Scattered across systems.
Reports, scans, photos and logs sit in shared drives, inboxes and old systems.
The data behind
every answer.
From where your data lives, without migrating systems first. Every value tied to its source. Never used to train models for anyone else.
Wherever the work is.
Hosted
Private cloud
On-premise
Edge
Air-gapped
What we do with data.
We gather, structure, check and label your data where it lives, and keep it flowing into the systems you use.
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Collection
We gather documents, databases, sensors, field reports, video and audio from wherever they live, without migrating your systems first.
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Extraction
We turn reports, logs, drawings, forms and scans into structured data, each value tied to where it came from.
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Processing
We remove duplicates, standardise units and formats, and match the same asset, supplier or customer across systems.
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Labelling
People who know your industry label the examples your AI learns from, and we create realistic extra examples where real ones are rare.
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Pipelines
We build and run the connections that keep data moving into the systems you use, and fix them when a source changes.
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Analysis
We find the patterns, anomalies and trends that matter, and show the records behind every finding.
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Reporting
Dashboards and reports built for the people who act on them, from the plant floor to the board pack.
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Governance
Who can see what, where every number came from, and sensitive details hidden, with every step recorded.
Most AI projects stall before the model.
The data is scattered, inconsistent and unlabelled, and nobody knows how much of it is usable.
Messy and repeated.
The same record appears in different formats, with gaps and duplicates.
No trail behind a number.
When a director asks where a figure came from, someone has to rebuild it.
Measured, not assumed.
Before any model trains.
Every source is assessed and every gap named before anything is labelled or trained. Here, a supplier master: ready for most records, and a person decides the rest.
Matched. Two duplicate supplier pairs, one record kept for each.
Conflict. Payment terms to be confirmed with the contract owner.
Held. 38 records with no VAT number, sent to a person.
Proposed, not applied: a person decides which record is correct before any change is made. Illustrative example. Systems, records and figures are fictional.
Built inside your controls.
Who can see what, every step recorded, and sensitive details hidden. Everything runs inside your boundary.
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Who can see what.
Role-based access controls govern who configures workflows, accesses data and reviews outputs. Every access event is logged.
Security & trust -
Inside your boundary.
Source data, extracted records and processing logs stay in the environment you control, from cloud to fully air-gapped.
Where it runs
Where it runs.
Source data, extracted records and processing logs stay where you choose, from hosted to air-gapped.
A managed environment.
Start with a hosted service when speed of adoption and managed operations are priorities. Agree data handling, access and service requirements as part of the engagement.
Discuss hostedIn the cloud you use.
Deploy dedicated instances in your AWS, Azure, Google or Oracle account. Align the architecture with your cloud standards, operational responsibilities and integration requirements.
Discuss public cloudWithin your enterprise network.
Run it inside your own private cloud or VPC, with network access, storage and controls set around your boundary.
Discuss private cloudOn your infrastructure.
Run software, storage and local inference on your own servers. Plan capacity, integration, maintenance and support around the environment your team operates.
Discuss on-premiseClose to the operation.
Place intelligence near industrial sites and operational equipment, where latency and connectivity shape the design. Match the workload to the local compute available.
Discuss edgeWithout constant connectivity.
Operate local workflows without a continuous internet connection. Define how data is transferred, how updates are applied, and what must remain available offline.
Discuss offlineA physically isolated environment.
Plan for an installation with no external network connection. Local models, dependencies, data transfer and maintenance all become part of the deployment design.
Discuss air-gappedPut it to work.
Data feeds the rest of what we build. Each of these is built on the same tested foundations.
Models
Specialist models, evaluated on your data and run inside your boundary.
Explore Models →Agents
Agents that run on their own and hand over to a person at the decisions that matter.
Explore Agents →Products
Ready-made products for recurring jobs, from documents and contracts to sustainability data.
Explore Products →APIs
Model, agent and data APIs that plug into the systems you already run.
Explore APIs →Evaluations
Tests built from your own cases, rerun every time a model changes, so you always know how well it does your work.
Explore Evaluations →Bring us the work where wrong is expensive.
Start with the operational problem. We’ll work out the data, models and agents it needs, and where it should run.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India