No specific job to do
Teams start with "we need an AI strategy" instead of a task to take off someone’s desk — so nothing is measured, and nothing ships.
AI development services
AI Development Services for UK Businesses
Build practical AI systems that automate work, improve customer experiences and turn business data into useful intelligence.
- Production systems, not stalled proofs of concept
- UK GDPR considered from the first sprint
- Start with a scoped pilot, not a big bet
Why AI projects stall
Most AI projects fail for unglamorous reasons.
It is rarely the model. It is an unclear use case, data that is not ready, and prototypes that were never designed to meet real users.
Where AI reliably pays off
The strongest returns come from narrow, high-volume work where a person currently reads, decides and types:
- Answering questions from your own documents and policies
- Triage and routing of emails, tickets and forms
- Pulling structured data out of invoices, contracts and claims
- Drafting first versions of replies, reports and summaries
- Checking data for patterns people have no time to look for
Knowledge the model cannot reach
The answers live in PDFs, shared drives, the CRM and people’s heads. Without clean, permissioned access, output is generic or wrong.
Demos that break on real traffic
A prototype that handles ten hand-picked questions falls over on the thousandth real one — no evaluation, no monitoring, no fallback.
Risk nobody signed off
Personal data sent to third-party models, no audit trail and unclear accountability: exactly how pilots get paused by legal or IT.
What we build
AI capabilities, delivered end to end.
From a single assistant to agents that work across your systems. Every build ships with an evaluation set, guardrails and monitoring as standard — not as an extra.
AI agents
Agents that plan multi-step work, call your tools and APIs, and hand over to a person when confidence is low.
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Workflow automation
Remove the copy-and-paste steps in operations, finance and support — AI does the reading and drafting, rules handle the rest.
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RAG and knowledge search
Search and answer across documents, wikis and systems, with citations back to the source and existing permissions respected.
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Chatbots and voice assistants
Customer-facing assistants for web, WhatsApp and phone that answer from your content and escalate cleanly to your team.
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LLM integration
Add GPT, Claude, Gemini or open models to an existing product, with prompt management, cost controls and fallbacks.
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Document processing
Extract, classify and summarise invoices, contracts, claims and forms into structured data your systems can use.
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How we work
How an AI project runs with us.
The same seven stages as any software we build, with AI-specific work inside each: a measured baseline, an evaluation set, and a person in the loop wherever the stakes justify it.
- 01
Discovery
We pick one workflow and measure it as it runs today — volumes, time per item, error rates — so the AI has a baseline to beat rather than a feeling.
A use case with a measurable baseline
- 02
Strategy
Build, buy or leave alone: we compare existing tools with a custom build, choose models on cost, latency and data terms, and agree a pilot scope.
Pilot scope, model choice and budget
- 03
UX & architecture
We design where people stay in control — review screens, confidence thresholds, escalation paths — and the retrieval, permissions and data flows underneath.
Human-in-the-loop design and architecture
- 04
Development
Two-week sprints with an evaluation set from day one, so every prompt or model change is tested against real examples before it ships.
A working pilot on your own data
- 05
Testing
Accuracy, hallucination and safety testing against the evaluation set, plus load, cost and failure-mode checks: what happens when the model is slow or wrong.
An evaluation report you can share internally
- 06
Launch
A staged rollout with logging, monitoring and usage dashboards, so you can see what the system is doing — and what it costs — from day one.
A monitored production release
- 07
Optimisation & support
We watch for accuracy drift, tune prompts and retrieval as your content changes, swap in better models when they appear, and move on to the next workflow.
Ongoing tuning and a roadmap
Technology
The stack we build AI on.
Model-agnostic by design. We choose models and infrastructure per use case — on accuracy, cost, latency and where your data is allowed to go.
