Architecture sprint
2–3 weeks
Resolve the system boundary, technical choices, risks and a buildable delivery plan.
All services
Estma service / AI implementation
AI implementation
Move a useful AI idea from prototype to an owned production system, with the integrations, controls, evaluation and handover it needs to survive real use.
Where this starts
A good fit when…
- A prototype works in demos but not reliably in production
- A team needs to add AI to an existing product or workflow
- Architecture, model or vendor choices are blocking progress
- An agent or RAG system needs real controls around it
The operating problem
The difficult part is rarely the headline technology.
The engagement focuses on the surrounding system: boundaries, evidence, permissions, exceptions, people and the decisions the implementation must support.
- 01
Unclear boundaries between the model, application, data and people
- 02
Prompts and integrations that work only on the happy path
- 03
No representative evaluation set or release threshold
- 04
No monitoring, fallback path or operational owner
What leaves the engagement
Concrete output, not advisory residue.
- 01System architecture and data-flow map
- 02Working application, agent, retrieval or model integration
- 03Evaluation baseline and release checks
- 04Safety controls, exception handling and approval paths
- 05Production instrumentation, runbook and team handover
Ways to start
Choose the smallest engagement that resolves the next decision.
Production build
6–12 weeks
Implement one defined AI workflow through integration, evaluation, release and handover.
Embedded improvement
Monthly
Work alongside the product team on reliability, evaluation and controlled expansion.
What Estma needs from your team.
- A product owner who can make scope decisions
- Access to the current application and relevant data
- People who understand the real workflow
- A route to production or a representative test environment
Inside the work
See the decisions behind the service.
Representative engagements show how this capability connects to the product, workflow, controls, and people around it.
01 / Evaluation and observability
The AI release that stopped relying on gut feel
A product team turns scattered examples and subjective review into a release decision it can repeat.
Read the work note02 / AI application security
The AI layer nobody had security-tested
A release review follows risk across prompts, retrieval, permissions, tools, APIs, and cloud controls.
Read the work noteService questions
What teams usually ask.
Do you build with a specific model provider?
No. The model and vendor should follow the use case, data constraints, operating environment and cost profile. We can work with commercial APIs, open models or a mixed architecture.
Can you improve an existing prototype?
Yes. We first establish what is worth keeping, then make the system observable and measurable before changing the architecture.
Start with context
Is this the work you need?
Describe the current system, the decision ahead and the constraint that is making progress difficult.