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AI Engineering

AI Engineering & Integration

We design, build and integrate AI features into your products and internal tools. That means grounding models in your documents and databases, measuring answer quality, and shipping something your team can trust in daily work — not a demo that breaks on real questions.

Discuss this serviceBook a discovery call

Sound familiar?

The problems we solve.

  • Teams lose hours searching across drives, PDFs and chat threads for answers that already exist.
  • Generic AI chatbots give confident but wrong answers about your own products and policies.
  • AI pilots stall because nobody can tell whether the output is good enough to ship.

What we deliver

Capabilities

  • 01

    Retrieval-augmented generation (RAG)

    Assistants that answer from your knowledge base and cite the exact source, so people can verify before acting.

  • 02

    Document intelligence

    Extract structured data from invoices, contracts, forms and reports, with human review where accuracy matters.

  • 03

    AI inside your product

    Summaries, smart search, drafting and classification added to your existing app through clean, tested APIs.

  • 04

    Evaluation & guardrails

    Test sets, quality metrics and safety checks so you can measure improvements and catch regressions before users do.

Example use cases

  • Internal knowledge assistant for policies, SOPs and product documentation
  • Semantic search across a product catalogue or property listings
  • Automated extraction of line items from supplier invoices
  • Draft replies for sales and support teams, reviewed by a human

You receive

  • Working AI feature integrated with your systems
  • Evaluation suite and quality baseline
  • Cost and latency estimates per request
  • Documentation and handover to your team

Typical technology

  • OpenAI / Anthropic / open models
  • LangChain or custom pipelines
  • pgvector / Pinecone
  • Python
  • TypeScript
  • Next.js

We choose tools for your context — existing systems, team skills and budget — not by habit.

FAQ

Questions we often hear.

  • Will our data be used to train public models?

    No. We use API tiers and deployment options where your data is not used for training, and we can host open models in your own cloud when that's required.

  • How do you deal with wrong answers?

    We ground answers in your sources, show citations, set confidence thresholds, and route uncertain cases to a person. We also measure accuracy against a test set before launch.

  • Do we need clean data before starting?

    Not necessarily. Part of discovery is assessing what you have. Often a focused first use case can work with existing documents while the rest is improved over time.

Related

Often combined with

Let's talk about AI Engineering.

Tell us what you're working on. We'll tell you honestly whether and how we can help.

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