AI Engineering & Development

A language model demo takes an afternoon. A feature people rely on takes much more: a way to measure quality, a cost you can predict, a security review, and a safe way to ship changes. Most AI projects that stall do so in that gap. We build the feature and the engineering around it, so it keeps working after launch and your own team can maintain it.

Measured, not guessed

Every feature has a test set built from your real cases, and a score it must reach before each release.

Cost known up front

We estimate and track the cost of each request, and pick the smallest model that does the job well.

Your team can run it

Standard tools and clear documentation, with your engineers working alongside ours from the first sprint.

Language-model features

Drafting, summarising, extracting, classifying, and answering, built into your product or internal tools.

Search over your own data

Retrieval-based answers with citations, where people only see documents they are already allowed to open.

Custom and fine-tuned models

Where a general model falls short, we train or fine-tune a smaller one and show whether it beats the baseline.

Evaluation harness

Test sets and automated scoring that run on every change, so quality is checked rather than assumed.

Deployment pipeline

Versioned prompts and models, automated builds, staged releases, and rollbacks that take one step.

Monitoring and cost control

Dashboards for quality, speed, drift, and spend, with alerts when any of them moves out of range.

• Which model should we use?

  How we decide

  We test two or three candidates on your real cases and compare quality, speed, and cost per request.

  What you get

  A model choice backed by scores, with a fallback.

• Retrieval, fine-tuning, or both?

  How we decide

  We start with the simplest option and move on only if the evaluation shows it falls short.

  What you get

  A design matched to the problem rather than to a trend.

• Hosted service or your own infrastructure?

  How we decide

  We weigh data sensitivity, volume, speed, and running cost at the usage you expect.

  What you get

  A hosting decision with a cost projection.

• How will we know it works?

  How we decide

  A test set built from your real cases, with scoring rules agreed with the people who do the work.

  What you get

  A release threshold anyone on your team can read.

• How do we ship changes safely?

  How we decide

  Versioned prompts and models, staged rollouts, and a tested way back to the last good version.

  What you get

  A pipeline that catches a bad change before release, or reverses it quickly.

1. Phase 1

   1 to 2 weeks

   Define

   • Agree the use case, the success measures, and what the feature must never do
   • Collect real examples and build the first version of the test set
   • Review data access, security, and hosting constraints

   You receive

   Technical design, test set, and release thresholds.

2. Phase 2

   2 weeks

   Prototype

   • Build the thinnest working version using your real data
   • Compare candidate models and designs on the test set
   • Estimate the running cost of each request

   You receive

   Working prototype and a scored comparison of the options.

3. Phase 3

   3 to 4 weeks

   Build

   • Build the production service and connect it to your systems
   • Set up the pipeline, with evaluations running on every change
   • Add monitoring, then run a security review and load tests

   You receive

   Production-ready service with its pipeline and dashboards.

4. Phase 4

   1 to 2 weeks

   Launch and hand over

   • Release to a small group first, then widen access
   • Write runbooks and train the engineers who will own the feature
   • Review results against the agreed thresholds

   You receive

   Live feature, runbooks, and a handover session.

How we protect your data

Your data stays yours

Your data is not used to train third-party models, and we use providers whose terms say the same.

Access follows your permissions

Search respects the permissions you already have, so people only see what they could open themselves.

Tested before release

Quality, safety, and cost checks run on every change, and a failed check blocks the release.

Environments kept apart

Development, staging, and production are separate, and credentials sit in your own secrets vault.

You own the work

The code, prompts, test sets, and any trained model weights are yours from the first commit.

Tools we work with

Models

OpenAI, Anthropic, Google Gemini, Llama, Mistral, plus small fine-tuned models where they win

Application

TypeScript and Python, AI SDK, FastAPI, Next.js

Retrieval

Postgres with pgvector, Azure AI Search, OpenSearch, Pinecone

Training and MLOps

PyTorch, MLflow, Weights & Biases, SageMaker, Azure ML, Vertex AI

Evaluation and monitoring

Langfuse, custom test harnesses, OpenTelemetry, Grafana

Delivery

GitHub Actions, Terraform, Docker, Kubernetes

We build on your cloud and your existing tools wherever we can.