Looking backwards.
Reports say what happened last month, not what is about to happen.
Know what comes next,
and plan for it.
Predict, forecast, simulate and plan from your own records, with how sure it is stated every time.
Wherever the work is.
Hosted
Private cloud
On-premise
Edge
Air-gapped
Most surprises
were in the data.
Failures, shortages and missed deadlines usually announce themselves weeks ahead. The signs are spread across records nobody lines up, and by the time someone notices, it's too late to be cheap.
Single numbers.
A forecast without a range hides how uncertain it is, so people either trust it too much or not at all.
Patterns missed.
The same cause behind different incidents goes unnoticed because each is handled alone.
What the AI
can anticipate.
Simple where it works.
Honest about the range.
Many forecasts are best made by simple statistics on good history. The AI uses more complex models only where they measurably do better, and always shows the range.
Clean historyCheck
Checked, structured records from the rest of the system.
BaselinesRules & code
Seasonality, trend and usage rates.
ModelsPrediction
Only where they beat the baseline on held-back data.
RangesPrediction
How sure, not just how much.
WarningsOperate
Raised with enough time to act.
Likely short before
the delivery lands.
Tank T-14 · antifreeze · level forecast. A forecast with its range. People decide what to do about it.
History. 90 days of daily levels and 6 deliveries.
Plan. Winter use starts in 9 days; last year's use rose 40% in the first fortnight.
Supply. Next delivery confirmed for day 16.
Forecast. Below minimum in about 11 days; likely between day 8 and day 15.
Illustrative example. Site, tank and figures are fictional.
Built to be
trusted, carefully.
A forecast is only useful if people know how far to trust it.
-
Always a range.
Every forecast shows how sure it is, not just a single number.
Stock Shortages -
Tested before it's used.
Models are compared with simple baselines on data they haven't seen.
How Check works -
Explains the drivers.
Each warning says which records and events moved the forecast.
How Understand works
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-gappedThe other capabilities.
Each one is tested on your own work before you rely on it.
Sense
It sees, hears and reads the work as it happens.
Explore Sense →Understand
From piles of paper to records you can use.
Explore Understand →Check
Every line compared. Only the gaps reach you.
Explore Check →Investigate
Follow the evidence across systems. Find out what happened, and why.
Explore Investigate →Operate
The next step, taken. The reason, recorded.
Explore Operate →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