DOMAINS · V2.2
MADE IN INDIA. BUILT FOR THE WORLD.22+ verticals.
22+ verticals.
One reasoning
core.
Every domain pack ships a pre-tuned cohort of agents, a domain ontology, and signal calibrations from real cases. The reasoning core stays constant — what changes is which of the 404 agents are taught to see. 20,825 swarm agents and 2.4 million lines of proprietary code underneath, 100 million+ production runs behind them.
DOMAINS ACTIVE
22+
AGENTS PER RUN
404
SWARM AGENTS
20,825
PRODUCTION RUNS
100M+
SIGNAL HEALTH
STATUS
All systems nominal
22+ Domains LiveUp to 404 agents per Omega run20,825 Swarm Agents9 Capability Layers100M+ Production Runs
D01 · DOMAINLIVE
Financial Modeling & Risk
Risk-adjusted forecasting, capital allocation, and stress scenarios — modeled in parallel across nine force variables, not collapsed into a single point estimate.
// SIGNALS & SPEC
Latency · deep
~3.4m
Confidence
0.88
Domain signals
27
Deployed since
v0.7
// REPRESENTATIVE SIGNALS
Capex pacingF3 · F4 · F8
Phase capital deployment against demand variance and momentum decay across the planning horizon.
Liquidity stressF4 · F5 · F8
Adverse-scenario cash-flow paths across covenant, FX, and rate shocks.
M&A signal triageF1 · F6 · F7
Target screening with second-order coupling effects across portfolio.
// FORCE LOAD · PER-RUN AVG
F3Momentum
F4Cost
F8Volatility
F1Intent
F5Constraint
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN FINANCIAL
See customer outcomes →USE CASE
Should we accelerate Q4 capex?
Impact: 38% outlay deferred
USE CASE
Does our runway survive a rate shock?
Impact: 7 of 9 regimes hold
USE CASE
Which targets pass the coupling test?
Impact: 3 of 47 pass
USE CASE
How do we allocate 500 crore?
Impact: 27% better returns
D02 · DOMAINLIVE
Supply Chain Optimization
Multi-tier disruption modeling. Tracks tier-1 through tier-4 supplier risk, port congestion, and lead-time distributions — not averages.
// SIGNALS & SPEC
Latency · deep
~2.8m
Domain signals
34
Tier visibility
T1 → T4
Deployed since
v0.6
// REPRESENTATIVE SIGNALS
Tier-2 concentrationF7 · F4
Identify single-points-of-failure two tiers deep, weighted by replacement lead time.
Disruption pathF3 · F6 · F8
Simulate cascading shutdowns from origin point through downstream SKUs.
Inventory postureF4 · F8
Optimal buffer levels per SKU given volatility and carrying cost.
// FORCE LOAD · PER-RUN AVG
F7Coupling
F3Momentum
F4Cost
F8Volatility
F6Exploration
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN SUPPLY CHAIN
See customer outcomes →USE CASE
What's our tier-2 failure probability?
Impact: 23% lower risk
USE CASE
Where should new warehouses go?
Impact: 16% lower cost
USE CASE
How will demand shift next quarter?
Impact: 31% better accuracy
USE CASE
What's the optimal safety stock?
Impact: 4.2 crore released
D03 · DOMAINLIVE
Healthcare Diagnostics
Clinical decision support with full provenance. Every output is traceable back to the agent cohort, evidence chain, and confidence calibration that produced it.
// SIGNALS & SPEC
Latency · standard
~14s
Domain signals
51
Trace depth
agent-level
Deployed since
v0.8
// REPRESENTATIVE SIGNALS
Differential rankingF1 · F8 · F9
Probabilistic differential across symptom vector with explicit uncertainty.
Treatment pathwayF4 · F5 · F8
Cost-effectiveness across regimen alternatives with constraint enforcement.
Outlier flagF6 · F9
Surface cases whose features fall outside the cohort distribution.
// FORCE LOAD · PER-RUN AVG
F9Provenance
F5Constraint
F1Intent
F8Volatility
F4Cost
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN HEALTHCARE
See customer outcomes →USE CASE
What are the top differentials here?
Impact: 0.84 combined mass
USE CASE
Which regimen wins on cost?
Impact: dominates on 4 of 5
USE CASE
Which cases fall outside the cohort?
