Where the intelligence actually sits. Some data arrives clean and goes straight in. Most of it needs a model before it is worth storing at all.
Finance, stock, procurement
Process and production
Fleet, sensors, energy
Assay and test results
Invoices, contracts, forms
Cameras, drone, inspection
Clean, fill, flag drift
Extract, validate, score
Detect, classify, measure
Built on your data
Straight through, logged
Reason, decide and act
Live, board to shop floor
Finance, stock, procurement
Process and production
Fleet, sensors, energyAI
Assay and test results
Invoices, contracts, formsAI
Cameras, drone, inspectionAI
Clean, fill, flag drift
Extract, validate, score
Detect, classify, measure
Built on your data
Straight through, logged
Reason, decide and act
Live, board to shop floor
What the AI actually does.
Also in use: computer vision for PPE compliance, asset and conveyor monitoring.
Foundational AI, inside your business.
Not a pilot we run for you. A capability your teams own, built layer by layer on the data asset underneath.
We stand each layer up with your team, then hand it over.
Let the routine run itself.
AI in a complex business is a data problem before it is an AI problem. We fix the data problem. Then we build the AI that runs on it.
Not a portfolio. Proof.
The foundation, three years on.
A structural steel and platework contractor built its monthly reporting by hand. We replaced the whole path (integration, ingestion, warehouse, reporting) and still run it.
Key resultThree years in continuous production use.Delivered · managed service Financial servicesMonth-end, automated.
A group of legal entities consolidated its accounts by hand, days after close. We built the hub straight against the accounting API, with an AI layer on top.
Key resultConsolidation automated across every entity.Delivered · in active use MiningVision on the conveyor.
Material on the feed was being estimated by eye. Vision models now measure it frame by frame, landing beside every other operational source.
Key resultVision output reports like any other data source.In deployment
Straight answers.
What happens after you build it?
We don't hand over a system and disappear. Dataloop stays on as the managed service provider. The same team that built the integration keeps it running, watches for drift in the data, and extends it as your systems and requirements change.
How fast can we see something working?
Depending on scope, weeks rather than quarters. Delivery follows a set process (baseline, build, review cadence), the same discipline whether it's an integration, a report, or an AI agent. You'll see something real running on your own data early, not a slide deck first.
How do you charge?
There's no published rate card, because the answer depends on the systems involved and what needs to be built. Tell us what's not working and we'll scope it honestly, including telling you if the fix is smaller, or different, than what you came in asking for.
Do we have to replace our existing systems?
No. We build the integration and data layer around the systems you already run (ERP, plant and SCADA systems, accounting platforms) and layer reporting and AI on top. Nothing gets ripped out for the platform to work.
What happens to our data?
It stays on Microsoft Azure, under your own governance model. That's the same platform behind every engagement we run as a Microsoft Solutions Partner.
Does the AI just make things up?
No. Agents answer from your own warehouse, grounded in your data, not the open internet. Every answer traces back to a live source system, the same one your team already trusts for reporting.
See it running on your data.
A short walkthrough, your systems, no slideware.