What we build

AI scoped to one job it can do reliably.

Models trained on your data and your decision logic, from an assistant that answers to automation that acts. Each one given a single task it performs with confidence, and measured against it.

The failure mode of enterprise AI is a system that is impressive in a demo and untrusted in production, because nobody defined what a correct answer looks like before it shipped.

We work the other way round. The task is narrowed until it can be evaluated, the evaluation set is agreed with the people who will rely on it, and the system goes live on evidence rather than enthusiasm.

What this covers

01

Assistants grounded in your data

Answers drawn from your documents and records, with the source shown, not from general model knowledge.

02

Decision automation

Rules and judgement encoded where the volume justifies it, with a human in the loop wherever the cost of being wrong is high.

03

Agent workflows

Multi-step processes where the system gathers, checks and produces, and reports what it did.

04

Evaluation and guardrails

A measured accuracy target per task, refusal behaviour when the system is out of its depth, and logging that lets you audit any answer after the fact.

05

Cost and usage control

Per-feature and per-user metering with budget ceilings, so the running cost is visible before the invoice arrives.

06

On-premise where required

The same systems can run entirely inside your network when data cannot leave it.

Where we have done it

TheMorph

A parallel-agent architecture in production: a client’s own data turned into publish-ready, on-brand content at volume.

Miter

One product URL in, a complete direct-response strategy and campaign assets out, with research pulled live rather than recalled from training data.

The AI inside our GRC products.

RisQore and SentiQore both carry grounded assistants that answer only from the active client’s data and propose changes for human confirmation.

How an engagement runs

  1. 01

    Pick a task worth automating

    Volume, cost of error and available data decide it. Some candidates get rejected here, which is the point.

  2. 02

    Define correct

    A test set and an accuracy target agreed with the team that will depend on the output.

  3. 03

    Build and measure

    Retrieval, prompting, tooling and guardrails, evaluated against the target rather than a demo.

  4. 04

    Ship with a kill switch

    Live behind monitoring and metering, with a documented way to turn it off and fall back.

Start with a conversation.

Tell us what you are running today and what has to change. A founder responds directly.

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