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Founder-led. Collective-powered.
Senior accountability
Problem-shaped teams
Digital, data and AI products
From focused product to portfolio
Boutique technology consultancy
Founder-led. Collective-powered.
Senior accountability
Problem-shaped teams
Digital, data and AI products
From focused product to portfolio
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AI & Automation

Intelligence in the workflow

Intelligent workflows and assistants grounded in your data and processes — not a demo that dies in a notebook.

Understand → Shape → Build → Evolve

The same method, applied to this practice

Understand, shape, build and evolve — the studio method applied to this domain. Select a stage to see the questions we settle and the artefacts we leave behind.

01

Find the work worth making intelligent

Map the workflow, the repeated judgement and the volume where an assistant or agent would actually save time. We separate what should be automated, what should be assisted, and what must stay with a person. The first output is a baseline of today's work, with a stop rule for anything that must not go live.

Workflow baseline

How the work moves today, where time is spent, and where the same judgement is applied hundreds of times.

Automation boundaries

What an assistant can draft, what an agent can do, and what a named reviewer must still decide.

Risk and volume

Where errors hurt, how often the judgement repeats, and whether the case is large enough to justify a production loop.

A representative pattern

What a focused engagement can look like

Illustrative delivery pattern, not a client case study or measured outcome.

Typical engagement

Move high-volume judgement from the inbox to a governed assistant, then an agent

Situation

A team classifies, drafts and routes work from email and documents, hundreds of times a week. The same judgement is applied by hand, quality depends on who is on shift, and nothing is traced. A demonstration model may already exist. It has no grounding contract, no evaluation set, no fallback, and no named person who can stop it.

Intervention

Baseline the workflow: steps, volume, judgement points, failure modes and a stop rule. Ground an assistant in approved knowledge, wrap it in a path with human review on ambiguous cases, then let an agent take only the routine next action. Evaluation, a safe fallback and a kill switch are part of the first live loop — not a later hardening phase.

Concrete deliverables
  • Workflow baseline, volume and stop rules
  • Approved knowledge, evaluation set and quality bar
  • Grounded assistant with a named human-review path
  • Agent actions, operational signals, fallback and kill switch
What changes

Routine work moves through a consistent, traceable path. Ambiguous cases stay with a named reviewer, and the organisation can see — and stop — what the system did.

Who is in the room

The operations or process owner, the reviewers who currently apply the judgement, and a named accountable lead who can authorise a kill switch. Legal-partner support is scoped before any client context if the workflow carries confidentiality, regulated language or access constraints.

Team that forms

Founder-led, with an AI and ML lead on grounding, evaluation and the production loop. A product designer shapes the review surface. Data and platform join if retrieval or logging has to sit on a trusted store. Practising counsel is optional and only when the engagement needs that depth.

Evidence / stop-or-expand

The engagement is working when sampled cases meet the agreed quality bar, traces exist for actions taken, and a reviewer can stop the loop. Let the agent take more of the path only when those controls hold. Stop or keep the assistant in draft-only if the knowledge cannot be approved, or if no one will own the exceptions.

Concrete deliverables

Useful artefacts, not empty activity

The exact scope changes with the system and environment. These are the outputs the practice is designed to leave behind.

01

Workflow baseline

Steps, volume, judgement points, failure modes and a stop rule for the first live loop.

02

Grounding and evaluation set

Approved knowledge, representative cases and quality bars agreed before anything ships.

03

Human-review and agent design

Where the assistant drafts, where the agent acts, and where a named person must still decide.

04

Monitored production loop

A traceable workflow in the real system, with fallback, operational signals and a kill switch.

Put this practice against a real business problem

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