Services

AI & Data

AI where it earns its place.

Data pipelines, reporting and AI features inside the tools you already use, with a person making the decisions that matter.

Book a 15-min call See case studies

What we deliver.

  • 01Production ML or LLM workflowModel deployed behind a versioned API, with prompts, fine-tuning data, or weights all version-controlled.
  • 02Data pipeline infrastructureAirflow or dbt for batch, streaming where it earns its keep. Schema-validated, observable, idempotent.
  • 03A RAG or agent system that worksEmbedding pipeline, retrieval evaluation, system prompts, guardrails. Fall-back behaviour when the model stalls.
  • 04Drift + cost monitoringModel performance, token spend, and data drift dashboards. Alerts when accuracy or budget moves.
  • 05Evaluation harnessGolden test set, scoring rubric, and CI gate so model changes don't silently regress quality.

What we don't do.

  • Ship a demo and call it production — every model lands with monitoring, fallback paths, and an eval suite.
  • Use the largest model when a smaller one fits — we measure, then pick. Cost is a feature.
  • Generate plausible nonsense — RAG without retrieval evaluation is a liability. We measure groundedness.

How a project runs.

  1. System health check

    We map what you run, how it connects and where the risk sits, then hand you a written plan, whether or not you build with us.

  2. A plan you sign off

    Scope, sequence and price agreed in writing before any work starts.

  3. Weekly increments

    Working software every week, shown in a demo, so you see progress rather than status reports.

  4. Handover and support

    Code, documentation and runbooks handed over. We stay on for support if you want us to.

Tools we use.

Backend

Node.js Python Go Java C#

Cloud

AWS GCP Azure Vercel Netlify

Data

PostgreSQL MongoDB Redis BigQuery Snowflake

AI / ML

TensorFlow PyTorch OpenAI Anthropic Gemini NotebookLM ElevenLabs

Recent work.

Common questions.

  • 01OpenAI, Anthropic, or open-source?

    We work across all of them and ship multi-provider via Vercel AI Gateway when latency or cost matters. The model is an implementation detail; the eval harness is what stays.

  • 02How do you handle hallucinations?

    Retrieval-grounded responses with citations, structured-output validation via Zod schemas, and a per-response confidence check. Where stakes are high, we add a human review queue.

  • 03Do you fine-tune or just prompt?

    Both, depending on the workload. Fine-tuning earns its keep when prompt engineering plateaus and you have ≥1K labeled examples. We measure before deciding.

  • 04What about data privacy?

    On-prem deployments via Bedrock, Azure OpenAI, or self-hosted Llama where required. We never send data to a model your contracts don't cover.

Three ways to work with us.

  1. 01

    System health check

    A fixed-fee assessment of what you run, ending in a plan you can act on with us or without us.

    Choose this if you need clarity before committing.

    Book a health check
  2. 02

    Dedicated Team

    2–8 engineers embedded in your workflow on a monthly retainer.

    Choose this if you need ongoing build velocity.

    Embed a Team
  3. 03

    Project-Based

    Fixed scope, fixed timeline, fixed price for a locked-brief MVP.

    Choose this if you have the spec and want a price upfront.

    Define a Project

Not sure? Book a 15-min call — we'll point you to the right door.

Start here

Let's start with a system health check.

Tell us what you run and where it hurts. We'll come back with a scope and a fixed fee for the health check.

Book a system health check Contact