What the decision-grade AI stack can do.

Mathematics fit for each domain, turned into decisions an executive can act on: what is likely, what it would cost, what to do about it and who owns the response.

At a glance

Ten capabilities, one decision layer.

01
1. Data
2. Training
3. Challenged
AI & machine learning
Statistical, stochastic, symbolic, fuzzy and learned model families, each used where it fits.
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02
1. History
2. Forecast
3. Interval
Predictive analysis
Forecasts with their horizon and interval, not a single line.
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03
1. Decision
2. Options
3. Best action
Prescriptive analysis
The next action ranked by consequence, with an accountable owner.
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04
1. Signals
2. Translation
3. P&L impact
Financial exposure
Every signal translated into currency and mapped to the P&L line it hits.
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05
1. Estimate
2. Distribution
3. Tails
Uncertainty modeling
Distributions and tails, so the cost of being wrong is visible.
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06
1. Allocation
2. Marginal value
3. Rebalance
Capital optimization
Capital, liquidity and hedging allocated where they reduce the most exposure.
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07
1. Noise
2. Pattern
3. Signal
Early signal generation
Weak signals qualified before they appear in reports.
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08
1. Detection
2. Routing
3. Owner
Real-time alerting
Anticipatory alerts with probability, impact, owner and deadline.
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09
1. Entities
2. Relations
3. Context
Ontology
Entities, relations, units and timing with one definition, as the context for inference.
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10
1. Data
2. Agents
3. Action
AI agents
AIT Agents that monitor, predict and prescribe on top of TheraOS, under control.
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01
1. Data
2. Training
3. Challenged
AI & machine learning
Statistical, stochastic, symbolic, fuzzy and learned model families, each used where it fits.
See the full story
02
1. History
2. Forecast
3. Interval
Predictive analysis
Forecasts with their horizon and interval, not a single line.
See the full story
03
1. Decision
2. Options
3. Best action
Prescriptive analysis
The next action ranked by consequence, with an accountable owner.
See the full story
04
1. Signals
2. Translation
3. P&L impact
Financial exposure
Every signal translated into currency and mapped to the P&L line it hits.
See the full story
05
1. Estimate
2. Distribution
3. Tails
Uncertainty modeling
Distributions and tails, so the cost of being wrong is visible.
See the full story
06
1. Allocation
2. Marginal value
3. Rebalance
Capital optimization
Capital, liquidity and hedging allocated where they reduce the most exposure.
See the full story
07
1. Noise
2. Pattern
3. Signal
Early signal generation
Weak signals qualified before they appear in reports.
See the full story
08
1. Detection
2. Routing
3. Owner
Real-time alerting
Anticipatory alerts with probability, impact, owner and deadline.
See the full story
09
1. Entities
2. Relations
3. Context
Ontology
Entities, relations, units and timing with one definition, as the context for inference.
See the full story
10
1. Data
2. Agents
3. Action
AI agents
AIT Agents that monitor, predict and prescribe on top of TheraOS, under control.
See the full story

01 · AI & machine learning

Mathematics fit for each domain.

Large language models are remarkable interfaces. Measuring liquidity stress, credit drift or energy price distributions takes model families chosen for the phenomenon.

02 · Predictive & prescriptive analysis

From what is likely to what to do.

03 · Exposure, uncertainty & capital

Every probability carries a price.

04 · Early signals & real-time alerting

See it while there is still time to act.

05 · Ontology & AI agents

Structure first. Then agents. Then evidence.

See these capabilities on your own decision problem.

Request a demo Explore TheraOS

Ten capabilities, one decision layer.

AI & machine learning

Predictive analysis

Prescriptive analysis

Financial exposure

Uncertainty modeling

Capital optimization

Early signal generation

Real-time alerting

Ontology

AI agents