AI implementation services for UK businesses

AI implementation is the process of taking an identified AI opportunity and delivering it as a working system inside your business. The work covers data preparation, model or tool selection, prompt and tool design, system integration, evaluation against real tasks, deployment, and post-launch adoption support. The AI Consultancy delivers AI implementation engagements for UK SMEs and enterprise clients across professional services, financial services, logistics, healthcare and dental, and the public sector. Our 2026 primary platform is Anthropic Claude; we also implement ChatGPT, Google Gemini, Microsoft Copilot, and bespoke LLM stacks where they are the better fit. Engagements start from GBP 5,000 and run between 2 and 12 weeks.

Common AI implementation patterns

Most UK SME and mid-market implementations fall into one of five patterns. Identifying the pattern early shortens scoping and avoids the common mistake of designing a custom solution where a configured one is already available.

  • Configured LLM rollout. Claude.ai Team or Enterprise, ChatGPT Business or Enterprise, with admin configuration, SSO, role-based access, audit logging, and a documented usage policy. The fastest pattern to value for knowledge-work-heavy teams.
  • Retrieval-augmented assistant. An LLM grounded in your own documents (SharePoint, Drive, NetDocuments, iManage, a case-management system) via the Model Context Protocol or a dedicated RAG pipeline. The pattern most often used for internal knowledge management and fee-earner assistants.
  • Workflow automation. An LLM stitched into a business process via the Anthropic API, AWS Bedrock, or Vertex AI, with deterministic rules around the agentic step. The pattern that drives most SME time-savings.
  • Customer-facing chatbot or voice agent. A bounded conversational interface with handoff to a human, audit logging, and a tight tool whitelist. Higher governance bar than an internal rollout.
  • Bespoke model or product.A custom AI feature that becomes part of the commercial product, usually built on a foundation model API with a proprietary data layer. The highest-effort pattern, with R&D tax credit eligibility in most cases.

What do you get?

  • 1.Technical requirements specification and architecture design, signed off before build.
  • 2.Data audit, cleaning, and preparation for the AI workload, including a documented data classification.
  • 3.Model and tier selection, with reasoning for the choice and a fallback option if the primary platform changes commercially.
  • 4.Prompt, tool, and system-prompt engineering validated against a held-out test set drawn from your real tasks.
  • 5.System integration with your CRM, ERP, document repositories, and cloud infrastructure (AWS, GCP, Azure).
  • 6.Evaluation harness covering pass-fail rates, hallucination checks, prompt-injection defence, and data leakage red-team tests where the use case warrants.
  • 7.Deployment to production with monitoring, alerting, and a cost-cap budget on agentic runs.
  • 8.Role-specific user training, an admin runbook, and a written acceptable use policy aligned to UK GDPR and the EU AI Act.
  • 9.Post-launch support period (typically 2 to 4 weeks) with a final ROI report against the baseline.

How long does an AI implementation take?

Most AI implementations take 2 to 12 weeks, depending on the pattern, the data readiness, and the regulatory perimeter. Indicative shapes:

  • Simple (2 to 4 weeks): chatbot, document classifier, single-integration workflow, configured LLM rollout for under 25 users.
  • Medium (4 to 8 weeks): multi-system integration, RAG pipeline over a defined corpus, bespoke workflow with audit and approvals.
  • Complex (8 to 12 weeks): custom model or evaluation harness, regulated-sector compliance overlay, enterprise rollout above 100 users, multi-tenant architecture.

All projects follow an iterative delivery approach with weekly progress reviews, defined milestones, and named acceptance criteria written into the statement of work. We do not run implementations against open-ended scope.

Worked examples from our delivery work

Three published engagements show what implementation looks like in three different contexts.

Video-first AI property surveys

MoverAI, built for Master Removers Group on Google Gemini Flash with a Cloud Run + Firestore backend. Customers record a short walkthrough; the platform produces a structured inventory with per-item cubic footage. Eliminates 1 to 2 hours of surveyor time per move and has no UK equivalent.

Read the MoverAI case study

AWS architecture for a high-end dental practice

Eight-domain discovery across AmniVogue dental, a UK private dental sector analysis, and a full AWS cloud-native architecture (Lex, Connect, Polly, SES, SNS, API Gateway, Lambda, DynamoDB, RDS, SageMaker, S3 with DICOM storage) deployed in eu-west-2 for UK data residency.

Read the AmniVogue case study

Cloud-native AI for an 11-site dental group

A multi-chain dental group scaled from 1 flagship to 11 practices on a single AWS platform combining patient communication (Lex, Connect, Polly), serverless operations (Lambda, API Gateway), and AI-powered clinical support (SageMaker, S3 DICOM). Total infrastructure cost under GBP 1,750 per month.

Read the multi-chain case study

What does AI implementation cost?

AI implementation engagements start from GBP 5,000 for straightforward deployments such as a customer-facing chatbot or a document-processing pipeline. Medium implementations involving multi-system integration are typically GBP 15,000 to GBP 50,000. Complex enterprise projects with custom models or regulated-sector compliance work are scoped per project. Every project is scoped and quoted before work begins, after a free initial conversation.

Where the work qualifies for an Innovate UK BridgeAI grant, a Smart Grant, a Knowledge Transfer Partnership, or an R&D tax credit claim, we run that screening alongside the commercial proposal. See our grant-funded AI implementation service for the application route.

