Jubilant Ingrevia’s growth targets and margin ambitions hinge on orchestrating complex, multi-segment operations — from custom CDMO contracts to scaling specialty chemicals and nutrition exports. Yet, fragmented data across plants, supply chain, and commercial teams leaves margin and cash on the table.
SCIKIQ unifies manufacturing, supply chain, and commercial data into an actionable business graph — so you don’t just monitor disruptions, you pre-empt and resolve them. Move from lagging reports to real-time, AI-driven action, protecting ₹70 Cr+ in EBITDA and accelerating time-to-market for every new molecule.
Captured from 28 sources across strategy, leadership, lines of business, competition, geographies, capabilities and recent signals — and used to ground everything below.
The maturity curve runs from visibility, to explanation, to natural-language reasoning, to action — and each step depends on the one before it.
Integrates SAP, LIMS, MES, and CRM data to give a live, plant-to-customer view of production, order fulfilment, and margin leakage — across pharma, nutrition, and agro lines.
Operational EfficiencyMaps every batch, asset, and customer order to root-cause quality events, supply delays, and their financial impact — so you see the true drivers of EBITDA and compliance risk.
Profitability & ComplianceNatural-language answers on plant throughput, order delays, and margin dips — with context, not just numbers. Ask: 'Why did Q2 EBITDA miss target in Nutrition?'
Competitive AdvantageAutonomous agents close supply gaps, trigger alternate sourcing, and resolve receivables bottlenecks — protecting cash and accelerating order-to-cash.
Cash & GrowthTracks real-time margin erosion by product, contract, and customer, surfacing actionable levers to protect profitability.
ProfitabilityBuilt bottom-up, because trust compounds upward: agents are only as safe as the copilot's grounding, the copilot only as reliable as the graph, the graph only as complete as the 360° model beneath it.
Unifies siloed data across SAP, LIMS, MES, CRM, and procurement into a live business 360.
Models causal links between batches, assets, orders, and compliance events — surfacing root causes.
Conversational AI grounded in business context, not just dashboards.
Autonomous agents that resolve supply, quality, and cash issues — not just alert.
Data remains in your core systems — SAP, LIMS, MES, CRM — but is unified into business entities and relationships, not just tables. This creates a living 360 for every plant, batch, customer, and contract.
200+ pre-built connectors ingest data from SAP, LIMS, MES, CRM, and more — with 85% faster integration.
Business concepts (batch, contract, supplier, customer) are mapped and enriched with metadata and KPIs.
Entities and relationships are deduplicated and modeled into a business graph — the foundation for root-cause analysis.
Lineage, access, and quality controls ensure compliance and auditability — with 95% fewer compliance violations.
Live operational view of each plant — OEE, downtime, batch genealogy, and compliance events.
End-to-end trace of every batch — from raw material to customer delivery and compliance reporting.
Unified view of every customer, order, contract, SLA, and receivables risk.
Quality, delivery, and incident records for every supplier — with risk scoring and alternate sourcing.
Margin, pipeline, and compliance context for every CDMO and Nutrition contract.
SCIKIQ delivers 85% faster integration and 60% lower TCO than custom builds — with 200+ connectors, contextual business graphs, and autonomous agents out-of-the-box. No multi-year, high-risk IT projects.
BI dashboards show you what happened; SCIKIQ’s knowledge graph and agents trace causes and fix issues — closing the loop on margin, compliance, and cash, not just reporting.
Most data fabrics unify tables, not business context. SCIKIQ’s graph models real entities (batch, contract, supplier), enabling root-cause analysis and autonomous action — tailored to chemicals and CDMO.
GenAI Studio is grounded in your real data and compliance rules, not hallucinated answers — so you get explainable, auditable insights and actions, not just chat.
When the four layers are in place, leadership sees a single live view — every number traceable to its source, every alert to its root cause.
Entities and their typed relationships as one connected, physics-driven graph. Drag nodes, scroll to zoom, click to inspect — or trace the live scenario from root cause to business outcome.
Ask any question about your operations, margin, or compliance — get answers grounded in real plant, order, and contract data. Language models supply the fluency; the graph supplies the truth.
Every agent draws on the same graph and semantic layer — then closes the loop with a real transaction in the source system.
“Can we trust it?” — answered by design, not by promise.
Every leader sees themselves — each use case builds on a business 360 and the four pillars it needs: 1 Enterprise 360 · 2 Knowledge Graph · 3 AI Copilot · 4 Agent Factory.
Unify MES, LIMS, and SAP data for every plant and batch; trace quality events to supplier or process root causes, and trigger autonomous remediation.
Monitor supplier quality and delivery in real time; agents switch to alternates automatically when risk emerges, protecting production and margin.
Track overdue orders, automate collections, and link receivables risk to operational events — freeing up working capital and reducing DSO.
Model every CDMO and Nutrition contract, link to batch and supplier events, and optimize pricing and risk in real time.
Automate compliance reporting from LIMS and SAP, trace every event to root cause, and surface audit risks before they escalate.
Built once, the context layer becomes shared infrastructure — so each new AI initiative starts from enterprise context, not a blank integration backlog.
We would prove the context layer in three focused sprints — earning the right to scale with evidence, not slideware.
Rapidly connect SAP, LIMS, MES, and CRM; create the first Plant and Batch 360s.
Model causal links, enable root-cause analysis, and deploy AI Copilot for plain-language answers.
Launch agents for supply risk, receivables, and margin optimization; measure impact on EBITDA and DSO.
In 120 days, move from siloed reporting to autonomous, AI-driven value creation — with measurable impact on margin, cash, and compliance.