Building products keeps our advice grounded
Alongside client work, we build our own AI products: some are now independent companies, others built with sector-expert founders. Every venture sharpens our consulting, and vice versa.
Products are where our method gets tested
A firm that builds its own products meets the real problems of data modelling, interface design, evaluation, deployment, cost, security and adoption. Those lessons return to client work. The products do not replace consulting; they keep it practical.
Real product architecture
Multi-tenant data, permissions, isolation and running cost are decisions we take for ourselves before we recommend them to a client.
Hands-on AI engineering
We build retrieval, evaluation, agent orchestration and model routing ourselves, so our advice on them comes from practice.
Operational failure modes
Running our own systems shows where agents fail: weak evidence, duplicated actions, injected instructions. We design controls from those failures.
Six ventures, one studio
Each began inside real client work and is now built and run as its own venture.
Every article, in more formats
An AI engagement platform for digital publishers. It plugs into a publisher's CMS and automatically turns each article into an AI-narrated audio version, a playlist and a short summary — no editorial work required.
What it does
Connects to a publisher's CMS and automatically generates an audio narration, a playlist and a text summary for every article, without editorial work.
Who it's for
Digital publishers and media outlets reaching readers who would not otherwise click into a text article.
How it connects to AI Workify
It grew out of the same content-automation pattern we build for clients: read a document once, generate the formats a business actually needs. It now runs as its own company.
Personalised outreach, drafted in seconds
An AI copilot for sales teams. It reads a prospect's LinkedIn profile and public activity, matches a message framework to what it finds, and drafts a short, personal outreach message — built to replace generic, mass-sent templates.
What it does
Reads signals from a prospect's profile and activity, matches them to a message framework, and drafts a personalised outreach message in seconds.
Who it's for
Sales teams doing outbound prospecting on LinkedIn who want messages that read as personal, not mass-sent.
How it connects to AI Workify
We built it after seeing how much enterprise sales work is manual research repeated prospect by prospect — the same pattern behind our agent-automation work. It now runs as its own company.
Interview the whole organisation at once
An AI-interview diagnostic from the AI Workify ecosystem. Adaptive interviews run in parallel across a team, and every finding stays linked to the words it came from.
What it does
Turns interviews into pain points, a quick-win roadmap, an economic impact view, and digital-maturity and sentiment readings. A consultant validates each interview first.
Who it's for
Operations and transformation teams who need to hear an entire organisation, not just the people who show up to a workshop.
How it connects to AI Workify
We offer it within discovery engagements, alongside in-person interviews, when a diagnosis needs to hear many people rather than a few workshops.
A short public taster is planned
We intend to offer a brief sample of the BizMRI interview on this site: a few questions about one operational pain, answered with a structured first reading. It is not available yet, and nothing on this page is interactive.
- It will not ask for names, email addresses or company details.
- It will be a sample, not a diagnosis. A full assessment hears the whole team and is validated by a consultant.
- It will run as a separate, isolated service. This site stays static and holds no keys.
In a sentence, what is your team ultimately responsible for?
We reconcile supplier invoices against purchase orders every week.
Thanks. Walk me through the last time you did it. Where is information copied or re-typed by hand?
AI skills, trained through practice
Our training platform for AI skills: short workouts, longer programmes, community practice and AI-coached evaluation. It counts repetitions of practice, not video views.
What it does
Organises learning into workouts, blocks and programmes inside private communities, and tracks competence on a four-step ladder from exposed to mastered.
Who it's for
Teams building AI fluency at scale — practitioners who learn by doing repetitions, not by watching a slide.
How it connects to AI Workify
Its practice-first pedagogy already shapes how we design AI Academy programmes. The platform is intended to deliver them at scale.
Back-office work, run by auditable agents
Our applied research into agents that run real company processes. Every action passes through one command gateway: validated, approval-tiered, receipted and reconcilable.
What it does
Records each operation as an append-only event with actor, evidence, approval tier and content hash. Twelve validation stages run before anything is written.
Who it's for
Our own finance, procurement and controllership teams, who run their real back-office work through it. It triggers no external actions and is not sold.
How it connects to AI Workify
We run our own back office on it first. What holds up there — gateways, receipts and approval tiers — becomes the governance pattern we propose to clients.
One governed view of the operation
A unified operational-intelligence layer: workflow state, agent activity, exceptions and audit events, brought from fragmented sources into one governed view.
What it does
Gives process owners one place to see what's running, what awaits approval, what failed and which evidence supports each step.
Who it's for
Process owners and operations leads who need one screen instead of five disconnected systems.
How it connects to AI Workify
We build it into solution designs when an engagement needs an operational view, specific to the client's systems, data and controls.
Every venture strengthens the consulting
Building and running our own products exposes the same problems our clients face — data modelling, evaluation, deployment, cost, security — before we ask them to take them on. What we learn shipping a venture returns to engagement work, and what we learn in engagements shapes the next venture.
Judge us on the client work
Ventures show how we think. The case studies show what we delivered, with the source of every number.