AI-Powered Operations Intelligence
From Excel-based planning to a living, adaptive forecasting & inventory brain
About Trinesis
Gen AI–driven process automation & software engineering — trusted by global enterprises for 6+ years
Global Footprint
- HQ & Innovation Hub
Pune, India
- Sales Offices
Germany & Austin, TX, USA
- Around-the-Clock Support
24×7 global availability — your business never stops
Recognition & Partnerships
A fast-growing team of visionary project managers, industry-specific domain experts, and cutting-edge technologists driving innovation.
Our Expertise
The same engineering depth that builds FluxAI — from Gen AI to ERP
GenAI & Agentic AI
Autonomous agents and LLM applications that decide and act — not just answer.
Machine Learning & Analytics
Forecasting, predictive & anomaly-detection models that turn history into foresight.
Core Process Automation
An intelligent layer over your operations that removes manual, repetitive work.
Our Solution
Meet FluxAI — an autonomous intelligence layer for manufacturing & operations
FluxAI connects your ERP, manufacturing, inventory, quality, procurement, distributor and sales data to answer three business-critical questions:
What is likely to occur?
Which demand, quality or operational risk is likely to occur across your plants and product lines.
When will it impact us?
When it will hit production, product availability, delivery and profitability — weeks ahead.
What action to take?
What to do to maximise throughput, product quality, inventory efficiency and business performance.
Where We Left Off
Quick recap of our first conversation, Rishi
What You Told Us
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Forecasting & planning lives in Excel
Manufacturing demand is planned on spreadsheets with hand-maintained formulas the team must keep re-tuning.
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It doesn't capture past-year trends
The formulas miss seasonality and history — so the forecast is always a step behind reality.
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Stock vs requirement vs stock-out isn't streamlined
Tying what's in stock to what's required, and seeing stock-outs early, is still largely manual.
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You want to move to something adaptive
A system that learns, instead of formulas someone edits by hand.
What We Aligned On
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See it running on your kind of setup
A live walkthrough, not a deck — today you see the platform in action.
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On-premises, on your data, under your control
FluxAI sits on top of your existing ERP/Excel sources — no rip-and-replace, no data leaving your walls.
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Start with forecasting & inventory
The exact pains you named — demand forecasting and stock-out intelligence — first.
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Prove on one division → scale to all five
A focused pilot, then roll out across divisions at your pace.
Our Vision for Baidyanath
One adaptive operating brain across demand, inventory & sourcing
Adaptive Demand Forecasting
Learns years of sales history, seasonality and festival timing to forecast SKU-level demand — and re-learns every time new data lands. No more hand-edited Excel formulas.
Inventory & Stock-Out Intelligence
One live view tying stock-on-hand to requirement, flagging stock-outs before they happen and recommending what to make or buy — freeing cash from the 115-day inventory cycle.
AI Operations Assistant
Ask in plain English — "Which products will stock out before winter?" — and get an instant, sourced answer from your sales, production and inventory data. Your team keeps working the way they think.
Procurement & Seasonal Risk
Herb-aware planning: anticipates raw-material needs against harvest windows and price volatility, so sourcing for Amla, Ashwagandha, Giloy & more is planned, not opportunistic.
How FluxAI Works
Sits on top of your stack — then discovers, decides, builds & deploys, autonomously
SAP / Tally
Planning sheets
MES / batch
Stock & WIP
Herbs & PO
1,500+ sales
QA / GMP
Connect & Unify
Taps every distributed source and fuses them into one operational truth.
Discover Trends
Mines years of history for seasonality, festivals & hidden patterns.
Decision Intelligence
Autonomously designs the ML pipeline — no data scientist required.
AutoML: Build & Train
Generates, tests & trains candidates — picks the best model.
Deploy & Serve
Pushes trained models live to power every use case below.
Use Case 1: Adaptive Demand Forecasting
Chyawanprash, FY25–26 — FluxAI vs your current Excel forecast
Last Oct–Dec, real demand jumped +58% for the cold season. The static formula under-forecast by 22% → stock-outs at peak.
Begin Chyawanprash build-up by 2nd week Sept; secure Amla & honey now. Projected to capture ₹1.4 Cr of peak demand previously lost.
Use Case 2: Inventory & Stock-Out Intelligence
Stock on-hand ↔ requirement ↔ projected stock-out, in one live view
| Product (SKU) | On Hand | Req. (30d) | Projected Stock-Out | Status | FluxAI Action |
|---|
Use Case 3: AI Operations Assistant
Ask in plain English. Get instant, sourced answers.
3 products are at risk before the Oct–Dec cold season:
- Chyawanprash 1kg — stock-out ~28 Sep at forecast demand
- Giloy Ghanvati — stock-out ~11 Oct
- Baidyanath Honey 500g — tight, ~12 days cover at peak
Recommended: start Chyawanprash production build-up now and pre-secure Amla & honey — lead times tighten in monsoon.
