Document processing
Extracting structured data from invoices, purchase orders, contracts, applications and forms, with low-confidence cases escalated to a person rather than guessed at.
AI Consultancy
AI Consultancy & Deployment
Practical AI in real business operations — chosen for return, built to be secure, and integrated into the systems your team already uses.
The problem
Plenty of interest, very little in production
Most organisations we speak to have tried AI. Very few have anything running that they would miss if it stopped.
The usual pattern is a licence bought, a few enthusiastic weeks, and then a quiet decline in use. Not because the technology failed, but because it was never pointed at a specific job, never connected to the systems where the work happens, and never measured against anything.
Meanwhile the useful work is happening informally. Someone in finance is pasting documents into a public tool. Someone in sales has a prompt that drafts proposals. It is faster than the sanctioned process, which is exactly why it continues — and it is entirely invisible to the business.
Our approach is unglamorous and it works: find the small number of tasks where automation clearly pays back, build one of them properly, prove the value, then extend. Experimentation has its place, but it is not a deployment strategy.
How we approach it
- Starting point
- Readiness and workflow discovery, not tool selection
- Selection
- Ranked by volume, repeatability and cost of error
- Build
- One use case end to end, not several partially
- Security
- Identity, permissions and logging designed first
- Human role
- Approval on anything consequential
- Measurement
- Baseline agreed before build begins
Applications
Where AI actually helps
These are the areas where we see consistent, defensible returns in SME and mid-market organisations. The common thread is high volume, clear rules and a tolerable cost of occasional error — with a person reviewing exceptions.
Internal knowledge search
Answering "where is the current version of X" and "what did we agree with this client" from your own documents, with citations back to the source file.
Reporting
Assembling recurring management, project and compliance reports from the same sources every month, leaving people to interpret rather than compile.
Research
Structured first-pass research — market, supplier, regulatory or tender — summarised into a consistent format that a person then verifies.
Customer service assistance
Drafting responses for staff to review, retrieving account history and surfacing relevant policy. Assisting the team rather than replacing the conversation.
Email triage
Sorting a shared inbox by type and urgency, extracting the key details, and drafting the routine replies for approval.
Workflow automation
Joining steps that currently need a human to copy information between two systems, with the judgement points kept and the transcription removed.
Management information
Turning data that already exists across systems into a consistent weekly or monthly picture, without a data warehouse project first.
Repetitive administration
Onboarding paperwork, data entry, record reconciliation, chasing, filing and the small tasks that quietly consume a day a week across a team.
Equally important is where it does not help. Low-volume tasks, judgement calls with serious consequences, anything requiring accountability that cannot be delegated, and processes that are broken to begin with — automating those produces a faster version of the same problem. We cover the boundary in detail on AI automation and agents.
What we do
From readiness assessment to a system in daily use
An engagement can stop at any stage. Some clients want the assessment and shortlist and will build internally from there; others want the whole thing delivered and handed over.
AI readiness assessment
Whether your data, permissions, processes and systems can actually support AI yet — and what to fix first if they cannot. This is usually the cheapest hour you will spend on the subject.
Workflow discovery
Time spent with the people who do the work, establishing what actually happens rather than what the process document says. Automation designed from a process map that was never accurate does not survive contact with reality.
Automation opportunities
A shortlist of tasks ranked by volume, repeatability, tolerance for error and the cost of getting it wrong — with the ones that are not worth doing removed.
Secure deployment
Identity, permissions, data boundaries and logging designed before anything connects to live systems, so the security work is not a retrofit.
AI agents
Systems that take actions rather than just produce text — scoped to specific tools, operating under their own identity, with human approval where the consequences justify it.
LLM applications
Purpose-built tools for a specific job: extraction, classification, drafting, summarising, comparison. Narrow scope, measurable output, far more reliable than a general assistant.
Internal knowledge systems
Making the information already held in your documents, email and systems findable and usable — while honouring the permissions on the source data rather than flattening them.
Document workflows
Invoices, contracts, applications, reports, tenders and forms: reading them, extracting what matters, routing them, and escalating what does not fit the pattern.
