AI integration that fits your product

Practical LLM and ML features wired into real workflows — with evaluation, cost controls, and a path your engineers can maintain.

  • Proof-of-concepts that never leave a notebook
  • Unclear data readiness for model or RAG features
  • No evaluation plan, so quality regressions go unnoticed
Discuss an AI use case
Abstract visualisation representing AI and machine learning work

What we deliver

Product and engineering teams are under pressure to add AI. The risk is shipping a demo that cannot be evaluated, governed, or costed. We integrate AI where it changes a specific workflow.

LLM features in product
Assistants, summarisation, extraction, and generation features behind your existing UX.
  • Use-case scoping with success criteria
  • Prompt and tool design with guardrails
  • API integration into your application
Retrieval & knowledge features
RAG-style search over your documents or product data when that is the right pattern.
  • Document ingestion and chunking approach
  • Retrieval pipeline with basic relevance checks
  • Citation or source display where useful
Classical ML where it fits
Prediction and classification models when tabular or structured ML is the better tool than an LLM.
  • Feature and label definition with your domain experts
  • Training and evaluation pipeline
  • Deployment path with monitoring hooks
Evaluation & operations
Lightweight eval sets, cost tracking, and failure modes before you scale usage.
  • Golden-set examples for regression checks
  • Token / cost visibility for production calls
  • Runbooks for common failure modes

How we work

  1. 1

    Qualify the use case

    Confirm the workflow, data access, and whether AI is the right lever at all.

  2. 2

    Prototype against real data

    Build a thin vertical slice with measurable quality, not a slide demo.

  3. 3

    Integrate

    Wire into your product with auth, logging, and cost controls.

  4. 4

    Evaluate & hand over

    Leave an eval set, docs, and ownership so your team can iterate safely.

Technologies we work with

Stacks and platforms we use on this kind of work — not a partnership claim.

Models & platforms

  • OpenAIOpenAI
  • Hugging FaceHugging Face

Frameworks

  • PythonPython
  • TensorFlowTensorFlow
  • PyTorchPyTorch

Cloud

  • AzureAzure

Patterns

  • RAG
Diagram of an AI feature flow from user request through application, model or retrieval layer, and evaluation

Common questions

Do you build custom models from scratch?

Only when the use case and data justify it. Many product features are better served by carefully integrated foundation models with evaluation and guardrails.

What about data privacy?

We design to your data-handling requirements — including options that keep prompts and documents within your cloud tenancy where the provider supports it.

How do we know the feature is good enough to ship?

We agree success criteria and a small evaluation set up front. Shipping happens against those checks, not against a vague sense that the demo looked good.

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