# Ask your data. Check the answer · Clarify · Dscvry > Clarify (formerly DotPlot) lets people ask questions of enterprise data in everyday language and get answers with their sources, within the access they already have. Source: https://dscvryai.com/technology/products/clarify/ # Ask your data. Check the answer. In everyday language. Within your permissions. With its sources. How Clarify answers · How it works · Wherever the work is. Hosted Private cloud On-premise Edge Air-gapped ## Insight locked in dashboards. The answers exist in the data, but getting them means a dashboard only analysts can use, or a request that waits days in a queue. ### Analyst bottleneck. Every new question becomes a request, and the request waits. ### Dashboards answer yesterday's questions. They show what someone decided to chart, not what you need to know now. ### Access gets in the way. Opening the data to more people risks showing them what they shouldn't see. ## How Clarify answers. Questions in your own words. ### Ask People ask what they need to know in everyday language, and follow up the way they would with a colleague. See it in stock shortages → https://dscvryai.com/use-cases/stock-shortages/ Answers within your access. ### Permissions Governance-aware access controls mean an answer only draws on data the person asking is allowed to see. See it in contract compliance → https://dscvryai.com/use-cases/contract-compliance/ Every number, traceable. ### Sources Each answer shows the rows, filters and tables behind it, so it can be checked rather than trusted. See it in revenue leakage → https://dscvryai.com/use-cases/revenue-leakage/ Hours, not days. ### Beyond dashboards Business users get answers directly, instead of waiting for an analyst to build or change a dashboard. See it in maintenance planning → https://dscvryai.com/use-cases/maintenance-planning/ ## Language on top. Exact queries underneath. The question is understood by a model; the answer comes from exact queries against governed data, so the numbers are computed, not written. Question Understand Asked in everyday language. Access check Rules & code Only data this person may see. Query Rules & code Exact queries against the data. Answer Reasoning Written from the query results. Sources Rules & code Rows, filters and tables attached. ## A number you can open and check. Question · late open orders, EU. The model understands the question. The data answers it. **Asked.** Which open orders ship late against the promised date? **Checked.** Order book and shipments allowed; price lists skipped for this user. **Queried.** Open orders where the projected ship date is after the promised date. **Answered.** 214 orders, most in AW26 for the EU, with the list attached. Illustrative example. Orders, seasons and figures are fictional. ## Real work of this kind. Engagements where questions were answered directly from the source records. - ### Apparel · South Asia, global. Order-book questions answered in under 60 seconds, previously ~3 days. [Financial control](https://dscvryai.com/solutions/finance/) - ### Capital programmes · UK. Answers to project-controls questions, linked to source records. [The full record](https://dscvryai.com/solutions/) - ### Within your boundary. Runs in your environment, from managed cloud to air-gapped. [Security & trust](https://dscvryai.com/security/) ## Where it shows up. Problems that start with a question nobody can answer fast. What runs out before the next delivery? ### Stock shortages Anticipate shortages before they become a problem. See how → https://dscvryai.com/use-cases/stock-shortages/ Did they deliver what the contract says? ### Contract compliance See whether obligations were actually met. See how → https://dscvryai.com/use-cases/contract-compliance/ What does this new rule change for us? ### Regulatory changes Map new rules to what the business must change. See how → https://dscvryai.com/use-cases/regulatory-changes/ What should we have charged? ### Revenue leakage Find money that should have been charged. See how → https://dscvryai.com/use-cases/revenue-leakage/ ## Where it runs. The AI goes to where the work is, 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 them to work. Each one is tested on your own work before you rely on it. ### 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/ ### Data Data collected, extracted and governed, with every value traced to its source. Explore Data → https://dscvryai.com/technology/data/ ### 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. 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