Decision AI · Strategy, Analytics and AI Teams

Decision AI for

Enterprise

Decision Infrastructure

Purpose-built for high-consequence enterprise decisions.

Grounded in causality, context, and memory - so every decision gets faster, sharper, and smarter than the last.

World Rank #2 in Enterprise Reasoning

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Signals
Reason
Decide
Track
Learn
Blindspot
Forecast
Stress test
Strategic
Operational
Execution
RCA
Competitive
Scenarios
Optimise
Tactical
Investment
Memory
DX
Detecting cross-functional signals
0signals detected
Pipeline velocity down 18%
CRM · last 14 days
Warn
NPS score up 12 pts (Q3→Q4)
Survey · last 30 days
+↑
Competitor price cut 12%
Market intel · this week
Watch
Scanning ERP, market feeds
Decision reasoning
Strategy lead
Why is pipeline slowing despite strong NPS? Should we push enterprise this quarter?
Pulling CRM pipeline and deal velocity data
Cross-referencing NPS signals with win rate
Assessing margin floor and capacity risk
DecisionX
Ask a follow-up question...
Decision
Accelerate enterprise
with margin and capacity guardrails
Primary actionTarget top-20 accounts
Margin floor32%
Capacity triggerOEE above 94%
Review cadence28-day checkpoint
Confidence
87%
Shared via
Email
Slack
Delivered
Track
Execution alignment & investment returns
Enterprise push on plan
92%
Top-20 account pipeline
+23%
Margin floor maintained
34%
28-day review signal
Due
+23%
Pipeline lift
87%
On-plan rate
Day 28
Review due
Enterprise push ↑23%
NPS causal: cycle length
Institutional memory
Decisions recorded
0
Patterns identified
0
Replication rate
0%

Powering 30,000+ Decisions

Trusted by Strategy & AI Teams

NVIDIA Inception Program Recognized by NVIDIA as an AI Startup to watch out for

Real World Decisions with Consequence

Months of deliberation. Minutes to decide. Causally. Contextually.

Each domain shows the operating model: the workflow, the inputs DecisionX connects, what the agent recommends, and the business outcome.

Move spend, inventory, and teams toward growth. For commercial teams that need to explain gaps, reallocate capital, and act before the quarter is gone.

Trade Spend

Reallocate ₹8 Cr to South & East for 2.3x incremental margin

2.3x marginScale

Shift MT / CSD mix to 60:40 as elasticity favours modern trade

Demand

Commit the Depot x SKU order with 94% of lines in confidence band

94% confidenceWatch

Flag 187 low-confidence lines for planner review

HCP

Prioritise 3 HCPs today at 3.2x uplift over list average

3.2x upliftScale

De-prioritise 40 HCPs with flat engagement this cycle

Media Mix

Move £2M from TV to digital for 1.8x ROAS uplift

1.8x ROASScale

Accelerate the Q4 campaign as competitor spend drops

SKU & RCA

Region 3 decline traced to distribution, not demand

12 SKUsReduce

Cut the tail 12 SKUs running margin-negative for 3 quarters

Every one of these decisions used to take weeks of context-stitching across data, tools, decks, and meetings, and the reasoning disappeared the moment it was made. Now it is captured, audited, and compounds into the next decision.

Prevent quality, yield, and downtime losses before they surface. For operations and quality teams that need to trace risk across inputs, process, equipment, and outcomes.

Batch Release

Reroute Batch 447 to Site B with parameter P4 outside spec

Out of specWatch

Release Batch 512 with all QA parameters in spec

Quality

Escalate the Line 3 deviation requiring regulatory notification

RegulatoryWatch

Halt Batch 512 in-process as quality floor is not met

Downtime

Schedule preventive maintenance on Line 1, signalled 48 hrs early

48 hrs earlyScale

Raw Material

Change process parameter P4, traced to input variance

Param P4Reduce

Flag Supplier 2 CoA drifting from historical baseline

Supplier Risk

Switch to Supplier 4 after a third cycle of elevated risk

3rd cycleWatch

Every one of these decisions used to take weeks of context-stitching across data, tools, decks, and meetings, and the reasoning disappeared the moment it was made. Now it is captured, audited, and compounds into the next decision.

Find the signal, choose the bet, and move faster. For scientific, medical, and innovation teams where each decision carries regulatory, portfolio, or time-to-market risk.

