Built around your business. Working worldwide.

AI Integration and Automation

AI integration helps your business process documents, find internal information and handle repetitive requests within the systems you already use. We build and test the integration, with human review, cost controls and a documented handover.

Based in London. Available for remote projects worldwide.

Built around your business. Working worldwide.
01OpenAI · Anthropic · Google AI
02RAG & knowledge search
03Chatbots & assistants
Provider-agnosticOpenAI, Anthropic, Google
Fits your stackWeb, desktop, or server
Cost-awareCaching and usage caps
You own itFull source, with docs
01

AI integration for everyday business workflows

Define the workflow, the systems involved and what a successful result should look like before choosing models or tools.

Turn documents into usable business data

Extract and classify information from invoices, forms, PDFs or emails. Validate the fields, route uncertain results for review and prepare structured records for your existing systems.

  1. Incoming invoice
  2. Extracted fields
  3. Approval
  4. Accounting record

Make internal knowledge easier to find

Build an assistant over approved company information. Retrieval and source references help staff check answers, with access rules designed around the information they are allowed to see.

  1. Staff question
  2. Permitted sources
  3. Draft answer with references

Handle repetitive requests with a review step

Classify incoming messages, suggest the next action and prepare responses or tasks. Keep sensitive decisions and unusual cases with your team.

  1. Support email
  2. Classification
  3. Draft
  4. Human approval
02

Start with a scoped AI integration pilot

A scoped pilot gives you something concrete to evaluate before a broader rollout. Agree representative inputs, acceptance criteria and delivery boundaries at the start.

WorkflowOne defined business task, with named source and target systems.
Inputs and evaluationSample inputs, expected results and a repeatable evaluation set.
IntegrationA focused implementation with validation, review and exception handling.
Operating costsVisibility into model usage, limits and running-cost assumptions.
HandoverSource code, documentation, known limitations and recommendations for rollout.

Price and schedule follow a review of your workflow and are agreed before development begins.

Discuss your workflow
03

AI integration example: invoice processing with human review

An invoice arrives by email. Instead of retyping its details, your team could receive a draft accounting entry, with unclear information flagged for a person to check.

  1. Read the invoice

    The integration reads the attached invoice and extracts the supplier, invoice number and total.

  2. Check anything unclear

    If a purchase order number is hard to read, the invoice is held for review. A member of your team checks the original and corrects the field.

  3. Create an accounting draft

    After review, the checked details are sent to your accounting software as a draft entry. Payment approval stays with your team.

The goal: less manual data entry, with people still responsible for checking exceptions and approving payments.

Illustrative workflow, not a live integration. Your accounting system and approval rules would be agreed during scoping.

04

Our AI integration stack

We add AI integration end to end, from a single chatbot to multi-provider pipelines. A selection of what we deliver:

LLM & chat integration

We connect OpenAI, Anthropic, Google AI, or open models to your app with clean, maintainable code.

Prompt engineering

We design, test, and version prompts so outputs stay accurate, on-brand, and reliable.

RAG & knowledge search

We ground answers in your own data with retrieval-augmented generation and vector search.

Chatbots & assistants

Customer-facing or internal assistants with streaming replies and conversation memory.

Monitoring & cost control

Usage dashboards, token and cost tracking, rate limiting, and caching to keep spend predictable.

Security & guardrails

Input validation, output filtering, and data-privacy controls to keep your AI features safe.

05

Our Process

1

Discovery & use cases

We map where AI adds real value, define success metrics, and choose the right providers and approach for your use case.

2

Solution design & provider selection

We design the integration architecture, select models, and plan for cost, latency, and data privacy from the start.

3

Integration & prompt engineering

We build the integration, engineer and test prompts, and wire in retrieval, streaming, and structured outputs.

4

Testing & evaluation

We evaluate quality with regression and benchmark tests, then harden error handling, rate limiting, and caching.

5

Deployment & monitoring

We deploy to production, set up usage and cost monitoring, hand over documentation, and provide 30 days of bug fixes.

06

London based. Built for remote collaboration.

Your business does not need to be in the UK. Agree the practical delivery arrangements before starting.

Agree the working rhythm

Confirm time-zone overlap, communication channels, review meetings and who approves decisions.

Make ownership clear

Set out custom-code ownership, third-party dependencies, access requirements and handover responsibilities.

Define commercial terms

Agree scope, milestones, invoicing currency and payment terms in the quote.

Plan ongoing operation

Clarify who manages accounts, hosting, usage costs and support after handover.

07

What would you like to automate?

Describe one workflow and the systems involved. We can discuss feasibility, scope and a practical starting point.

Please do not include credentials, customer records or confidential documents. You can request an NDA before sharing sensitive information.

256-bit SSL encrypted Your data stays private NDA available
08

F.A.Q About AI Integration

What is AI integration?
AI integration means adding capabilities like chat, summarisation, classification, or retrieval to your software using large language models and other AI services, wired into your app with reliable, production-ready code.
Which AI providers do you work with?
We work with all the major providers, including OpenAI, Anthropic (Claude), and Google AI, as well as open models. We are provider-agnostic and recommend the best fit for your use case, budget, and data requirements.
Can you add AI to our existing application?
Yes. Most of our work is adding AI integration to existing web, desktop, and server applications. We assess your current stack and integrate cleanly without rebuilding what already works.
What can AI realistically do for our business?
Common wins include customer-support assistants, document and email summarisation, search over your own knowledge base, content drafting, data extraction, and classification. We focus on use cases with clear value rather than novelty.
What is RAG and do we need it?
Retrieval-augmented generation (RAG) grounds the AI in your own documents and data so answers are accurate and specific to your business. If you need the AI to answer from your content rather than general knowledge, RAG is usually the right approach.
How do you control AI costs?
We design for cost from the start: choosing right-sized models, caching responses, setting usage and rate limits, and monitoring token spend so you get predictable, controllable bills.
How do you keep our data private and secure?
We use providers and configurations that do not train on your data, add input validation and output filtering, and apply sensible access controls. Where needed we can discuss self-hosted or open models for sensitive data.
How accurate and reliable are the results?
We engineer and test prompts, add guardrails, and where appropriate use retrieval to ground responses. We also set up evaluation so quality is measured, not assumed, before anything goes live.
How long does an AI integration project take?
It depends on scope. A focused integration can be quick, while multi-provider or RAG systems take longer. After an initial consultation we give you a realistic timeline with clear milestones before any work begins.
Will we own the code?
Yes. On final payment you own all the custom code and intellectual property we build for you, delivered with full source and documentation.
Do you provide ongoing support?
Every engagement includes 30 days of bug fixes after delivery. Beyond that, we offer ongoing support, monitoring, and enhancements on a package or per-project basis, which matters as AI models evolve.
Can you work with us outside the UK?
Yes. We work remotely with businesses worldwide. We agree communication, time-zone overlap, invoicing and handover arrangements before starting.
09

Related reading

06

Prefer to Talk First?

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A clear scope and accountable delivery.