Confidentiality comes first
Privileged advice, client financials and market-sensitive information cannot go into a public chatbot. Staff know it, so they either avoid AI or use it quietly.
AI development company · London
AI Development Company in London
We help London law firms, accountancies, consultancies, insurers and media businesses put AI to work on bounded, useful jobs — reading submissions, drafting first versions, finding the right precedent — with governance your risk team can review and client confidentiality designed in from the first workshop.
- One workflow at a time, measured before and after
- Client data handled under rules you approve
- A person signs off anything client-facing
Why London firms hesitate
What keeps London firms stuck at the AI pilot.
Most firms have tried a chatbot. Far fewer have AI doing real work inside a live process — usually for reasons that have little to do with the model.
Where AI earns its place
The best first projects are narrow and high-volume, where skilled people spend hours reading before they can decide:
- First drafts that a professional then edits and signs off
- Pulling key terms out of submissions, contracts and filings
- Search across precedent and past work, with sources cited
- Checking documents against a checklist and flagging exceptions
Regulators expect control
The ICO, the FCA and professional bodies expect firms to understand, supervise and evidence the tools they use. “The vendor said it was fine” is not an answer.
Expert time is the bottleneck
Partners, underwriters and senior managers will not spend weeks testing a pilot. Anything that needs a lot of their time to evaluate stalls.
Generic tools, specific work
Off-the-shelf assistants do not know your precedents, underwriting guidelines or house style — and cannot see the systems where that knowledge lives.
Use cases
AI use cases across London’s core sectors.
Bounded jobs with a clear before-and-after, chosen because a professional stays responsible for the result.
Precedent and clause search
Ask questions across your precedent bank and closed matters and get answers that link to the source clause — with matter permissions and information barriers respected.
Client paperwork, sorted
Read the bank statements, invoices and receipts clients send in, extract the figures, and flag what is missing before anyone starts the accounts or the return.
Submission triage
Extract key terms from broker submissions and slips, check them against appetite, and hand underwriters a summary instead of a stack of attachments.
Find what the firm already knows
Search past proposals, frameworks and deliverables by client, sector or problem — limited to what each consultant is allowed to see.
Summaries for review
Condense research, fund documents and client correspondence into drafts that portfolio managers and client-reporting teams check and approve.
Archive search and tagging
Transcribe, tag and search archives of articles, audio and video, so editors and producers can find and reuse what they already own.
What we build
AI development for London firms.
Most engagements start with one workflow and one of these building blocks, then grow once the first is in daily use.
Choosing the right use case
A short, structured look at where AI would pay off in your firm and where it would not — ending in one pilot worth running.
AI consulting
Knowledge search with citations
Retrieval across precedent banks, policies, research and past work, with every answer linked to the passage it came from.
RAG and knowledge search
Document extraction
Structured data from slips, contracts, statements and filings, with low-confidence fields routed to a person to check.
Document processing
AI for law firms
Intake, review checklists, clause comparison and drafting support built around how fee-earners actually work and record time.
AI in legal
AI for insurers and brokers
Submission triage, claims document handling and wording comparison for underwriting and claims teams in the London Market.
AI in insurance
AI for financial services
Research summaries, client-reporting drafts and operations triage for banks, wealth managers and fintechs, with review built in.
AI in financial services
Governance
AI governance your risk team can sign off.
In most London firms, someone has to say yes before AI touches client work: a compliance officer, a data protection officer, a risk committee or the partners. We plan for that conversation from the first workshop, not after the demo.
That means a plain description of what the system does and does not do; a record of which data goes to which model provider, under which terms and where it is processed; logs of inputs, outputs and approvals; and an evaluation report showing how the system performed on your own examples. Together, those make the ICO’s guidance on AI and data protection — and your own policies — practical to apply.
For firms regulated by the FCA, obligations such as the Consumer Duty and senior managers’ accountability stay with you, and we do not give legal or compliance advice. What we can do is build systems that are easier to supervise: outputs that show their sources, human review wherever customers are affected, and audit trails that answer “why did this happen?”.
- Data-flow record: what goes where, under which terms
- Evaluation report on your own examples before go-live
- Audit log of inputs, outputs and who approved them
- A switch back to the manual process if something goes wrong
Confidentiality
Client confidentiality, designed in.
The controls we reach for when AI works with privileged, commercially sensitive or personal data. Which ones you need is agreed with your DPO and risk team in discovery.
Model choice by data terms
We choose providers on where data is processed and whether it can be retained or used for training — and confirm the current terms for your account, not last year’s blog post.
Open models in your cloud
When client material has to stay inside your own environment, we deploy an open model — Llama or Mistral, for example — in your cloud account, with no external API involved.
Redaction before the model
Names, account numbers and other identifiers the task does not need are removed before a prompt is sent, and restored only in the reviewed output.
Permission-aware retrieval
Search respects matter, client and team permissions, so an assistant cannot surface a document the person asking could not open themselves.
A full audit trail
Requests, sources, outputs and approvals are all recorded — the evidence you need when a client, an auditor or a data subject asks what happened.
Your accounts, your keys
Model provider accounts, cloud resources and API keys sit in your organisation’s name, so access can be reviewed or revoked without us.