Models
OpenAI GPT modelsAnthropic ClaudeGoogle GeminiLlama and Mistral (open models)
Retrieval and search
RAG pipelinesEmbeddingsVector searchHybrid keyword + semantic search
Application layer
PythonNode.js and TypeScriptNext.js and ReactREST and GraphQL APIs
Infrastructure
AWSDocker and KubernetesPostgreSQL and RedisQueues and background jobs
Machine learning
PyTorchTensorFlowComputer visionOCR and document parsing
Safety and operations
Evaluation suitesGuardrails and PII redactionUsage and cost monitoringAudit logging
We are not tied to any AI vendor. If a no-code tool or a feature in software you already pay for does the job, we will tell you.
Use cases
Where UK teams put AI to work.
Practical, bounded jobs with a clear before-and-after — the kind that get past a pilot and into daily use.
Support inbox triage
Classify incoming emails and tickets, pull the order and policy context, and draft replies an agent approves in one click.
Invoice and document capture
Read invoices, statements and purchase orders, push line items into your accounting system, and flag mismatches for review.
Internal knowledge assistant
Let staff ask questions of policies, procedures and past work, with answers that cite the exact document and respect access rights.
Tender and RFP drafting
Draft tender and questionnaire responses from your library of past answers, so the team edits rather than starting from a blank page.
Quality and compliance checks
Review calls, chats or documents against a checklist and surface only the exceptions a person needs to look at.
AI features in your product
Search, summaries, recommendations or a copilot for your own users, built into the web or mobile app you already run.
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 much does AI development cost in the UK?
Cost depends far more on scope and data readiness than on the model. A discovery sprint to scope and de-risk a use case starts from £2,000, and a pilot on one workflow is then quoted as a fixed-price project. Model usage (API fees) is a separate running cost; we estimate it up front because it scales with volume.
How long does it take to build an AI pilot?
We plan in two-week sprints. A pilot focused on a single workflow is usually measured in weeks rather than months, and after discovery you get a dated plan with the evaluation criteria that define "done" — measured against real examples, not a demo.
Will our data be used to train AI models?
Not by us. With the major providers, data sent through their business APIs is not used for training by default, and we confirm the current terms for your chosen provider during design. Where data cannot leave your environment, we can run open models in your own cloud account.
How do you stop the AI from making things up?
Retrieval with citations, so answers come from your own sources; an evaluation set that measures accuracy before every release; confidence thresholds that route uncertain cases to a person; and monitoring in production. No system is perfect, which is why the human review step is designed in from the start.
Can AI work with our existing systems?
Yes — most of our AI work is integration. We connect models to your CRM, helpdesk, document store, ERP or database through their APIs, so the AI works inside the tools your team already uses rather than in yet another tab.
Is your AI development compatible with UK GDPR?
We design with UK GDPR in mind: data minimisation, redaction of personal data the model does not need, access controls, audit logs and a clear record of where data flows. We can support your DPIA with technical detail. Sign-off stays with your DPO or legal adviser — we do not give legal advice.
Should we build a custom AI solution or buy a tool?
If a mature product already solves the problem, buy it — we will tell you so. Custom makes sense when the workflow is specific to your business, when data cannot leave your environment, or when AI is part of the product you sell.
Which AI models do you work with?
Whichever fits the job: OpenAI, Anthropic Claude and Google Gemini, or open models such as Llama and Mistral when data has to stay in your infrastructure. We keep the model layer swappable so you are not locked into one vendor’s pricing.
What happens after the AI system goes live?
We monitor accuracy, usage and cost, tune prompts and retrieval as your content changes, and plan the next workflow. You can keep us on a support retainer, or take the system in-house with full documentation.
Do you work with startups as well as established businesses?
Yes. Startups usually come to us for an AI MVP or AI features inside their product; established businesses more often want internal workflows automated. The process is the same — the amount of governance around it is what changes.
Start a project
Have an AI project in mind?
Tell us which workflow you want to change. You will get an honest view on whether AI is the right tool, and what a first version would involve.
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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