Impact: 2.3% routed
USE CASE
Can we audit this recommendation?
Impact: 27 nodes signed
D04 · DOMAINLIVE
Legal & Compliance
Risk surfaces across contract clauses, jurisdictional overlap, and regulatory regimes — modeled as a coupled system rather than independent checks.
// SIGNALS & SPEC
Latency · deliberate
~46s
Domain signals
42
Jurisdictions
India + 28
Deployed since
v0.8
// REPRESENTATIVE SIGNALS
Contract risk mapF5 · F4 · F7
Score every clause for liability exposure and counterparty dependence.
Regulatory driftF3 · F8
Detect regulatory regime shifts before formal publication via signal momentum.
Conflict graphF7 · F9
Map cross-jurisdictional conflicts in a single transaction.
// FORCE LOAD · PER-RUN AVG
F5Constraint
F9Provenance
F7Coupling
F3Momentum
F8Volatility
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN LEGAL
See customer outcomes →USE CASE
Which clauses carry real exposure?
Impact: 38 crore surfaced
USE CASE
Is a regulatory shift coming?
Impact: 18-day lead
USE CASE
Where do jurisdictions collide?
Impact: 3 conflicts found
USE CASE
What matters in this document set?
Impact: 61% review cut
D05 · DOMAINLIVE
Energy Grid Management
Load balancing, dispatch optimization, and weather-coupled forecasting at substation granularity. Sub-second decisions on a 90-agent cohort.
// SIGNALS & SPEC
Latency · reflex
~480ms
Domain signals
38
Granularity
substation
Deployed since
v0.7
// REPRESENTATIVE SIGNALS
Dispatch optimizationF3 · F4 · F8
Real-time generator dispatch across constraints and weather coupling.
Demand forecastF3 · F6
Substation-level demand curves with confidence bands.
Renewable integrationF6 · F7 · F8
Stochastic intake from solar/wind under grid stability constraints.
// FORCE LOAD · PER-RUN AVG
F3Momentum
F8Volatility
F4Cost
F7Coupling
F6Exploration
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN ENERGY
See customer outcomes →USE CASE
How do we dispatch generation now?
Impact: 3.1% cost cut
USE CASE
What is substation demand in 24h?
Impact: tight bands
USE CASE
How much solar can the grid absorb?
Impact: 11% less curtailment
USE CASE
Which feeders fail under load?
Impact: zero violations
D06 · DOMAINLIVE
Defense & Geopolitical Intel
Multi-actor scenario modeling. Signal triage at scale. Escalation paths surfaced with probability bands rather than narrative speculation.
// SIGNALS & SPEC
Latency · deep
~5.1m
Domain signals
64
Actor models
agent-graph
Deployment
sovereign
// REPRESENTATIVE SIGNALS
Escalation treeF1 · F7 · F8
Branch-and-bound exploration of actor moves with volatility bands.
Signal triageF6 · F9
Surface anomalous signals from noise; tag provenance to specific cohort agents.
Capability inferenceF1 · F3 · F8
Estimate actor capability with explicit uncertainty.
// FORCE LOAD · PER-RUN AVG
F1Intent
F7Coupling
F8Volatility
F9Provenance
F6Exploration
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN GEOPOLITICAL
See customer outcomes →USE CASE
Which actor moves escalate?
Impact: 5 paths flagged
USE CASE
What in this noise is real?
Impact: 14 of 11,402
USE CASE
What can this actor actually do?
Impact: 2 dimensions revised
USE CASE
Can this run air-gapped?
Impact: zero egress
D07 · DOMAINLIVE
Climate & Environment
Coupled physical-economic modeling for transition strategy, asset risk, and operational adaptation.
// SIGNALS & SPEC
Latency · deep
~6.4m
Domain signals
46
Horizons
5y / 25y / 75y
Deployed since
v0.8
// REPRESENTATIVE SIGNALS
Asset-level riskF8 · F4
Physical risk under multiple emission pathways at site granularity.
Transition costF4 · F3 · F5
Levelized cost of transition pathways with regulatory drift.
Supply chain couplingF7 · F8
Climate-induced supplier risk propagation across the tier graph.
// FORCE LOAD · PER-RUN AVG
F8Volatility
F4Cost
F3Momentum
F7Coupling
F5Constraint
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN CLIMATE
See customer outcomes →USE CASE
Which sites breach risk thresholds?