How we work
How an AI pilot runs for a London firm.
Seven stages, built around the scarcest resource in most firms: expert time. We ask for it at the start and at review points, and protect it in between.
- 01
Discovery
A use-case workshop on video with the people who do the work today, then a measured baseline of volumes, time per item and error rates.
One use case with a measured baseline
- 02
Strategy
Build, buy or leave alone. We compare features in software you already license with a custom build, choose models on data terms, cost and accuracy, and agree who signs off.
Pilot scope, model choice and approvers
- 03
UX & architecture
Review screens designed for busy professionals: sources beside every answer, confidence shown plainly, and one-click accept, edit or reject.
Review workflow and data-flow diagram
- 04
Development
Two-week sprints with a demo at the end of each, code review on every change, and a staging environment your team can use.
Working software every sprint
- 05
Testing
Accuracy, hallucination and permission testing against an evaluation set built from your real, anonymised examples — reported in a form your risk committee can read.
Evaluation report for sign-off
- 06
Launch
A staged rollout to one team or practice group first, with usage, cost and accuracy dashboards from day one.
Monitored release to a pilot group
- 07
Optimisation & support
After launch we fix, measure and improve — a support retainer, a roadmap of next features, or a clean handover to your own team.
Support plan or handover
Engagement models
Ways to start with AI.
Most firms begin with a discovery sprint on one workflow. If the pilot earns its place, it moves to a fixed-price build or an ongoing team.
Discovery sprint
From £2,000 fixed fee
You have an idea or a problem, but not yet a scope you would trust a quote against.
- Workshops with your team
- Prototype or technical spike
- Written scope, plan and estimate
Fixed-scope project
Quoted after discovery
A defined build — an MVP, a rebuild or a feature set — with milestones and a fixed budget.
- Agreed milestones and deliverables
- Milestone-based billing
- A demo at the end of every sprint
Managed product team
From £9,500 per month
A small cross-functional pod — engineering, QA and delivery lead — that owns an outcome.
- One accountable delivery owner
- Sprint planning and reporting
- QA and code review included
Prices are in GBP. Every estimate is confirmed in writing after a discovery call — the figures above are where engagements start, not a quote.
Relevant work
Products we have designed and built.
A client-intake and risk-check platform for a law firm, and an AI imaging product where clinicians review every result — the same human-in-the-loop pattern we apply to professional work.
All case studiesWhy Techsleight
What working with us is actually like.
How we run AI work for firms where confidentiality and supervision are not optional.
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 much does an AI pilot cost for a London firm?
Discovery on a single use case starts from £2,000; it finishes with a fixed price for the pilot. Model usage is a separate running cost that rises with volume, so we forecast it before you commit and track it against that forecast once the system is live.
How do you run the use-case workshop with busy partners?
On video, in shorter sessions that fit around their diaries rather than one long day, with a shared board and the outcomes written up afterwards. Day-to-day delivery is remote too: video calls, a shared Teams or Slack channel, fortnightly demos and a weekly written update, all on UK business hours.
Where will our data be processed, and who on your side can see it?
That is agreed with you before any real data is used. We can host in UK or EU cloud regions, build and test with anonymised examples, and limit any access to live data to named engineers, with every access logged. Our engineers are in the UK and India, so your DPO will want to review the transfer safeguards — we provide the detail they need.
Could our confidential documents end up training someone else’s model?
Not if the system is set up properly. The major providers’ business API terms exclude customer data from training by default, and we check the terms that apply to your own account before any client material is sent. If your policies rule out external providers entirely, open models can run inside your own cloud instead.
Are you regulated by the FCA or the SRA?
No. We are a software development company, not an authorised firm or a law firm, and we do not give legal or compliance advice. We build and document systems so your compliance officer, COLP or DPO can assess them against your own obligations — the decisions stay with you.
Which tasks should a professional firm not hand to AI?
Anything that reaches a client, a court or a regulator without a qualified person checking it, and anything where you could not explain afterwards why the system did what it did. We design AI to prepare, draft and flag; the professional decides and signs.
Can AI work inside our document management and practice systems?
Usually, through their APIs — document management, practice management, policy administration and CRM systems often expose one. Where a system has no usable API, we say so in discovery and design around exports, rather than promising an integration we cannot deliver.
How will we know whether the pilot worked?
By comparing it with the baseline from discovery: time per item, error rates and throughput on your real workflow, plus accuracy on an evaluation set of your own examples. If the pilot does not beat the baseline, we will tell you plainly rather than recommend scaling it.
Does this approach work for media and creative businesses?
It does, with different questions. For publishers, broadcasters, agencies and production companies the useful jobs are transcription, tagging, archive search and first-draft copy, where rights, attribution and editorial standards matter most. The method is the same: a narrow pilot, measured, with editors in control.
Who signs the contract, and what hours do you keep?
Krapton IT Consultancy — the UK company that trades as Techsleight Labs — is the contracting party, and prices are in pounds. The team works UK business hours, and we will sign your NDA or confidentiality terms before discovery begins.
Start a project
Have a workflow worth testing with AI?
Tell us about the work, the volumes and who would need to approve a pilot. We will come back within one working day with an honest view on whether AI fits, and what a first step would look like.
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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