We design the most suitable architectures for your company.

Core service

Turn your AI demo into a production system

An LLM demo works within days. Turning it into a system with measured accuracy, predictable cost and clear security boundaries is a separate piece of engineering.

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Duration
4 to 12 weeks
Model
Design and production rollout
Output
Working system and eval pipeline

Signs of an AI project that cannot reach production

What these problems have in common is not the model itself, but the missing system around it.

  • The demo is impressive, but it answers the same question differently every time.
  • There is no way to measure whether answers are correct; quality is judged by feel.
  • Token costs grow unpredictably as usage rises.
  • The model has access to company data, but what it can reach is not restricted.
  • Answers without sources erode user trust.
  • Nobody knows how much of the system would need rewriting if the model provider changed.

You design the system, not the model

An AI feature in production is made of layers: data access, context building, the model call, output validation and monitoring. The model is only one link in that chain, and often the easiest one to replace.

Design starts with an evaluation pipeline. Without a test set that measures whether the system works correctly, every improvement is a guess. RAG, agent flows and tool use are built on top of that measurement.

Security boundaries are drawn from the start. Which data the model can reach, which actions it can take and which outputs need human approval are part of the architecture, not a filter bolted on later.

Scope

Included

  • Prioritising use cases by business value and feasibility
  • RAG architecture: document processing, chunking, embedding and retrieval
  • Agent flows and tool use (LangGraph, MCP)
  • Evaluation pipeline and test sets
  • Cost model and caching strategy
  • Access control and data boundaries
  • Monitoring, logging and feedback loop
  • A layer that keeps you independent of any single model provider

Not included

  • Training a model from scratch
  • Data labelling services
  • Legal compliance opinion
  • End-user interface design

How the project runs

  1. Weeks 1-2Use case and data. The use case, success criteria and data sources are pinned down. The first evaluation set is prepared.
  2. Weeks 3-5Architecture and first measurement. Retrieval, context and model layers are built. The system is measured against the evaluation set and a baseline is recorded.
  3. Weeks 6-9Improvement. Retrieval, prompts and flows are improved based on the measurements. The effect of every change is verified on the same set.
  4. Weeks 10-12Production rollout. Monitoring, cost tracking, access controls and the feedback loop are put in place. The system is handed over to your team.

What you receive

Code, prompts, evaluation sets and infrastructure definitions are all yours.

Working system
A RAG or agent flow running in production, with its tests.
Evaluation pipeline
A test set that measures accuracy automatically and runs on every change.
Cost model
A projection of token and infrastructure costs for your use case.
Security boundaries
Data access rules, action permissions and the points that need human approval.
Monitoring dashboards
Views for answer quality, latency, cost and error rate.
Provider independence
An abstraction layer that lowers the cost of switching model providers.

Who this service is not for

  • Organisations that want to add AI because competitors have, rather than because of a product need.
  • Teams that want to start without defining success criteria. A system that is not measured cannot be improved.
  • Projects that want to train their own model from scratch. This service focuses on integrating existing models into your systems.

Frequently asked questions

Which models do you work with?

We are not tied to any provider. We design systems that work with commercial APIs and open-source models; the choice depends on accuracy, cost and data residency requirements.

Is our data sent to the model provider?

That depends on the architecture, and you decide. For sensitive data we can design models that run on your own infrastructure, or data masking layers.

RAG or fine-tuning?

In most enterprise scenarios RAG is the better starting point: data stays current, sources can be cited and costs stay low. Fine-tuning is considered when a specific format or behaviour is required.

How do you measure accuracy?

With an evaluation set built from real use cases. Every change is tested against that set, so improvement is shown in numbers, not by feel.

Who owns the code and prompts?

You do. Code, prompts, evaluation sets and infrastructure definitions are your property from day one.

Let’s assess your use case together

In thirty minutes you describe your use case; we tell you whether it can reach production and what to measure first.

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