AI strategy and implementation
for PE-backed companies
I partner with PE firms and their portfolio companies to surface profit-driving AI initiatives and ship them to production.
Problems I solve
- Initiative sprawl with no business caseScattered use cases with unclear ROI. You need the few that drive most of the value.
- Stuck at 80%, never shippedDemos are easy. Production AI is not. Most projects die in the last 20%.
- No systematic process to measure and improveEvery prompt or retrieval tweak is a coin flip. You cannot tell improvement from regression.
- An AI that explains but can't actIt answers questions but can't update the CRM, open the ticket, or trigger the workflow.
Services
- 01AI strategyIdentify productivity gaps and prioritise high-ROI use cases on an impact-effort matrix.
- 02AI implementationArchitect and deploy production AI systems that hold up under real-world scenarios.
- 03AI continuityPeriodic reviews after handover catch silent regressions and keep the system performing.
Background
- Bright Pixel Capital
- Lead AI Engineer, 4 years. €600M B2B infrastructure software fund. Shipped production systems for core fund workflows; ran technical assessments on 100+ companies.
- Deloitte Digital
- Tech Consultant, 2 years. Led digital transformation programmes for major commercial banks.
How I work
A typical engagement lasts 3–6 months, with the first six weeks structured as a rollover pilot. By the end of week six you have a working prototype, a quantified business case, and a defined scope for the rest of the engagement. At that point, you can walk away or roll into the full engagement.
- Forward-deployedI work as an embedded operator inside the team for the project duration.
- Evaluation-centricEvery system I ship has a measurement layer before it has a UI.
- Built for handoverYour in-house team can operate and extend the system independently when I leave.
Convictions
A set of principles learned the hard way from building production systems. If you don't relate, we're probably not a fit.
- 01Process over toolsMost production AI value lives in the system around the model, not in the model itself.
- 02Prompt and pipeline before fine-tuningMost failures aren't a model problem, they're a specification problem.
- 03Systematic evaluation at the coreDevelopment and evaluation are one engagement, not two.
- 04Deterministic workflows over fully agentic systemsAgents are useful at specific points in a pipeline, not as a general solution.
- 05Augment the decision, don't automate itThe highest-value AI systems reduce cognitive load around a decision, not the decision itself.
Who I work with
Right fit
- PE-backed portfolio companies with operational drag in a specific domain.
- VC-backed scale-ups from Series B onwards, gated by operational throughput.
- Mid-market ops-heavy operators with meaningful revenue and margin.
Not the right fit
- Companies building their own foundation models or novel architectures.
- Teams looking for a fully autonomous agent as a magic bullet.
- Companies without the revenue, margin, or organisational readiness.
FAQ
How long does an engagement take?
3–6 months, with a six-week initial sprint and a cancel window at week six. By the end of the sprint you have a working prototype, a quantified business case, and a defined scope for the rest of the engagement.
How do we collaborate?
Weekly working sessions to review what shipped and what's next, shared visibility on the roadmap, and direct communication between sessions when something needs a fast decision.
What's the pricing model?
Two stages: a fixed-fee initial sprint that produces the business case, then outcome-based pricing on the remaining engagement anchored to that business case.
What are the deliverables?
A working system in production, the documentation, and the deployment instructions to run it. Handover is structurally built into the engagement.
How do you handle privacy and data?
Default to open-source, self-hostable infrastructure that can run in the client's cloud or on-prem. Client data is never used to train any third-party model.
What is the tech stack?
Python-first, containerised, built on open-source frameworks for transparency. Lock-in matters most in the orchestration, retrieval, and data layers, and that's where I push back hardest.
Which models do you use?
Frontier-model APIs from Anthropic, OpenAI, and Google by default. Open-weights models when self-hosting is in scope.
How is the system deployed?
Client's cloud or on-prem, depending on infrastructure and compliance constraints. The client owns the stack and can change providers as needed.
What if we need to extend the system?
Most engagements include a continuity layer for exactly this. Periodic reviews catch silent regressions, recalibrate as data drifts, and address new requirements.
If you have a metric you want to move, get in touch. The first call is a working conversation about what you're trying to achieve and what's blocking it.
Book a callAbout
Most AI consultants are either engineers who can't translate the work into business value, or business people who can't ship code. I do both.
I'm Rui Sá, based in Lisbon. Find me on LinkedIn.