One store, many surfaces
We build AI on top of data you own. Agents pull your sources into one store, automate the work on it, and deliver to whatever the job needs: reports, dashboards, alerts, or a product people log into. Those outputs feed back in, so every cycle makes the store richer.
INGEST ANYTHING
over API · MCP
over API · MCP
FEED ANY SURFACE
Most AI projects bolt one more tool onto the pile
This is the opposite. The AI we build reads and writes to one store you own, so it never gets trapped in a vendor's dashboard. The product is still the agent, report, or tool you came for. Owning the data underneath is what makes each new one cost less than the last.
It's the same architecture whether the source data is AI visibility, job bookings, patient referrals, supplier feeds, or CRM activity. Below is how it stacks up.
A closed loop, not a pipeline
Agents pull your sources in, automate the work on the store, and deliver to every surface. Then results and new data feed back in, so the store gets richer every cycle. Tap the agents, the store, or an output to see what each does.
INGEST ANYTHING
over API · MCP
over API · MCP
FEED ANY SURFACE
AI agents (the engine)
Agents run the whole loop. They pull data into the store, automate the work on it, and push results out to every surface. Built in whatever fits your stack: Copilot Studio, ChatGPT agents, the Claude Agent SDK, n8n, or fully custom. Every run is logged with its cost, model, and what went in and out.
Tap the agents, the store, or any output to see what each does.
Four layers, each one built on the last
Data at the bottom. Agents run the work on top of it. Outputs on top of the agents, whatever the job needs. Every layer reads from the one below it.
Ingest anything, own the data
Your data lives all over the place. Vendor APIs, analytics platforms, documents, call transcripts, spreadsheets, whatever your team types in by hand. We pull it into one database you own and can query directly. The vendor tools underneath stay swappable, so the data outlives any of them.
Agents run the work
Agents are the engine. They pull data into the store, automate the recurring work on it, and push results out. Each one owns a job. Some run on a schedule, some on demand. Every run logs to a single spine with its cost, the model it used, and what went in and out. Anything that leaves the building passes a human approval gate first. Built in whatever fits your stack: Copilot Studio, ChatGPT agents, the Claude Agent SDK, n8n, or fully custom.
Feed any surface from the same store
The store feeds whatever the job needs. Live dashboards and scheduled reports. Alerts when something crosses a line. A product your clients log into. Results pushed back to the tools you already run, over their APIs or MCP. Same store underneath all of it.
Every new thing compounds
Because the data is owned and structured, every new report, agent, or screen is just another read off the same store rather than a fresh integration. The next capability costs less than the last, and it keeps going that way.
Because the data is owned and structured, the marginal cost of the next capability keeps falling while the vendor tools underneath stay swappable.
Most businesses have data trapped in vendor dashboards, work happening by hand in Slack and spreadsheets, and reporting stitched together every month. This turns all three into one system they own. The data outlives the vendor. The work runs itself with an audit trail. The client watches their own progress.
What transplants, and what's bespoke
The architecture is the same every time. The content is what we build around your business. That split is why the second client costs less to serve than the first.
- ✓The owned data store you can query directly
- ✓Agents as the engine, every run logged with its cost, model, inputs and outputs
- ✓Human approval gates on anything that leaves the building
- ✓The surfaces it feeds: dashboards, reports, alerts, a client product
- ●Which sources get pulled in, and how they get normalised
- ●Which jobs the agents own, and how often they run
- ●The metrics, story, and actions that matter to that business
- ●Which surfaces it feeds, from live dashboards to a client product
Same pattern, any industry
We've pointed this architecture at very different source data. The layers don't change. Only what flows through them does.
Does this fit your business?
The pattern pays off fastest when these are true. If a couple of them sound like you, it's worth a conversation.
Your data is trapped in vendor dashboards
You can see it, but you can't own it, query it, or keep it when you switch tools.
The work lives in Slack and spreadsheets
Real work gets done, but there's no record of it and nothing runs on its own.
Reporting gets assembled by hand every month
Someone stitches the same numbers together again instead of reading them off one place.
Book a free consultation
30 minutes on a call. We map your data sources, the work you're doing by hand, and what a first owned store could feed. Neil takes the discovery calls himself, so you'll be talking to the person who'd scope the build.