Answers without sources
A fluent answer with no citation is of little use to a lawyer, who has to check it anyway — and dangerous if someone does not. Every output needs a link back to the text.
AI by industry · Legal
Legal AI for UK Firms and In-House Teams
Contract review against your own playbook, drafting from approved precedents and search across your know-how — built on retrieval so every point links to a source document, evaluated on your own matters, and reviewed by the lawyer who stays responsible for the advice.
- Every answer cites the clause or document
- Matter permissions applied to search and AI
- Lawyers approve anything that leaves the firm
Why legal AI disappoints
Legal AI falls short when it cannot show its working.
Lawyers will not rely on an answer they cannot check. Most disappointing legal AI pilots come down to missing sources, a generic playbook or documents the tool should never have seen.
Where AI helps lawyers most
The strongest cases are high-volume reading and first drafts, where a lawyer checks the output against the source:
- First-pass review of standard contracts against a playbook
- Clause and date extraction across a data room
- Cited answers from precedents, notes and past advice
- First drafts from approved clauses and the matter file
- Chronologies and summaries of long correspondence and bundles
Someone else’s playbook
Off-the-shelf review tools apply generic positions. Your fallbacks, risk appetite and client-specific terms are what make a first-pass review useful to your lawyers.
Search that ignores information barriers
An AI index built without matter permissions can surface a document to someone on the wrong side of an information barrier. Access rules have to apply to the AI too.
Long documents, lost definitions
Leases, facility agreements and data rooms run to hundreds of pages. Naive splitting separates definitions from the clauses that use them, and answers go subtly wrong.
What we build
Legal AI, grounded in your own documents.
We start from your precedents, playbooks and matter data — not a generic legal model — and measure every capability on documents your lawyers have already reviewed.
Playbook-based contract review
Language models compare a contract with your standard positions and fallbacks, flag deviations and missing clauses, and propose mark-up linked to the exact text.
Document processing
Data room clause tables
Extraction pipelines that pull parties, dates, change of control, assignment and termination rights into a review table, each cell linked to its page.
Document processing
Cited know-how retrieval
Retrieval-augmented search over precedents, research notes and past advice that applies matter permissions and returns answers with the passages they rely on.
Knowledge search
Precedent-based drafting assistance
Drafts assembled from your approved clauses and the matter facts, with the model filling gaps only where you allow and every generated sentence marked for review.
Generative AI
Bundle and correspondence summaries
Summaries of long email chains, witness statements and bundles, with each point referenced to the page it came from so it can be checked in seconds.
Discuss this
Obligation and renewal tracking
Notice periods, renewal dates and ongoing obligations extracted from signed contracts into a register with reminders, each entry linked to its clause.
Discuss this
How we work
From your playbook to a reviewed pilot.
Our seven stages, with legal-specific work inside them: a playbook written down before any prompt, an evaluation set of documents your lawyers have already reviewed, and permissions designed before indexing starts.
- 01
Discovery
We pick one document type or question set and measure how a first review works today: how long it takes, who does it and what they look for.
A baseline for one document type
- 02
Strategy
We turn your positions and fallbacks into a written playbook the system can apply, settle hosting and model terms with your risk team, and agree what the tool will not do.
A written playbook and scope
- 03
UX & architecture
Review screens that put each finding next to its clause, accept and reject controls, and a permission model that carries matter access and information barriers into the index.
Review design and permission model
- 04
Development
Retrieval, extraction and prompts built against documents your lawyers have already reviewed, so the system is measured against a known answer.
A pilot on your own documents
- 05
Testing
Caught, missed and wrongly raised issues counted by clause type, citation checks on every answer, and tests that restricted documents never appear for the wrong user.
Evaluation results by document type
- 06
Launch
A rollout to one practice group, with feedback on every accepted and rejected suggestion and usage and cost visible from the first day.
A monitored practice-group launch
- 07
Optimisation & support
Playbook updates as your positions change, re-testing after every model change, and extension to the next document type once results hold.
Playbook upkeep and a roadmap
Technology
Built for long legal documents.
Chosen for long documents, reliable citations and where client data is allowed to go — including open models inside your own cloud account.
