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Services

Four capabilities. One standard.

We organize by capability, not by industry. Specificity comes from the work — the real problem, its constraints and its data — not from a per-sector template.

01

AI engineering

We take AI from pilot to production, with ROI metrics rather than a demo.

Problem
88% of companies use AI, yet most pilots never reach production or measure their impact.
Solution
Copilots, assistants and agents embedded in the real workflow, built on data→model pipelines that hold the result over time.
How
Rigorous evaluation and observability from day one: every model is measured, monitored live, and iterated against a quantified target.
Result
AI in production, integrated into the business, with an ROI metric defined before the first line is written.
  • Copilots and assistants embedded in the workflow, not in a separate tab.
  • RAG over your own sources, with citations and hallucination control.
  • Agents that execute scoped tasks with guardrails and traceability.
  • Reproducible data→model pipelines — versioned, observable and deployed.
  • Evaluation and observability: quality, cost and latency metrics, live.
Stack
  • Python
  • LLMs
  • RAG
  • MLOps
  • AWS
02

Custom product

End-to-end digital product, built under your real constraints.

Problem
Generic tools force the business to bend to their shape; custom product built without rigor ages just as fast.
Solution
Discovery, architecture and build of web, mobile and APIs by an elite team that owns the problem from start to finish.
How
We iterate against real usage, with frequent releases and documented architecture decisions; end-to-end ownership, no handoffs.
Result
A product that fits your operation exactly — maintainable and ready to grow, not a prototype to be rewritten.
  • Discovery that scopes the work down to the problem that actually moves the needle.
  • Architecture designed for real constraints: scale, budget and team.
  • Web and mobile with a focus on performance, accessibility and detail.
  • APIs and services designed to integrate, not to lock you in.
  • End-to-end ownership: from first sketch to production and operation.
Stack
  • TypeScript
  • React
  • Node
  • Astro
  • PostgreSQL
03

Modernization

We rewrite legacy systems without stopping the business.

Problem
The legacy system keeps the operation running but blocks every change; replacing it in one move is a risk few can take.
Solution
Incremental strangulation: we wrap the old system and migrate piece by piece, keeping it in production the whole time.
How
Each milestone retires a concrete risk and is reversible; we measure coverage, performance and debt at every step.
Result
A modern, maintainable base — reached without a single outage window, with risk falling milestone by milestone.
  • Dependency map and layered migration plan, not a big-bang rewrite.
  • Strangler pattern: the new runs alongside the old and gradually replaces it.
  • Refactoring backed by tests that lock in behavior before it is touched.
  • Cloud migration and CI/CD to ship safely and often.
  • Observability to prove, at each milestone, that risk has gone down.
Stack
  • Refactor
  • Cloud
  • CI/CD
  • Observability
04

Data & platforms

The foundations on which AI stops being a pilot and becomes infrastructure.

Problem
Without reliable data foundations, every AI initiative is rebuilt from scratch and none becomes infrastructure.
Solution
Data platforms, pipelines and governance that turn scattered data into a single, reliable, queryable base.
How
We design for lineage, quality and cost: every data point has an origin, an owner and a contract, and every pipeline is observable.
Result
A data foundation on which product and AI are built once and reused, instead of starting over each time.
  • Data warehouse and modeling designed for analytics and to feed models.
  • ETL pipelines and streaming ingestion — versioned and monitored.
  • Governance, lineage and data quality as part of the design, not an add-on.
  • Internal platforms that give teams self-service without losing control.
  • An AI-ready base: reliable data that makes it infrastructure, not a pilot.
Stack
  • Data Warehouse
  • ETL
  • Streaming
  • Governance

How we begin

We don’t publish prices. Every engagement is sized against your problem, your context and the result that needs to move — not against a catalog rate.

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The next step

Have a problem that demands the best engineers?

We review every application personally. We don’t work with everyone — and that’s precisely the point.

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