profile is honest. The preparation is thorough. The opportunity is genuine.