Questions to ask before signing an AI implementation engagement

Most AI projects that disappoint were scoped badly rather than built badly. These are the questions worth putting to any supplier, including us, before a contract is signed.

  • What exactly are we getting at the end, and in what state? A demonstration, a prototype, a pilot with real data, or a production system are four different things at four different prices. Agree which one before work starts.
  • Who owns the code, the prompts and the data? Get this in writing. It is straightforward to agree at the start and awkward to unpick later.
  • What happens when the model changes? Model providers deprecate and retire versions on published schedules. Ask who is responsible for migration and whether it is inside the fee.
  • How will we know if it is working? A measurable success criterion agreed before build is the single best predictor of a project that lands.
  • What does it cost to run, not just to build? Usage-based pricing means the bill scales with adoption. Ask for a running-cost model, not just a build quote.
  • Who supports it once it is live, and under what response times? An unsupported production system is a liability rather than an asset.

Demo, prototype, pilot or production: scoping the right thing

A great deal of wasted spend comes from building one of these while expecting another. We classify every engagement at the outset so the cost, the timeline and the level of hardening all match the intention.

LevelWhat it provesWhat it is not
DemoThat the idea is possible. Happy path only, mock data acceptable.Anything you can put in front of a customer.
PrototypeThat the workflow holds together end to end with limited hardening.Ready for real users or real data.
PilotThat it works with real data and real users, with safeguards and operational testing.A system with a full support and monitoring wrapper.
ProductionThat it can be relied on: full controls, monitoring, documentation and a support plan.Cheap or quick, if the earlier stages were skipped.

Related services and industries

Related services

Industries

For background, see the Knowledge Hub: Implementation section.

Frequently asked questions

What does AI implementation involve?+
AI implementation covers the full technical delivery lifecycle: requirements gathering, data preparation, model or tool selection, prompt and tool design, system integration, evaluation against real tasks, deployment, and post-launch monitoring. The scope depends on the use case, but every implementation includes a written acceptance test set so the result can be measured against the original brief, not against a vendor demo.
How long does a typical AI implementation take?+
Simple implementations such as a chatbot or document classifier can be delivered in 2 to 4 weeks. Medium projects involving multi-system integration, custom workflows, or retrieval-augmented generation typically take 4 to 8 weeks. Complex projects with custom models, large datasets, multiple regulators, or enterprise rollout typically take 8 to 12 weeks. Pilot-to-production work that began as a proof of concept usually compresses to the lower end of these ranges.
Do you work with our existing technology stack?+
Yes. We integrate with your current systems wherever possible. Common integrations include CRM platforms (HubSpot, Salesforce, Pipedrive), ERP and finance systems (Xero, QuickBooks, NetSuite, Sage), Microsoft 365, Google Workspace, AWS, Google Cloud, Azure, document repositories (SharePoint, Drive, NetDocuments, iManage), and internal databases. We avoid unnecessary re-platforming and document any integration that materially constrains the design.
Which models do you implement?+
Anthropic's Claude family (Sonnet 4.6, Opus 4.7, Haiku) is our 2026 primary platform for document-heavy, regulated, and coding-intensive work. We also implement OpenAI ChatGPT (Plus, Business, Enterprise, and the API), Google Gemini, and Microsoft Copilot where they fit. For specific tasks (image analysis, voice, embeddings) we mix providers. The model recommendation falls out of the use case, the data residency constraints, and the existing cloud commitment, not the other way round.
What about data residency and UK GDPR?+
Most implementations need a documented residency posture before any production rollout. Claude can run in a UK or EU residency configuration via AWS Bedrock (UK South or EU Ireland) and Google Cloud Vertex AI (EU regions). ChatGPT Enterprise supports a documented data processing addendum and zero data retention clauses. The UK-US Data Bridge has been in force since 12 October 2023 and covers transfers to certified US providers. We document the residency, retention, and lawful basis for processing as part of the engagement.
What happens after go-live?+
Every implementation includes a post-launch support period of typically 2 to 4 weeks, with weekly review calls and active monitoring. During this window we resolve adoption issues, refine the prompt and tool layer against real usage, and capture the time-saved and quality measurements that feed the post-engagement ROI report. Ongoing managed support is available as a separate retainer where the client wants us to keep the operational running.
Can you implement AI solutions recommended by another consultancy?+
Yes. If you have an existing AI strategy or roadmap from another provider, we can pick up from the implementation phase. We will review the strategy for technical feasibility before beginning delivery, and flag any structural issues (data readiness, residency mismatch, governance gaps) before sign-off rather than during build.
What does AI implementation cost?+
AI implementation engagements start from GBP 5,000 for straightforward deployments such as a customer-facing chatbot or a document-processing pipeline. Medium implementations with multi-system integration are typically GBP 15,000 to GBP 50,000. Complex projects with custom models, regulated-sector compliance overlays, or enterprise rollout are scoped per project. Where the work qualifies for an Innovate UK BridgeAI grant, a Smart Grant, or an R&D tax credit claim, we run the eligibility screen alongside the commercial proposal.
Engagement models and pricing

Readiness Sprint from GBP 3,500 · Discovery and Pilot from GBP 15,000 · Build and Embed from GBP 40,000 · Day rate GBP 950 to GBP 1,500. All prices exclude VAT.

Book a free 30-minute implementation scoping call

If you have a defined AI use case ready to deliver, we will run a free 30-minute call covering the technical shape, residency, and integration constraints. Pricing is scoped after the call, never quoted from a list.

Or call +44 2033 550 558