Analyst bottleneck.
Every new question becomes a request, and the request waits.
Ask your data.
Check the answer.
In everyday language.
Within your permissions.
With its sources.
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.
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.
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.
QuestionUnderstand
Asked in everyday language.
Access checkRules & code
Only data this person may see.
QueryRules & code
Exact queries against the data.
AnswerReasoning
Written from the query results.
SourcesRules & 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 -
Capital programmes · UK.
Answers to project-controls questions, linked to source records.
The full record -
Within your boundary.
Runs in your environment, from managed cloud to air-gapped.
Security & trust
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 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 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 →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 →Data
Data collected, extracted and governed, with every value traced to its source.
Explore 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 →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