Pharmacovigilance

Escalate ICSR Case 4471 with the 15-day deadline at risk

15 daysWatch

Flag Drug X safety signal past the WHO-UMC threshold

Clinical Trials

Open sites in Germany & Poland enrolling 23% faster

23% fasterScale

Add Site 14 as the cohort trigger fired at Week 8

Protocol

Lock the 6-week visit schedule, 8 weeks lifts dropout 34%

34% dropoutWatch

Revise eligibility criterion E3, a known amendment driver

Portfolio

Advance Compound X to Phase 3 with thresholds met

Phase 3Scale

Kill programme Y as cost exceeds expected value

Every one of these decisions used to take weeks of context-stitching across data, tools, decks, and meetings, and the reasoning disappeared the moment it was made. Now it is captured, audited, and compounds into the next decision.

The Platform

The AI Decisioning Platform for all Scenarios.

DecisionX connects your data, domain knowledge, and decision history into a living causal model, then reasons through it to give you a decision, not just an answer.

Answer
Deep dive
Sources (237)
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Conversation
Should we reallocate ₹50 Cr of trade investment? Where should we invest and why?
10:21 AM
DecisionX Agent
Analyzing enterprise context
Collecting signals
237 sources
Analyzing with 8 agents
Demand, Supply, Pricing, Finance, Risk, Channel, ROI
Causal reasoning
Building impact & trade-off models
Generating decision paths
Ranking and validating options
Analysis complete. Here is your decision.
Recommended Decision
Invest ₹8 Cr more in South & East regions
Projected to deliver highest incremental revenue with low risk and fastest time to impact.
Revenue Impact
+₹14.2 Cr
(+11.3%)
Margin Impact
+₹3.8 Cr
(+4.2%)
Risk Level
Low
vs. options
Time to Impact
Immediate
0–2 weeks
Why this recommendation
Demand momentum strongest in South & East
Competitor spends decreased in key markets
Inventory position healthy to support growth
ROI projected highest among all options
Aligned with annual growth and margin targets
Decision Paths (Trade-off Analysis)
Option A · Recommended92%
Invest in South & East
Revenue
+₹14.2 Cr
Margin
+₹3.8 Cr
Risk
Low
Time
Immediate
Option B78%
Invest in All Regions
Revenue
+₹8.1 Cr
Margin
+₹1.2 Cr
Risk
Medium
Time
2–4 wks
Option C64%
Maintain Current Split
Revenue
+₹4.3 Cr
Margin
+₹2.1 Cr
Risk
Low
Time
4–6 wks
Institutional judgment
that compounds.
Every decision made is captured with full reasoning, assumptions, and outcome. The system learns from every call, so the next decision is faster, better informed, and draws on everything that came before.
D1
Decision made & captured
Rationale, trade-offs, context, and confidence score stored in Decision Repository.
D+
Outcome tracked
Actual result measured against recommendation. Causal model updated with real-world feedback.
D2
Next decision informed by the last
New hires inherit institutional judgment. Same mistakes never repeated. Decisions accelerate.
∞
Compounding moat
The longer you use DecisionX, the sharper it gets. Competitors starting fresh can never catch up.
Decision Repository Outcome Tracking Institutional Memory Compounding Intelligence
Decision Agents.
Always on.
High-frequency decisions that can't wait for a weekly meeting. DecisionX agents monitor signals continuously, reason through your causal model, and act, or escalate, in real time.
Pharmacovigilance Agent
Monitors adverse event feeds, triages ICSRs, surfaces safety signals before regulators do.
Active · 24/7
Quality Deviation Agent
Traces raw material variance through production chain, flags QA risks before manufacturing.
Active · Real-time
Trade Spend Agent
Monitors channel performance, competitor signals, and ROI, reallocates spend automatically.
Active · Daily
Demand Signal Agent
Detects demand shifts early, surfaces reorder and reprioritisation decisions before stockouts.
Active · Real-time

The Architecture

The Core: Decision Ontology.
Self Learning & Causal.

DecisionX autonomously constructs a living Decision Ontology by connecting your Data, Domain, and Process ontologies into a single Causal State Graph.

Autonomously
01
Data Ontology

What exists, how it connects, and what it means

02
Process Ontology

How work flows and how outcomes are achieved

03
Domain Ontology

Industry rules, constraints, and business logic

Causal State Graph
Live · Self-learning
Data Ontology
Process Ontology
Domain Ontology
Output
Decision Ontology
Causal State Graph

A living model that continuously learns what decisions matter, what drives them, and why, across your entire enterprise.