Integration
Built into the systems people already use — Microsoft 365, your finance system, your CRM — rather than as another application nobody remembers to open.
Governance
Approved tools, a usable policy, data-handling rules and oversight, so staff know what is permitted without needing to ask.
Permissions
Every AI system gets the narrowest access that lets it do the job, granted through identity, reviewable, and revocable in one place.
Measuring value
Agreed measures set before build — time per case, error rate, turnaround, exception volume — so the question of whether it worked has an answer.
Security
Useful AI needs access. Access needs designing.
An AI system that cannot see your data cannot help you. The moment it can, every ordinary question of information security applies: whose identity is it acting under, what can it read, what can it change, who approved that, and what record exists afterwards.
This is the part that most AI projects discover late, and it is the most common reason a promising pilot never reaches production. Handling security and AI together is not an upsell; it is the shortest route to something you can actually put live.
Settled before anything connects
- Which identity the system acts as
- Exactly which data it may read
- Which actions require human approval
- What is logged, and who reviews it
- Where data is processed and retained
- How access is revoked, and by whom
Process
How an AI engagement runs
Deliberately front-loaded. Most of the value comes from choosing the right thing to build.
- 01
Readiness and discovery
We look at your workflows, data and permissions, and identify where AI would genuinely help. If the honest answer is "nowhere yet", you get that answer.
- 02
Shortlist and design
Two or three candidate use cases, each with an estimated benefit, the security design it would need, and what it would cost to build and run.
- 03
Build one properly
We build the strongest candidate end to end — integration, permissions, logging, exception handling and the human approval step — rather than piloting several halfway.
- 04
Measure, adopt, extend
Measured against the baseline agreed at the start. Staff trained on it. Then, and only then, a decision about what to build next.
Reassurance
We will tell you when the answer is no
A meaningful proportion of readiness assessments conclude that the business is not ready, or that the task under consideration is not worth automating. Sometimes the process needs fixing first. Sometimes the volume is too low to justify the build and maintenance. Sometimes the data is in a state that no amount of clever tooling will rescue.
Saying so is more useful than building something that quietly falls out of use six months later. We do not take vendor commission, so there is no incentive here to recommend a licence you do not need.
Where the answer is yes, you get a specific proposal: what will be built, what it will be measured on, what it will cost to build and to run, and what happens when it gets something wrong.
FAQ
AI consultancy: common questions
We have tried AI pilots before and nothing stuck. Why would this be different?
Pilots usually fail for one of three reasons: the use case was chosen because it was interesting rather than valuable, the tool was not integrated into the systems people actually use, or nobody agreed in advance what success would look like. We address all three before building anything, and we build one thing properly rather than several partially.
Do we need to buy Microsoft 365 Copilot?
Sometimes it is the right answer, often it is not. Copilot is priced per user per month and delivers value where staff spend their day in Microsoft applications and your permissions are already in good order. For a specific, high-volume task such as invoice extraction, a purpose-built workflow is usually cheaper and considerably more reliable. We will tell you which situation you are in.
Will our data be used to train someone else's model?
Not under the business-tier agreements we deploy on, which contractually exclude training on customer data. That is one of the practical differences between a business arrangement and a member of staff pasting a document into a free consumer tool — and it is a large part of why an approved-tools position matters.
How long before we see anything working?
Readiness and discovery typically takes two to three weeks. A first working automation for a well-defined task is commonly four to eight weeks after that, depending on how accessible the systems are. Anyone promising a transformation in days is describing a demo, not a deployment.
What happens when the AI gets something wrong?
It will, and the design assumes it. Systems are built to recognise low confidence and escalate rather than guess, consequential actions require human approval, and everything is logged so an error can be traced and corrected. The question is never whether it will be wrong occasionally, but whether being wrong is contained.
Can you work with our existing IT provider?
Yes, and we usually do. They know your environment and will support it afterwards. We handle the design and build, and document it so the ongoing running does not depend on us.
Find the AI work that is genuinely worth doing.
A readiness conversation is free and takes under an hour. If nothing in your business justifies AI yet, that is a legitimate outcome and we will say so.