# The data behind every answer · Dscvry > Data collected, extracted, matched and labelled from where it lives, with every value tied to its source and kept inside your boundary. Source: https://dscvryai.com/technology/data/ # 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. What we do with data · How it is governed · 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. - ### Collection We gather documents, databases, sensors, field reports, video and audio from wherever they live, without migrating your systems first. - ### Extraction We turn reports, logs, drawings, forms and scans into structured data, each value tied to where it came from. - ### Processing We remove duplicates, standardise units and formats, and match the same asset, supplier or customer across systems. - ### Labelling People who know your industry label the examples your AI learns from, and we create realistic extra examples where real ones are rare. - ### Pipelines We build and run the connections that keep data moving into the systems you use, and fix them when a source changes. - ### Analysis We find the patterns, anomalies and trends that matter, and show the records behind every finding. - ### Reporting Dashboards and reports built for the people who act on them, from the plant floor to the board pack. - ### 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. ### Scattered across systems. Reports, scans, photos and logs sit in shared drives, inboxes and old systems. ### 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. - ### Who can see what. Role-based access controls govern who configures workflows, accesses data and reviews outputs. Every access event is logged. [Security & trust](https://dscvryai.com/security/) - ### Sensitive details hidden. Sensitive details are hidden from the people and systems that don’t need them, and every step is recorded. [Sensitive data](https://dscvryai.com/use-cases/sensitive-data/) - ### 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 data work helps. The same data work answers other questions too. Can we trust these numbers? ### Data quality Find duplicates, gaps and records that disagree across systems. See how → https://dscvryai.com/use-cases/data-quality/ Is our data ready for AI? ### Data readiness Collect, clean and label it so models can learn from it. See how → https://dscvryai.com/use-cases/data-readiness/ Why does month-end reporting take a week? ### Reporting Pull the figures together automatically, every number traced to its source. See how → https://dscvryai.com/use-cases/reporting/ How did the North region do last quarter? ### Ask your data Plain-language answers from your own databases, within each person’s access. See how → https://dscvryai.com/use-cases/ask-your-data/ ## 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 hosted · ## Put 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 → https://dscvryai.com/technology/models/ ### Agents Agents that run on their own and hand over to a person at the decisions that matter. Explore Agents → https://dscvryai.com/technology/agents/ ### Products Ready-made products for recurring jobs, from documents and contracts to sustainability data. Explore Products → https://dscvryai.com/technology/products/ ### APIs Model, agent and data APIs that plug into the systems you already run. Explore APIs → https://dscvryai.com/technology/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 → https://dscvryai.com/technology/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 →](https://dscvryai.com/contact/) **London**Discovery AI Limited · United Kingdom **Hyderabad**ReDscvry Technology Private Limited · India **Chennai**ReDscvry Technology Private Limited · India