Causal State Graph · live nodes
Trade Spend Demand Signal Competitor Activity Inventory Price Elasticity Seasonality Channel ROI
→Trade Spend causes Demand Uplift (+32%)
→Competitor Activity moderates Channel ROI
→Price Elasticity constrains Trade Spend ceiling
Zero hallucination Cross-functional Fully auditable Answers Why
Causal. Not Semantic.
Generic AI tools match patterns in text. DecisionX reasons through cause and effect, understanding why demand dropped, not just that it did. It traces the chain from raw material variance to batch failure, from competitor spend to channel erosion. Pattern matching gives you correlation. Causal reasoning gives you the answer.
Generic AI: semantic search & pattern match  •  DecisionX: causal reasoning
Self Learning. Always.
Every decision made sharpens the causal model. Every outcome tracked updates the ontology. DecisionX gets smarter with every decision your organisation makes, without manual retraining or knowledge management overhead. The system that learns is the system that compounds.

Real-World Proof

Decisions made. Outcomes delivered.

Not demos. Not projections. Production decisions running in CPG and Pharma enterprises today.

Pharma · R&D

Global Pharma Conglomerate

90%faster AE triage
Zeromissed 15-day deadlines
3×signals caught early

Millions of adverse event reports annually. Manual triage, missed signals, 15-day regulatory deadline pressure on every serious case.

Manufacturing · Quality

Leading Textile Manufacturer

60%quality defects prevented
48hrspredictive alert window
Liveraw → mfg causal chain

Quality failures traced to raw material variance, caught too late, post-production. No causal chain from input to defect.

Pharma OTC · Commercial

Global Pharma OTC Business

35%marketing efficiency gain
2×sales rep productivity
3 wksfaster campaign decisions

Marketing spend without channel-level ROI visibility. Campaign decisions on lag data. Sales teams unable to prioritise at scale.

Industries

Built for industries where
decisions carry cost.

DecisionX is specific enough for regulated, operational, and commercial contexts where a plausible answer is not enough.

Pharma
Consumer & Retail
Manufacturing
Financial Services
Revenue Ops

Pharma

Where DecisionX fits

medical safety, launch, quality, portfolio.

High-cost decision types
AE escalationSignal detectionLaunch sequencingQuality risk
Example workflow
Triage serious adverse events with causality, listedness, seriousness, and deadline logic.
Why generic AI fails here
The decision needs evidence, regulatory logic, audit trail, and human approval.
DecisionX difference
Context, policy, causality, action path, owner, and outcome memory live in the same workflow.

Consumer & Retail

Where DecisionX fits

commercial, supply, growth.

High-cost decision types
Trade allocationSKU focusDemand gap RCAPricing response
Example workflow
Reallocate trade spend by channel, region, and SKU.
Why generic AI fails here
It cannot connect sell-through, inventory, channel ROI, and field context into an auditable action.
DecisionX difference
Context, policy, causality, action path, owner, and outcome memory live in the same workflow.

Manufacturing

Where DecisionX fits

quality, yield, supplier risk, downtime.

High-cost decision types
Batch hold/releaseDeviation escalationRaw material riskMaintenance action
Example workflow
Hold a risky batch before a defect reaches production.
Why generic AI fails here
It lacks process causality and plant-specific context.
DecisionX difference
Context, policy, causality, action path, owner, and outcome memory live in the same workflow.

Financial Services

Where DecisionX fits

risk, claims, compliance, portfolio.

High-cost decision types
Claims triageAML/KYC escalationPortfolio riskAudit evidence
Example workflow
Rank exceptions by risk and action path.
Why generic AI fails here
It lacks policy grounding, traceability, and liability-aware workflows.
DecisionX difference
Context, policy, causality, action path, owner, and outcome memory live in the same workflow.

Revenue Ops

Where DecisionX fits

forecast, pricing, account focus, territory.

High-cost decision types
Forecast gapAccount prioritizationDiscountingTerritory rebalance
Example workflow
Identify why pipeline is down and where reps should focus.
Why generic AI fails here
It answers the question but does not own the decision loop.
DecisionX difference
Context, policy, causality, action path, owner, and outcome memory live in the same workflow.

Enterprise Trust

Built for decisions you must defend.

Explainable

Every answer traces to source, logic, assumptions and the full reasoning chain.

Human-in-the-loop

Ontology Agents build the model. Your team reviews, edits and governs before it reasons.

Secure

SOC 2 Type II, GDPR, ISO 27001. Public and private cloud. RBAC, IAM, SSO.

Compounding

Every tracked decision sharpens the ontology. Institutional intelligence that grows with use.

Decision intelligence platform

Stop assembling the picture.
Start deciding from it.

See DecisionX on your data, your context, your decisions. First value in 15 days.

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