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AI & Automation

AI features that are measured, not demonstrated

An AI feature is only finished when you can tell whether it is working. We build the evaluation harness alongside the feature, so quality is a number on a dashboard rather than a hunch after a demo.

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What this covers

Capabilities

AI agents

Tool-using agents scoped to a defined task, with guardrails, retries and a human approval step where the action is irreversible.

Workflow automation

Document intake, classification, routing and data entry replaced with reviewed automated steps.

LLM integration

Model selection, prompt and context design, structured output and cost control.

Retrieval and intelligent search

Chunking, embeddings, hybrid retrieval and reranking over your own content.

Evaluation

Golden datasets, regression suites and offline scoring wired into CI so a prompt change cannot quietly degrade quality.

Generative features

Drafting, summarisation and content generation with review workflows and provenance.

What you get

Why teams bring us in

  • Evaluated before launchWe agree on an accuracy bar during discovery and show the measurements against it.
  • Costs you can predictToken accounting, caching and model routing so unit economics work at volume.
  • A person stays in the loopAutomation proposes; for consequential actions, a human confirms.

Typical stack

What we usually reach for on this kind of work. The final choice follows the project, not the list.

  • OpenAI
  • Anthropic
  • Python
  • FastAPI
  • PostgreSQL
  • Node.js

How we work

A clear path fromidea to launch

Six stages, each with something concrete handed over at the end of it. You always know what has been done and what comes next.

  1. 01

    Discover

    Research, requirements, business goals and technical discovery.

    • Requirements brief
    • Technical assessment
    • Scope and estimate
  2. 02

    Design

    User flows, wireframes, prototypes and product experience.

    • User flows
    • Clickable prototype
    • Design system
  3. 03

    Develop

    Scalable frontend, backend, APIs and infrastructure.

    • Working increments
    • API documentation
    • Code review record
  4. 04

    Test

    Quality assurance, automation and performance testing.

    • Automated suite
    • Test report
    • Accessibility audit
  5. 05

    Deploy

    Cloud deployment, observability and release.

    • Infrastructure as code
    • CI/CD pipeline
    • Runbooks
  6. 06

    Scale

    Monitoring, optimization and continuous improvement.

    • Service level objectives
    • Cost report
    • Roadmap review

Questions

Frequently asked

  • Mainly the Anthropic, OpenAI and Google hosted models, plus open-weight models where data residency or cost makes that the better answer. We keep the provider behind an interface so switching is a configuration change rather than a rewrite.

  • We use enterprise API tiers that exclude your data from model training, and we can keep retrieval and embeddings inside your own infrastructure. Data handling is agreed in writing before the first request is made.

  • By constraining what it can answer from. Responses are grounded in retrieved source material with citations, outputs are validated against a schema, and anything below the confidence threshold is routed to a person instead of guessed.

  • Usually, and the first step is measuring the manual process — volume, handling time and current error rate. Without that baseline there is no way to show whether the automation helped.

Let’s build something exceptional.

Tell us what you’re building. We’ll help turn it into a reliable, scalable digital product — and tell you honestly if we’re not the right team for it.

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