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AI and machine learning

Assistants, semantic search and forecasting built into products people already use.

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What this service covers

We add AI features where they save users real time: assistants that answer from your own documentation, semantic search across large content libraries, document extraction, and demand or churn forecasting built on your data.

We start with an evaluation set: real examples of the inputs and the answers you would accept. Every prompt, model and retrieval change is scored against it, so quality is measured rather than guessed.

We are model-agnostic and design for cost from the start, with caching, smaller models for simple steps and usage limits, so the feature stays affordable at scale.

What you get out of it

The results clients typically see. We agree the measures that matter to you during discovery.

  • 01

    A working feature in 6–8 weeks

    Scoped around one high-value use case and shipped to real users, not left as a demo.

  • 02

    Quality you can measure

    An evaluation suite that scores accuracy on your data and runs on every change.

  • 03

    Predictable running costs

    Per-request cost tracked and capped, typically cents per interaction.

  • 04

    Private data stays private

    Data handling, retention and access reviewed and documented for your security team.

How the engagement runs

A typical AI and machine learning engagement, start to finish. You see working results every week.

Stack we use most often

  • Python
  • OpenAI
  • Anthropic
  • LangChain
  • pgvector
  • Pinecone
  • scikit-learn
  • AWS Bedrock
  • Azure OpenAI

We fit in with what your team already runs.

  1. 1

    Use-case assessment

    1 week

    Pick the use case with the best value, build the evaluation set.

  2. 2

    Prototype

    2 weeks

    Test approaches against the evaluation set and pick the best.

  3. 3

    Production build

    3–5 weeks

    Integrate into your product with monitoring and guardrails.

  4. 4

    Measure and tune

    Ongoing

    Track quality and cost in production, and improve from real usage.

Deliverables

What you receive, all in your own accounts.

  • Use-case assessment with expected value and cost
  • Retrieval pipeline over your documents or data
  • LLM-powered feature integrated into your product
  • Evaluation dataset and automated scoring
  • Forecasting or classification models where relevant
  • Cost, latency and quality monitoring
  • Data privacy and security write-up

FAQ

Common questions about AI and machine learning

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Talk to an engineer about AI and machine learning

A 30-minute call with a senior engineer. You leave with a rough plan, a timeline and a price range, whether or not we work together.

Book a callSee our work