AI integration for the systems you already run on.
Most businesses don't need another AI tool. They need AI wired into the CRM, inbox, accounting package and spreadsheets they already use — with real permissions, real logging, and a clear line between what it can do and what a person signs off.
We do that integration work for companies in Ireland and the UK. Scoped and quoted per project after a free discovery call.
Why integration is the hard part
A model that can write a beautiful reply is worthless if it can't see the customer's order history, and dangerous if it can update that order without rules. The interesting work isn't the prompt — it's the authentication, the data mapping, the rate limits, the retries, the permissions and the escalation paths.
That's also why most internal AI pilots stall. They demo well against a copied spreadsheet and then never touch a live system. We start from the live system.
What we connect
Systems of record
HubSpot, Pipedrive, Salesforce, Dynamics, Zoho, plus custom CRMs and internal databases over Postgres, MySQL or SQL Server.
Communication
Gmail and Outlook, Slack and Teams, WhatsApp Business, SMS, plus voice through the telephony provider you already use.
Finance and commerce
Xero, QuickBooks, Sage, Stripe, Shopify, WooCommerce — reading transactions, writing records, reconciling both directions.
Documents and files
Google Drive, SharePoint, Dropbox, Notion, plus PDF and scanned-document extraction with confidence scoring.
Spreadsheets and data
Google Sheets, Excel, Airtable — usually where the real business logic is hiding, and usually the fastest thing to integrate.
Scheduling and operations
Google Calendar, Outlook Calendar, Calendly, job management and field service tools, ticketing systems.
How we approach it
1. Audit the integration surface
What systems exist, what APIs they expose, who owns credentials, where the data is dirty, and which of those is going to be the hard one. We do this before quoting a build.
2. Design the boundaries
Exactly what the AI can read, what it can write, and what it must hand to a human. Written down and agreed before it's built — this is what makes AI safe to deploy internally.
3. Build and instrument
Integration built with least-privilege access, retries, and logging of every read and write, so when something looks wrong you can see precisely what happened.
4. Pilot, then hand over
Run it alongside the existing process first. Once the output is trusted, full rollout with documentation and access to the code.
AI agent development
Where a workflow needs judgement rather than a fixed rule, we build an agent: a component with a defined goal, access to a specific set of your tools, memory of the case it's working, and hard limits on what it may do unsupervised.
Typical examples are an agent that handles an entire inbound enquiry thread until it needs a price approved, one that reconciles supplier invoices and only raises the mismatches, or one that keeps CRM records accurate by cross-checking them against your other systems every night.
Security and governance
- Least-privilege credentials per integration
- EU/UK-region model endpoints where available
- Full read/write audit logging
- Data stays in your own accounts
- DPA signed on request
- Documented human escalation paths
Common questions
What are AI integration services?
Connecting AI models and agents to the systems a business already runs on, so the AI can read real data and take real actions instead of sitting in a separate chat window. In practice it's API work, data plumbing, permissions and guardrails — the unglamorous part that decides whether AI is useful or a demo.
What is AI agent development?
Building an AI component that can decide as well as generate: it has a goal, access to specific tools such as your CRM or calendar, memory of the conversation or case, and rules about what it must escalate to a human. That's different from a chatbot, which only answers.
Can you integrate with our internal or legacy system?
If it has an API, a database we can reach, or even a scheduled file export, usually yes. If it has none of those, we'll tell you on the discovery call rather than take the project and discover it later.
How do you keep our data safe?
Data stays in your own accounts. Each integration gets least-privilege credentials scoped to only what it needs, model endpoints in the EU or UK region where available, full logging of what was read and written, and a DPA on request.
Which AI models do you use?
Whichever fits the job — OpenAI, Anthropic Claude and Google Gemini are the usual choices, and we'll use a smaller, cheaper model where it performs as well. Model choice is an implementation detail we own; you shouldn't be locked to one vendor.
How is this priced?
Per project, quoted in writing after a free discovery call. The scope drivers are the number of systems, how clean their data is, and how much exception handling the integration needs.