Custom AI for complex,
high-stakes work
When “off-the-shelf” isn’t enough.
Generic AI tools are great for routine, repeatable tasks. But if you want to automate your organisation’s expertise – or answer subtle, multi-step questions that span whole case files – you’ll get better outcomes from AI that’s designed around your data, decisions and duty of care.
Off-the-shelf vs custom: what’s the difference?
Not every problem needs a custom model
Off-the-shelf AI – including many “plug in your documents” products – is ideal when:
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the task is simple and repeatable (summarise, translate, draft emails)
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the risk is low (internal productivity, brainstorming)
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most organisations are doing roughly the same thing
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You should absolutely use those tools where they fit: they’re cheap, fast and getting better all the time.
Custom (or semi-custom) AI starts to make sense when:
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the task involves specialised judgment or domain-specific logic
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the cost of a bad answer is high – for people, finances or legal risk
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you need to encode your own policies, case law or ethics, not just generic internet patterns
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you want to reduce time spent on complex work, not just admin
In those situations, a “chat with your documents” bot is rarely enough. You need systems that understand how your experts think.
Where custom AI pays off
Automating expertise, not just text
We focus on problems where:
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your staff already spend hours or days reading, cross-referencing and explaining documents to each other
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each mistake can lead to harm, complaints, appeals or financial loss
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there’s a rich base of guidance, case law or internal know-how that could be made more accessible
Custom solutions can:
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cut investigation and review time on complex cases
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reduce rework and error, especially in high-pressure environments
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turn tacit expertise into reusable workflows, so new staff can perform closer to senior level
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free specialists to focus on the hardest judgments, not navigation and search
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We usually start with semi-custom, using our existing agents and components, then tailor them to your domain. That keeps costs sensible and ROI clear.
Case study: beyond out-of-the-box chat
When RAG isn’t just “chat with my PDFs”
Most organisations are now experimenting with RAG (Retrieval-Augmented Generation). The pattern is simple:
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index your documents
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retrieve the “most relevant” paragraphs
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ask a model to answer questions based on those snippets
That’s a great baseline. It reduces hallucinations and lets people search their own data.
But standard RAG still treats documents as a bag of paragraphs. It struggles when you need to:
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link logical conditions across a whole judgment or policy
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combine multiple requirements in a single query
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reason about who did what, to whom, and with what outcome, not just keywords
Example: family court caselaw search
In family courts, practitioners often need answers to questions like:
“Find all judgments where there was Cafcass involvement and domestic abuse, and the alleged abuser was given custody.”
A basic RAG chatbot might:
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find any paragraph with a meaning similar to the query
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find any paragraph mentioning a combination of words similar to “domestic abuse”, "Cafcass", or "custody"
and combie them into a summary
…but it can’t reliably tell you:
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whether the same person alleged to be abusive was given custody, or
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whether it's retrieved ALL the relevant documents
Our approach:
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models the structure of each judgment (parties, roles, issues, outcomes)
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extracts and links entities and relationships (who alleged what, what was found, what orders were made)
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builds an index that can answer multi-condition, legally meaningful queries
The result is a search system that behaves more like a junior specialist than a keyword engine – and saves experienced professionals hours per query.
What we build together
From idea to pilot to embedded tool
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Clarify the decision, not just the data
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What decisions are humans making today?
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How long does it take? What’s the cost of a mistake?
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Which parts rely on document navigation vs true judgment?
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Design the AI’s role
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Are we trying to triage and prioritise?
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Provide evidence-backed answers to complex queries?
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Produce timelines, summaries or checklists that experts review?
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Assemble components
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Use our existing agents for language harms (coercion, victim-blaming, bias) where relevant.
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Add custom retrieval and reasoning tuned to your documents and logic.
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Build lightweight UIs or integrate into tools your teams already use.
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Pilot, measure, refine
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Run side-by-side with current practice.
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Measure time saved, quality of outputs, and impact on outcomes/complaints.
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Adjust until there’s a clear, positive business case.
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Scale with confidence
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Move from pilot to production in a controlled way, with monitoring, audit logs and governance built in.
Measuring ROI from day one
For every custom project, we work with you to define:
Baseline metrics
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How long does the work take now?
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How many people touch each case or query?
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What do mistakes cost – in money, harm, complaints or appeals?
Target improvements
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e.g. 30–50% reduction in time spent on document review for a given workflow.
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Fewer escalations, repeats or complaint letters.
Evidence plan
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How we’ll gather data during the pilot and what “success” looks like for you.
That way you can explain to boards, funders or regulators not just what you’ve built, but why it pays for itself.