Impact: 17% of assets
USE CASE
Which transition pathway wins?
Impact: 620 crore better NPV
USE CASE
How does climate hit our suppliers?
Impact: tier-2 exposure
USE CASE
What do we harden first?
Impact: capex rerouted
D08 · DOMAINLIVE
Pharma R&D Pipeline
Pipeline triage, trial design under uncertainty, and regulatory pathway optimization. Confidence bands on every prediction.
// SIGNALS & SPEC
Latency · deliberate
~38s
Domain signals
58
Provenance
signed DAG
Deployed since
v0.7
// REPRESENTATIVE SIGNALS
Pipeline rankingF1 · F4 · F8
Risk-adjusted NPV across candidates with stage-gated decision points.
Trial designF6 · F8
Adaptive trial topology with explicit exploration-exploitation budget.
Regulatory routingF5 · F9
Path selection across regulatory pathways with timeline distributions.
// FORCE LOAD · PER-RUN AVG
F8Volatility
F9Provenance
F5Constraint
F4Cost
F6Exploration
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN PHARMA R&D
See customer outcomes →USE CASE
Which candidates clear the gate?
Impact: 3 of 14 pass
USE CASE
Can we shrink the trial?
Impact: 22% smaller
USE CASE
Which pathway approves fastest?
Impact: 18 months to approval
USE CASE
Is this an emerging safety signal?
Impact: signed trace
D09 · DOMAINLIVE
Real Estate & Infrastructure
Site selection, demand modeling, and infrastructure capacity planning grounded in coupled urban-economic signals.
// SIGNALS & SPEC
Latency · standard
~28s
Domain signals
31
Geo granularity
ward-level
Deployed since
v0.6
// REPRESENTATIVE SIGNALS
Site scoringF1 · F4 · F7
Composite site score across 18 demand drivers and 11 cost factors.
Demand curveF3 · F8
Granular demand forecast with confidence bands by use class.
Capacity planningF3 · F4 · F8
Stage infrastructure capacity against demand uncertainty.
// FORCE LOAD · PER-RUN AVG
F4Cost
F3Momentum
F1Intent
F8Volatility
F7Coupling
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN REAL ESTATE
See customer outcomes →USE CASE
Which site should we buy?
Impact: 6 sites clear
USE CASE
What is ward-level demand?
Impact: tight bands
USE CASE
Do we build phase 2 now?
Impact: 92% of NPV held
USE CASE
What use mix maximises returns?
Impact: 4.1% higher IRR
D10 · DOMAINLIVE
Marketing Strategy
Channel attribution under uncertainty, segment-level lifetime curves, and counterfactual modeling — beyond MMM black boxes.
// SIGNALS & SPEC
Latency · standard
~18s
Domain signals
29
Attribution
counterfactual
Deployed since
v0.8
// REPRESENTATIVE SIGNALS
AttributionF7 · F8
Counterfactual channel contribution with explicit uncertainty.
LTV by segmentF3 · F8
Segment-level lifetime curves with cohort-specific volatility.
Budget shapeF4 · F6
Optimal budget allocation under diminishing returns and exploration.
// FORCE LOAD · PER-RUN AVG
F7Coupling
F4Cost
F8Volatility
F6Exploration
F3Momentum
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN MARKETING
See customer outcomes →USE CASE
What did channel C actually earn?
Impact: 31% below MMM
USE CASE
Which segment is worth acquiring?
Impact: top-quintile value
USE CASE
Where should the next rupee go?
Impact: 2.1% margin gain
USE CASE
Who is about to leave?
Impact: retention up 6%
D11 · DOMAINLIVE
Manufacturing & Operations
Throughput modeling, defect-cost tradeoff, and supplier risk — coupled into a single agent run, not stitched from three dashboards.
// SIGNALS & SPEC
Latency · standard
~12s
Domain signals
37
Granularity
line-level
Deployed since
v0.7
// REPRESENTATIVE SIGNALS
Throughput modelF3 · F4
Per-line throughput under variable defect rates and changeover cost.
Defect costF4 · F8
Total cost of defects including downstream returns and warranty.
Supplier riskF7 · F4
Per-supplier risk score with substitutability cost.
