01Automate
Automate the clear cases
Routine work proceeds under the policies you define.
The decision layer for AI
Make reliable decisions with AI.
AI errors are costly. So is checking every output. Themis shows which predictions need attention.
A stream of predictions, each with its next step · ŷ prediction · σ Capsa’s uncertainty estimate
[ 01 ] The trade-off
Every AI output is a bet. Checking all of them is a cost.
AI errors are costly, and so is checking every output. The question is not whether to automate — it is which decisions to trust.
Themis finds the right point — for each decision, not once for the whole system.
Automate everything and errors reach operations. Review everything and the cost moves to your experts. Total cost is lowest in between — and the right point differs from one decision to the next.
Fig. 1 — Total cost = errors acted on + review effort. Scroll to move the operating point.
[ 02 ] The decision layer
A decision layer to guide AI actions.
Themis connects each output with evidence and operating context, and gives it a next step before it is acted on. Pick a case to see where it goes.
Example workflow — four cases
Output 02 of 04
The output matches the evidence, and the input resembles familiar cases.
→ ProceedThe action continues.
The output contradicts a relevant record.
→ VerifyMore evidence is gathered before acting.
The input is unfamiliar — but another model or tool is a better fit.
→ RerouteThe case moves to that model or tool.
Evidence is still inconclusive, and an error here would be costly.
→ EscalateA human expert reviews it.
Agreement with evidencelow
Input familiarityhigh
Cost of an errormedium
[ 03 ] What changes
Automation where it is safe. Attention where it is needed.
02Review
Spend review where it counts
Experts and checks go to the decisions that need them.
03Evidence
Put evidence where it matters
Decide when another document, simulation or experiment is worth its cost.
Next step · Talk to the team
Bring one repeated decision. We'll show you where Themis fits.
[ 04 ] Applications
Reliable decisions across engineering and operations.
Engineering & simulation
Simulate where it changes the answer.
Use predictions to move engineering work forward, and focus high-fidelity simulation, experiments and expert review where they improve the decision.
Explore engineering workflows Agent & operational workflowsAutomate the clear records. Route the conflicts.
Improve extraction, matching and reconciliation — and apply the same check to an agent's next action.
Explore operational workflows [ 05 ] Evidence
Fewer errors. Same automation.
Benchmark · Research evaluation on HWU64, zero-shot on 16 held-out intents, using d1-3B. Both approaches use the same underlying model; only the choice of which requests to automate changes.
Read the study Demonstration · NXP i.MX 8M Plus
A risk signal with every prediction — on the edge.
[ 06 ] How it works · Layer 01 / 05
It starts with your model.
The model you already run makes a prediction, ŷ — or an agent proposes its next action. On its own, nothing says whether to trust it.
[ 06 ] How it works · Layer 02 / 05
Capsa adds σ.
Capsa adds a proprietary risk signal alongside each prediction. Every prediction now carries σ — and this one is less certain than it looks.
[ 06 ] How it works · Layer 03 / 05
Evidence meets the output.
Themis connects outputs with evidence and operating context. Here, the prediction contradicts a relevant record.
[ 06 ] How it works · Layer 04 / 05
You set the policies.
You decide what can proceed automatically, and what is too costly to get wrong without a check.
[ 06 ] How it works · Layer 05 / 05
The right next step.
The decision layer combines every signal. This prediction is verified before anyone acts — the clear ones proceed on their own.
[ 07 ] Capsa
Go beyond the output. Build reliability into the model.
Capsa adds a proprietary risk signal to the models you already run, so every prediction ŷ comes paired with σ, Capsa’s uncertainty estimate — the signal that tells the decision layer when to check before acting.
See how Capsa works- Founded
- 2021
- Origin
- Dr. Daniela Rus's lab at MIT CSAIL
- Expertise
- Decision theoryRisk estimationModel adaptationProduction AI engineering
[ 09 ] Start
Start with one repeated decision.
Tell us where errors, delays or unnecessary review slow your workflow. We'll explore where Themis helps.
What to expect
- 01You share one decision your team makes again and again.
- 02We look at where errors, delays or review cost you most.
- 03You leave with a view of where Themis fits — and where it doesn't.