Three reasons your spreadsheet under-forecast by ~22%:
- It used a flat YoY growth %, so it flattened the seasonal peak
- Diwali fell earlier — demand pulled forward, but the formula assumed fixed months
- Post-COVID immunity demand wasn't in the manual trend line
FluxAI models all three automatically — and adjusts as this year's signals come in.
Executive Dashboard
360° visibility for leadership — live, across divisions
A fully interactive version of this dashboard ships with the pilot — open Baidyanath_FluxAI_Dashboard.html for the live demo.
Why Now
The category leaders are already moving from spreadsheets to AI
Excel Planning vs FluxAI
| Capability | Excel Today | With FluxAI |
|---|---|---|
| Forecast basis | Hand-edited formulas | Self-learning models |
| Seasonality | Often missed | Auto-detected |
| Stock-out warning | After it happens | Weeks ahead |
| Inventory days | ~115 days | Target ~86 days |
| Cross-division view | Siloed sheets | One live truth |
| "Why?" analysis | Hours of digging | Instant, in plain English |
The competitive signal
Dabur is already running multiple AI/ML supply-chain initiatives on a master-data foundation and is targeting predictive & prescriptive analytics. Patanjali, Himalaya and Emami/Zandu are investing in the same direction.
Baidyanath's 100+ years of demand history is a moat — but only once it's turned into a model. That's exactly what FluxAI does.
What We're Proposing
Prove it on one division & one product family — before scaling to five
One division (e.g. Nagpur) + your seasonal hero range — Chyawanprash & immunity line. We connect your existing ERP/Excel sources, on-prem.
FluxAI forecasts run live side-by-side with your Excel. You judge accuracy, stock-out warnings and freed cash with your own numbers.
Convinced? Roll out across divisions and into procurement & QA at your own pace — same proven platform.
Recommended Pilot
Best Starting PointForecasting + Inventory on 1 division
- Adaptive demand forecasts for the seasonal hero range
- Live stock-vs-requirement & stock-out alerts
- Plain-language AI assistant for the planning team
What we need from you
- 3–5 years of sales / production history (Excel or ERP export is fine)
- Read access to current stock & requirement data for one division
- One planning-team point of contact to validate outputs
Your Investment, De-Risked
Prove the value on one division — before you commit to the group
Data Assessment
Short, free review of your history & sources to confirm scope and lock a fixed price.
Pilot Build
We connect sources, train models, and stand up the cockpit & assistant on-prem.
Measured Results
Accuracy, stock-outs avoided & cash freed — measured against your own baseline.
Indicative scope — final pilot price confirmed after the data assessment. Group rollout priced per division, improving with scale.
From Pilot to Group Scale
The path we'll walk with your IT team — scope, timeline, cost, then every product & category
Free, ~1 week. Read-only look at SAP S/4HANA + Excel history to lock scope & a fixed price.
Forecasting + inventory on your seasonal hero range, live side-by-side with Excel. Fixed-fee.
Every product & category across 5 divisions, plus MES, procurement & QA use cases.
- One division
e.g. Nagpur — seasonal hero range (Chyawanprash + immunity line)
- Two use cases
Adaptive demand forecasting + stock-out intelligence, with the AI assistant
- SAP S/4HANA, on-prem
Read-only taps in your AWS/Azure/GCP — data never leaves your environment
No long-term lock-in. Price confirmed after the free data assessment.
- Division by division
Roll the proven platform out to all 5 divisions — same models, one live truth
- Every category
Chyawanprash, oils, churna, syrups & OTC/FMCG — full SKU coverage
- New use cases
MES (scrap, downtime, throughput), procurement & seasonal risk, QA/GMP
- Better economics with scale
Priced per division — unit cost improves as you expand
Commercial Model
A low-risk start, then predictable, subscription-led scale — priced per division, every SKU included
Data Assessment
~1 week, read-only across SAP S/4HANA. Locks scope & a fixed price before you commit.
Pilot — 1 Division
Two use cases, 6–8 weeks. Paid pilot — final figure set by implementation effort after the free assessment.
Production Rollout
All SKUs & categories in the division, RBAC dashboards. AMC covers support, retraining & updates.
Per Use-Case
Extend into sales projection, MRP, scheme intelligence & more — priced by complexity of each use case.
What annual AMC covers
Cloud infrastructure is billed directly by your provider (your opex) — not marked up by us. Your data never leaves your environment.
Indicative ranges — final fixed price confirmed after the free data assessment. No hardware required for data use cases; runs in your own cloud. Scale division-by-division: prove the pilot → all categories in one division → all 5 divisions.
Next Steps
Let's move forward together
Ready to retire the Excel forecast?
NDA & Contract Execution
Confidentiality and pilot agreement signed — so we can begin on your data
Pilot Kickoff & Success
Live on one division in 6–8 weeks — proven against your own baseline
Scale Across Divisions
Roll out to all five divisions & new use cases, at your pace
