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My Services

AI services for teams with messy workflows and real-world constraints.

If you are not sure whether you need an audit, an agent or a production sprint, that is the point. I make the readiness decision visible before the work gets expensive.

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Playful Builds, Serious Outcomes
Before you choose an offer

Start with the decision you probably have not named yet.

Most teams do not arrive asking for readiness. They ask for an agent, a prototype or a clever automation. Readiness is the part that tells you whether that is the right move.

What I do

I help teams turn messy AI intent into scoped, useful delivery. That usually means readiness first, then the right build.

  • AI readiness audits and use-case prioritisation
  • Scoped AI agents, automations and custom workflows
  • Prototype-to-production pilots with testing, guardrails and handover

What I do not do

I am not here to dress a vague idea up as innovation. If there is no outcome, owner or useful workflow, the build pauses before it starts.

  • Generic chatbot reskins with no workflow behind them
  • Open-ended AI transformation work with no success criteria
  • Dark patterns, surveillance-heavy work or greenwashing

How to tell if I am right for you

You want someone who will challenge the brief, make the risks visible and still ship the working thing.

  • You have a real workflow, not only an AI idea
  • You care about adoption, users, risk and handover
  • You want plain-English decisions before tool choices
Readiness first

Most AI build requests are readiness requests in disguise.

That is not a blocker. It is the useful part. Readiness shows what to build, what to fix first, what to leave alone and who needs to own the result.

Start with readiness

Nobody can name the workflow that should change first.

There are ideas, tools and pressure, but no shared view of the task worth fixing.

The data exists, but ownership is fuzzy.

Access, quality, permissions or human review points are still unclear.

Stakeholders are excited, but success is still vibes.

There is no agreed measure for time saved, accuracy, adoption, cost or user experience.

The team is asking for an agent before the job is defined.

That is usually a readiness problem hiding inside a build request.

Usually first

AI Readiness Audit

Find the AI work worth doing first.

A practical audit for teams with ideas, pressure and too many possible starting points. I map the workflow, spot the risks and choose the use case that deserves build time.

Best for: AI readiness assessment, workflow automation planning, governance-first AI.

Problem

The team has ideas, tools and pressure to move. The workflow worth fixing first is not obvious.

Risk

Without a workflow map, success criteria and a risk check, weeks go into automating the wrong thing.

Fix

I audit the process, data, users and handoffs, then give you a prioritised AI readiness plan.

What you get

  • Workflow and data map
  • Use-case shortlist with clear priority
  • Risk, governance and human review notes
  • Next-step plan for build, pilot or pause

What changes

  • Know where AI helps and where it does not
  • Avoid building around a vague idea
  • Give stakeholders a plan they can challenge

Useful when enthusiasm is high, but ownership, data quality and value measurement still need sorting.

Talk this through
Offer 2

Custom AI Agent Build

Build an agent around the actual job.

A focused build for teams that need more than a generic chatbot. The work starts with the task, permissions, handoffs and review points, then turns that into a usable AI agent or workflow.

Best for: Custom AI agents, Copilot Studio agents, business process automation.

Problem

A generic chatbot will not handle messy data, unclear permissions or human review points.

Risk

The agent looks useful in a demo, then breaks when the real workflow gets involved.

Fix

I design and build scoped Copilot Studio agents, automations and custom workflows around the real task.

What you get

  • Agent scope and conversation design
  • Workflow logic, data connections and review steps
  • Test scenarios for expected and awkward cases
  • Documentation for handover and support

What changes

  • Reduce repeatable admin without losing control
  • Make human review points explicit
  • Give teams a tool that fits the way work happens

Useful when the goal is practical automation, not a chatbot-shaped ornament.

Talk this through
Offer 3

Prototype-to-Production Sprint

Move the useful demo towards production.

A delivery sprint for AI prototypes with legs. I add the missing operational pieces: testing, measurement, guardrails, ownership and handover.

Best for: AI proof of concept, production AI, safe AI implementation.

Problem

The idea has legs, but ownership, measurement, testing and support are still vague.

Risk

No guardrails, no handover and no success signals means the pilot quietly dies.

Fix

I turn the prototype into a production-ready pilot with testing, documentation and ownership built in.

What you get

  • Production-readiness gap review
  • Pilot success criteria and measurement plan
  • Testing, guardrails and support notes
  • Handover pack for the team that will own it

What changes

  • Stop good prototypes getting stuck in demo land
  • Give stakeholders evidence, not vibes
  • Prepare the pilot for real users and real constraints

Useful when the idea works, but the operating model has not caught up.

Talk this through
How I work

The useful bits happen before and after the build.

Good AI work needs the dull-looking bits to be handled early: scope, data, guardrails, testing, ownership and handover.

1

Scope

Map the work before the build.

I get clear on the workflow, users, data, decision points and what success should look like.

2

Design

Shape the useful version.

I choose the smallest valuable build, define guardrails and make the human handoffs obvious.

3

Build

Ship the working thing.

I build the agent, automation or pilot around the real task, then test the awkward cases.

4

Handover

Leave it usable.

You get documentation, ownership notes and a practical next-step plan. No mystery machine.

Fit check

Before I build, I make sure the brief is worth building.

These filters keep the work practical, owned and measurable. They also make the handover feel less like a mystery machine.

Good fit

  • You have a real workflow, not only an AI idea.
  • A decision owner can help unblock scope and access.
  • Users, risk and handover matter as much as the build.
  • You want practical delivery with legal-services rigour.

Not a fit

  • You want AI theatre with no measurable outcome.
  • The work depends on dark patterns or surveillance-heavy design.
  • Nobody owns the workflow after launch.
  • The brief is greenwashing dressed as innovation.
Proof, not vibes

Delivery that stands up to scrutiny.

The service work is backed by production AI, enterprise delivery, impact projects and legal-services rigour.

Retail enterprise AI experience, including PetsDetect at Pets at Home.

Prototype-to-production delivery across Microsoft Copilot Studio, Azure and custom software.

18 years of legal-services rigour applied to documentation, risk and stakeholder detail.

Tech-for-good delivery through Scottish Tech Army and impact-led projects.

Questions people ask first

Useful answers before you book.

What is an AI readiness assessment?
A short review of your workflow, data, users, risks and success criteria. It shows which AI use case is worth building first.
When should a business build a custom AI agent?
When the task needs business context, permissions, handoffs or review steps a generic chatbot misses.
Why do AI proof-of-concepts fail before production?
They often miss guardrails, measurement, support ownership and handover. The idea works, but the operating model is missing.
What is the difference between an AI proof of concept and a pilot?
A proof of concept shows the idea could work. A pilot has success criteria, testing, guardrails, users, ownership and a plan for what happens next.
Do you only work with Microsoft Copilot Studio?
No. Copilot Studio is a strong fit for some enterprise workflows, but the tool should follow the problem. I work across AI agents, automation and custom software where that is the better route.
Do you work with charities and social enterprises?
Yes. Digital Boop sits between tech for good and enterprise delivery. The common thread is practical work that reduces admin, improves trust or helps a team ship something useful.
What makes AI automation safe enough for small teams?
Clear scope, known data sources, human review, simple failure paths and documentation. Small teams need guardrails that fit the work, not bureaucracy theatre.
Digital Boop blue robot avatar for practical support

Bring the messy workflow first.

I will work out whether it needs an audit, an agent build or a production sprint. If AI is the wrong answer, I will say that too.

Talk through the options