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The AI practice

Intelligence, engineered.

We build AI features that survive contact with real users. That means retrieval over your data, evaluation you can trust, and a human in the loop wherever being wrong is expensive.

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Pattern
RAG
Guardrails
Default
Evaluation
Built in
Human review
Where it counts

01The pipeline

Input to outcome.

Every AI system we ship has this shape. The pipeline is what makes an assistant auditable instead of magic.

  1. 01

    Input

    A question, a document, an event — whatever your users and systems actually send.

  2. 02

    Context

    Retrieval over your data, scoped by permissions, so answers are grounded in your sources rather than the model's memory.

  3. 03

    Intelligence

    The model reasons over that context under explicit instructions, with the tools it is allowed to call.

  4. 04

    Action

    It drafts, routes, updates or escalates — with a human in the loop wherever the cost of being wrong is real.

  5. 05

    Outcome

    Every run is logged, cited and evaluated, so quality is measured over time instead of assumed.

What we ship

  • Assistants grounded in your documentation, with citations users can check.
  • Retrieval search that respects existing roles and permissions.
  • Workflow automation for the repetitive middle of a process, not the judgement calls.
  • Evaluation harnesses, so a prompt change cannot silently make things worse.

What we will tell you honestly

  • Language models are wrong sometimes. We design the interface around that, rather than pretending otherwise.
  • If a rules engine or a database query solves it, we will tell you and build that instead — it will be cheaper and more reliable.
  • Retrieval quality is a data problem before it is a model problem. Messy sources produce messy answers.
  • Anything with legal, clinical or financial consequence keeps a human approving the outcome.

02What this looks like

The systems we actually build.

Four shapes cover most of what teams actually need. Anything genuinely novel, we will tell you it is novel.

Assistants grounded in your documentation

Internal and customer-facing assistants that answer from your sources and cite them, so an answer can be checked rather than trusted blindly.

Typical signals

  • RAG
  • Citations
  • Permissions

Retrieval search over messy corpora

Semantic search across documents, tickets and records that respects the access rules your product already enforces.

Typical signals

  • Embeddings
  • Hybrid search
  • Access control

Workflow automation with review gates

Drafting, classification, routing and summarisation for the repetitive middle of a process — with approval steps where judgement matters.

Typical signals

  • Classification
  • Drafting
  • Human review

Evaluation harnesses

Test sets and scoring so a prompt or model change is measured before it ships, instead of discovered by a customer afterwards.

Typical signals

  • Test sets
  • Regression checks
  • Monitoring

03Our position

Where we draw the line.

We will tell you when you do not need AI.

A surprising share of "AI projects" are better served by a database query, a rules engine or a redesigned form. Those are cheaper to build, cheaper to run and far easier to reason about when something goes wrong. If that is the answer, you will hear it in the first conversation.

Retrieval quality is a data problem first.

Model choice is rarely the bottleneck. Inconsistent sources, missing metadata and documents nobody has updated in three years are. We will look at your corpus early, because that is what determines whether the finished thing is useful.

Confidence is designed, not claimed.

Language models produce fluent wrong answers. The interface has to make uncertainty visible — citations, confidence signals, easy escalation to a human — rather than presenting every response with equal authority.

Anything consequential keeps a human in the loop.

Where an error has legal, clinical or financial cost, the system proposes and a person approves. That is a product decision as much as a safety one: it is also what makes the tool adoptable inside a regulated organisation.

Applied AI

Have a problemworth automating?

Bring us the workflow, not the technology. We will tell you honestly whether a model belongs in it.

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Or email us directly — info@techiesofepi.com