Keep what already works
An existing product or connector may cover the need. We assess that before recommending a new database, custom integration or interface.
Data integration & infrastructure
Your AI is ready. Is your business data?
You have useful information arriving in files, living in databases or sitting inside the tools your team already uses. We help connect it to AI through a data foundation you can operate and trust.
Perhaps you have worked with an AI assistant on a plan. Before spending more time building it, you need to know whether the approach fits your business and will work in practice. We assess the plan, keep what makes sense, change what does not, and implement the agreed system.
Discuss your project →From requirement to working system
What we work through with you.
An agreed data flow, implemented and tested, with documentation of where information lives, how it is updated and who operates it. We start with your existing systems and add the pieces the work needs.
Map the information that matters
Choose a specific task for AI to support. Identify its sources, owners, formats and refresh needs. Decide which records and fields it may access, and what should stay outside the application.
Connect and validate the incoming data
Configure provider integrations or build ingestion where needed. Agree how to handle missing fields, duplicate deliveries, changed formats and failed jobs, so a problem can be found and corrected.
Set up storage and operations
Choose suitable databases and file storage. Scope permissions, backup and recovery arrangements, scheduled jobs and monitoring alongside the data model. Record which responsibilities belong to your team, us and the hosting provider.
Make the data available to AI
Assess existing connectors, APIs, command-line tools or MCP interfaces against the application. Test what an agent can retrieve or change, with permissions and human review suited to the work.
The choices behind the implementation
Make the tradeoffs clear.
Start with one useful flow
You do not need to reorganize every piece of company information before using AI. Choose a bounded source and task, then expand when the result supports it.
Agree how we will test it
Use representative records and known expected results. Acceptance checks can cover import completeness, duplicate handling, access restrictions and recovery, depending on scope.
Client implementation / 101
From an AI-assisted plan to a working system.
Ryan Cockerill of 101 Net Lease brought a plan he had developed with Claude. We assessed it against his business needs, kept the sound parts and changed the parts that did not fit. We then built the Azure data foundation: private export delivery, validation, SQL ingestion and operational alerts.
Explore the 101 implementation →Delivered data foundation
- 01Exports arrive in private Azure Blob Storage
- 02Automatic validation and import into Azure SQL
- 03Monitoring and alerts, with a foundation for agents
Live scope: export delivery, validation, import, relational storage and operational alerts.
Before you commit
Questions worth working through.
Can you connect AI to our existing database?
Yes, where the database, access rules and chosen AI application support the integration. We assess the existing connection options first. Agent access can be limited to approved operations; it does not have to mean unrestricted access to the database.
Do we need a vector database or a new model?
Not necessarily. Structured records may be best served by a conventional database and defined queries. Document retrieval may call for a different approach. We choose around the information and task rather than requiring a particular architecture.
Will we control the accounts and data?
We agree account ownership, access, custom deliverable rights and handover in the engagement. Hosting services, models and other third-party software keep their own licenses and terms.
What do you need from us to start?
A description of the business task, where the data comes from and what is currently getting in the way. A sanitized sample or field list can help after we agree how to share it. You do not need to send confidential records in the enquiry form.
Go deeper: How to prepare business data for AI agents →
A concrete next step
Tell us what data you want AI to work with.
We agree the scope, deliverables, timing and fees before work begins. A focused configuration or integration request does not automatically require a full custom build.