Language models
Anthropic ClaudeOpenAI GPT modelsGoogle GeminiLlama and Mistral (self-hosted)
Retrieval
Clause-aware chunkingHybrid keyword and vector searchRe-rankingPermission-filtered indexes
Document handling
Word and PDF parsingOCR for scanned documentsTracked-changes outputDefined-term resolution
Integrations
Document management APIsPractice management APIsMicrosoft 365 and OutlookE-signature platforms
Engineering
PythonNode.js and TypeScriptNext.jsPostgreSQL
Controls
Evaluation suitesCitation checkingAudit loggingPrivilege tagging
We are a software development company, not a law firm, and we do not give legal advice. Professional judgement, supervision and sign-off stay with your lawyers.
Use cases
Legal work AI can take a first pass at.
High-volume reading and drafting, where a lawyer’s review is faster than starting from nothing.
NDA first review
Review incoming NDAs against your standard positions, return a marked-up draft with a short note of deviations, and escalate anything unusual to a lawyer.
Lease report tables
Pull rent, term, break options, repair obligations and alienation provisions from leases into a report table for the lawyer to check and finalise.
Due diligence issue lists
Group extracted clauses across a data room by risk — change of control, exclusivity, unusual liabilities — so the team starts its report from a sorted list.
Chronology building
Build a dated chronology from emails, letters and witness statements, each entry referenced to its source page, for the fee earner to verify and edit.
Answers from legal guidance
Let colleagues ask the legal team’s approved guidance — signing authority, NDA rules, data sharing — and get cited answers, with anything new routed to a lawyer.
Precedent drift checks
Flag precedents that differ from the current clause bank or have not been reviewed for a while, so the knowledge team knows what to update first.
Relevant work
Products we have designed and built.
A selection of client projects related to this work. Each case study covers the brief, the approach and the stack.
All case studiesWhy Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
How is legal AI different from using a public chatbot?
A public chatbot answers from its general training and, depending on its terms, may keep what you type. What we build answers from your own documents through retrieval, runs under business API terms or on open models in your cloud, applies your matter permissions, and shows the source for each point so a lawyer can check it.
How do you measure accuracy on contract review?
We take contracts your lawyers have already reviewed and compare the system’s flags with theirs: which issues it caught, which it missed and which it raised wrongly. Results are reported by clause type, so you can see where it is dependable and where a lawyer must read closely. Missed issues matter most, and the review workflow is designed around that.
Can the AI apply our own negotiating positions?
Yes — that is the point of a playbook. We write your standard positions, acceptable fallbacks and red lines into a structured playbook with your lawyers, and the system applies it clause by clause. When positions change, the playbook is updated and re-tested; no model retraining is needed.
Does it work on long documents like leases and facility agreements?
Yes, with the right handling. We split documents by clause rather than by fixed length, resolve defined terms so the model sees what they mean, and retrieve the relevant sections for each question. Long-context models help, but retrieval and citations are what make the answers checkable.
Can AI give legal advice to our clients directly?
We do not build it to. The tools we build prepare work for lawyers — reviews, drafts, summaries and answers to internal questions. Anything that reaches a client, a counterparty or a court is reviewed and approved by a lawyer, who remains responsible for it under your professional obligations.
How do you handle privilege and information barriers?
Permissions from your document and practice management systems are carried into the search index, so the AI only retrieves what each user is allowed to see, including behind an information barrier. Documents can be tagged as privileged and excluded from particular uses. Your risk team defines the rules; we build and test them.
Which models do you use, and can client documents stay in the UK?
Anthropic Claude, OpenAI GPT models, Google Gemini or open models such as Llama and Mistral, chosen per task. Where your policies require it, we use providers with UK or European processing options — confirmed for your chosen provider during design — or run open models in your own cloud account so documents never leave it.
Have you built software for law firms before?
Yes. For CO-LAW Group, a boutique law firm, we built a client intake and due diligence platform covering risk checks, document collection and compliance workflows. It was not an AI project, but it dealt with the same foundations legal AI depends on: sensitive documents, structured matter data and controlled access.
What does a legal AI pilot cost?
A discovery sprint to pick the document type, write the playbook and assemble the evaluation set starts from £2,000. The pilot is then quoted at a fixed price. Model and API fees are paid to the provider and depend on document volumes; we estimate them before you commit.
Start a project
Have a document type that eats fee-earner time?
Tell us which documents, roughly how many a month and how your lawyers review them today. We will tell you what AI can take on, how we would measure it and what stays with your lawyers.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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