// FORCE LOAD · PER-RUN AVG
F3Momentum
F4Cost
F7Coupling
F8Volatility
F6Exploration
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN MANUFACTURING
See customer outcomes →USE CASE
How do we lift line throughput?
Impact: 4.8% higher
USE CASE
What do defects really cost?
Impact: 2.7 times recorded
USE CASE
Which supplier threatens output?
Impact: 9.2% at risk
USE CASE
When do we take the line down?
Impact: downtime cut 18%
D12 · DOMAINLIVE
Government Policy Analysis
Impact estimation under uncertainty, stakeholder modeling, and second-order effects — surfaced before legislation, not after.
// SIGNALS & SPEC
Latency · deep
~4.6m
Domain signals
49
Stakeholder graph
mapped
Deployment
sovereign
// REPRESENTATIVE SIGNALS
Impact estimateF3 · F4 · F8
First and second-order policy impact with explicit uncertainty bands.
Stakeholder modelF1 · F7
Equilibrium response across stakeholder graph under proposed change.
CounterfactualF6 · F9
Compare against unchanged baseline with provenance trace.
// FORCE LOAD · PER-RUN AVG
F1Intent
F7Coupling
F4Cost
F8Volatility
F9Provenance
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN POLICY
See customer outcomes →USE CASE
What will this policy actually do?
Impact: impact modelled
USE CASE
Who loses under this change?
Impact: 3 clusters flagged
USE CASE
What if we do nothing?
Impact: welfare baseline
USE CASE
Can this run inside government?
Impact: air-gapped
D13 · DOMAINLIVE
Cybersecurity & Threat Intel
Threat triage, attack-path modeling, and posture assessment under adversarial pressure. Built around the assumption that the input is hostile.
// SIGNALS & SPEC
Latency · reflex
~620ms
Domain signals
58
Threat models
MITRE ATT&CK
Deployment
sovereign
// REPRESENTATIVE SIGNALS
Attack-path graphAdversarial · Domain · Coupling
Enumerate viable attack chains across the asset graph with kill-chain stage scoring.
Anomaly triagePerception · Adversarial · Volatility
Triage signals from EDR/NDR/IDS streams; surface novelty above noise floor.
Posture driftTemporal · Constraint · Provenance
Detect security-posture decay before next scheduled audit cycle.
// FORCE LOAD · PER-RUN AVG
F5Constraint
F6Coupling
F1Intent
F7Provenance
F5Volatility
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN CYBERSECURITY
See customer outcomes →USE CASE
How would they get in?
Impact: 9 paths viable
USE CASE
What in the stream is novel?
Impact: 14 of 22,408
USE CASE
Is our posture decaying?
Impact: caught early
USE CASE
What falls if this host falls?
Impact: 3 hops deep
D14 · DOMAINLIVE
Insurance & Actuarial Risk
Reserve adequacy, catastrophe modeling, and underwriting decisions under tail risk. Per-policy granularity; coupled to climate, supply, and macro signals.
// SIGNALS & SPEC
Latency · deliberate
~52s
Domain signals
44
Granularity
per-policy
Deployed since
v2.0
// REPRESENTATIVE SIGNALS
Reserve adequacyVolatility · Constraint · Temporal
Estimate ultimate loss under multiple reserve methodologies and pressure-test.
Cat modelingWorld-Model · Domain · Coupling
Catastrophe loss simulation coupled to climate scenarios and concentration risk.
Underwriting triageAdversarial · Intent · Memory
Per-risk scoring with explicit adverse-selection and fraud-pattern detection.
// FORCE LOAD · PER-RUN AVG
F5Volatility
F4Constraint
F3Momentum
F7Provenance
F6Coupling
Load = % of the 90 agents in a deep run that touch this force,
measured across the last 30 sessions in this domain.
measured across the last 30 sessions in this domain.
// QUESTIONS IT ANSWERS IN INSURANCE
See customer outcomes →USE CASE
Are our reserves enough?
Impact: adequate at 1-in-100
USE CASE
What does a bad season cost?
Impact: loss variance up
USE CASE
Should we write this risk?
Impact: 2.4% flagged
USE CASE
Where are we over-exposed?
Impact: concentration scored
Fourteen featured here. 22+ live in production.
Don't see your vertical? We calibrate a custom domain pack for your unique signals and ontology — a 4–6 week build with an agent topology and a confidence baseline against your own historical decisions.
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