Decision AI for Pharma, FMCG & Manufacturing

The Causal, Self-learning decisioning systems for Commercial, RnD, Manufacturing and Supply Chain Excellence Teams

Book a demo
#2 globally on Spider 2.0 LiteAhead of Samsung, Snowflake and Tencent.Read the announcement Backed by NVIDIARecognized by NVIDIA as an AI startup to watch.View the announcement
DecisionX ontology base view of SKU performance
Causal State GraphEvery entity, linked live
DecisionX ontology linked entity view
Conflicts raised3 critical metric conflicts found
DecisionX ontology cross-category cascade view
Resolve raised data conflictsIn one go

Decisions you can trust.
Outcomes you can measure.

Trusted by leading Pharma & FMCG

Dr. Reddy’s Laboratories
Philip Morris International
Aditya Birla Group
Birla
Accenture
Deloitte
Wipro
Allen

One Enterprise Brain.
Every Decision.

Every decision is traced from blind spot to cause to expected impact.

The most sophisticated ontology in enterprise AI.

Deeper, faster, governed as one, and always learning.

Multi-layered reasoning.

BI shows you the number. DecisionX works out why it moved, what happens next, and the best action to take.

  • 100% mathematical accuracy: every step shows its sources, assumptions and calculations.
  • Complex reasoning: forecasting, planning, optimisation and root cause analysis in one chain.
  • Grounded in the ontology, with provenance.

How far the answer goes

  1. What happened
  2. How it splits
  3. Root cause analysis: why it changed
  4. What if you act
  5. Forecasting: what happens next
  6. Planning: the steps to get there
  7. Optimisation: the best action within your limits

Reasoning steps, from what happened and how it splits (where typical BI stops), on through root cause analysis, what if, forecasting, planning and optimisation.

Beyond the recommendation.

Typical decision tools wait to be asked. Decision apps act as conditions change.

Typical decision toolsYou askIt recommendsand there it stopsYesNoEvery outcome sharpens the next decision01Conditions changeData, market, operations02Agent triggersNo one has to ask03Analyses every levelAround the clock, 24×704Within your limits?Limits you set05Acts aloneDecides and acts05Hands overTo the right person06Outcome trackedKept in decision memoryTypical decision toolsYou askIt recommendsstopsDecisionX decision appsYesNoEvery outcome sharpens the next decision01Conditions changeData, market, operations02Agent triggersNo one has to ask03Analyses every levelAround the clock, 24×704Within your limits?Limits you set05Acts aloneDecides and acts05Hands overTo the right person06Outcome trackedKept in decision memory

ExampleAgents watch for changes.

Examples are illustrative.

Decision agents trigger the decisions.

Multi-level analysis, 24×7.

Autonomous within limits you set, handing over to your people when it should.

Not a Pilot. A P&L Line.

SalesTop-10 pharma

From static lists to live signals

2.4%

Sales conversion gain

 

Read the case study
ManufacturingPharma

Catch batch risk early

~$1M

Avoided per potential batch loss

Estimate

Supply ChainFortune 500 pharma

One connected view of supply

45days

To an AI-ready ontology

vs an estimated 12 months

R&D

R&D case study

Coming soon.

Domain modelled for process industries.

FMCG, Pharma and Manufacturing

Industry knowledge comes pre-modelled, so you don’t start from scratch. Your data connects to a domain that already understands your process, and answers stay accurate in context.

45days

to an AI-ready domain, against an estimated 12 months from scratch

88%

contextual accuracy, against 38% for a data warehouse semantic layer

Illustrative. A sample of what is modelled before you connect data.Explore the FMCG lifecycle

Built for decisions you must defend.

  1. Explainable

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

  2. Secure

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

  3. Human-in-the-loop

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

  4. Compounding

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

Turn AI activity into AI advantage.

Seven guides, from the CEO agenda to the plant floor. Each is a ranked, sourced priority sequence, built on research from Bain, McKinsey, Deloitte, BCG, EY, ZS and MIT NANDA.

CPG NPD01The AI Impact Guide for NPD Excellence LeadersGet the guidePharma Manufacturing02The AI Impact Guide for Pharma Manufacturing LeadersGet the guideManufacturing Excellence03The AI Impact Guide for Manufacturing LeadersGet the guideR&D Excellence04The AI Impact Guide for R&D Excellence LeadersGet the guideCommercial Excellence05The AI Impact Guide for Commercial Excellence in Pharma & CPGGet the guideCDO / CAIO06From Data Platform to Pharma Decision InfrastructureGet the guideCEO07From AI Activity to Pharma AI AdvantageGet the guide

Stop assembling the picture. Start deciding from it.

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

Request a demo