Most AI projects die in the data plumbing. We start past it.
Backbone arrives with a single, unified data model already in place. That is the part most AI projects never get past, and on Backbone it is behind you before the project starts.
Nothing to do with the models. Everything to do with integration.
The reason artificial intelligence stalls in most organisations has nothing to do with whether the models are good enough. It is that the information they would need sits across too many systems and spreadsheets, and none of it agrees with the others. The work disappears into integration long before anyone sees a useful answer, and that is true whichever model you point at the problem.
Three tiers, each resting on the one beneath it.
One unified data store
A single coherent picture of your business that can be queried directly. Everything above depends on it, and it comes as standard.
Live insight and reporting
Answers on demand instead of reports assembled by hand. Forward-looking signals rather than a retrospective at month end.
Agents that do the work
Routine tasks carried out inside Backbone the way your team would: chasing, checking, reconciling, drafting, escalating. With clear limits on what runs unsupervised, and a full audit trail.
Each layer rests on the one beneath it. Without the data store the rest is guesswork, which is why so many AI projects stall before they produce anything.
Everything, in one model.
One data model behind every function, rather than four versions of the truth that quietly disagree. What goes in is broad on purpose, because the value sits in the questions it lets you ask once it is all in one place.
What goes in
- Operations: projects, sites, people, cost, compliance, variations.
- Support: ticket volume and resolution times; uptime; device and licence estate; patch and vulnerability posture; connectivity performance; IT cost per site and per head.
- Create: campaign spend and performance; web and portal analytics; conversion; SEO visibility; lead source and attribution.
- Development: what was built, when, and against which requirement.
What that makes answerable
Cost per qualified lead, set against the delivered margin on the projects those leads became.
Downtime and ticket load per site, set against that site's schedule slippage.
Security and compliance posture, set against the contractual obligations on live projects.
Licence, device and support cost, landing on the project cost lines that consume them.
This is not a dashboard exercise.
Bolting a reporting tool onto six systems and four spreadsheets does not produce live insight. It produces a seventh place that also needs reconciling, staffed by someone whose job becomes making the numbers agree before anyone can trust them. What makes the questions above answerable is not a cleverer dashboard sitting on top. It is that the data model was unified first, so there is nothing left to reconcile by the time a question gets asked.
The ratio worth naming.
Ask most organisations how their reporting time actually splits, and the honest answer is uncomfortable.
Most organisations spend the majority of their reporting effort collecting and reconciling data, and only a fraction of it deciding what to do. Inverting that ratio is the point.
What the agents can do, and who answers for it.
Almost nobody publishes this. We think you should be able to see it before you commit to anything, not discover it once something has already gone wrong.
| Question | Position |
|---|---|
| What runs unsupervised | Routine, reversible work with a fixed scope: chasing, checking, reconciling, drafting for a person to send. Nothing that commits money, changes a contractual position, or writes to a live customer record without a person confirming it first. |
| What requires human approval | Anything outside that fixed scope, and anything the agent itself flags as uncertain. Approval sits with a named person inside your organisation, not with us. |
| How every action is logged | Every action an agent takes is written to an audit trail you can read: what it did, when, against which data, and on whose approval. |
| Who is accountable when an agent gets it wrong |
Placeholder: Agent authority and liability position, in writing
What will go here once the content is supplied. |
Questions we get asked
Where does our data live?
Inside your own instance of Backbone, on infrastructure we run for you. It is not pooled with other clients' data, and the unified model that makes reporting and agents possible is built on top of that same instance rather than a separate copy somewhere else.
Can we get it out?
Yes. Your data is yours, and a full export in a usable format is available on request at any time, including if you ever decide to leave the platform. Nothing about how Backbone works depends on that data being hard to get out.
What if we already have a BI tool?
Point it at Backbone's unified data instead of the separate systems it currently has to reconcile by hand. The tool you already know how to use gets more reliable answers to work with; you are not obliged to replace it with ours.