AI-driven demand forecasting, inventory & expiry control, cold-chain integrity and a private GenAI assistant — unified on one platform, deployed on-premise, so your data never leaves your walls.
Building solutions for the future — a GenAI product-engineering partner since 2018
HQ & Innovation Hub: Pune, India · Sales Offices: Germany & Austin, TX, USA
Turn reagent spend lost to waste & stockouts into margin
Tests batched, delayed or outsourced — TAT breaches & silent revenue leakage
~10% of consumables expire on the shelf; cash locked in dead stock
Cold-chain breaks & delays hit TAT and patient experience
No appetite for long, invasive AI projects that risk daily output
Patient & diagnostic data under strict governance — cloud is a non-starter
Flux AI is built to remove both blockers from day one
On top of the systems you already run — it does not replace them
Flux AI is Trinesis's on-premise predictive-intelligence platform for diagnostic networks. It connects the data you already have, learns your patterns, and turns them into ranked, early-warning actions — without changing a single workflow. Three things make that possible:
Connects LIMS, inventory, procurement, logistics & reports across every lab into one operating truth
Trains purpose-built ML on your history to forecast demand, flag expiry & stockouts, predict rejection risk — with confidence on every call
Runs inside your infrastructure, read-only. Teams keep working exactly as they do today — nothing to rip out
From "what happened last month" → to "what will happen in the next 10–14 days, and what to do about it."
Five modules, one unified data layer
Per test × analyzer × lab. Learns seasonality & campaign spikes; recommends reorder points & safety stock.
Flags lots before they expire and recommends network redistribution instead of scrap + emergency freight.
One days-of-cover view across every node, with imbalance alerts between labs.
Watches inbound specimen transport for excursions & delays; predicts rejection risk in transit.
Ask your data in plain language; summarize reports; read invoices/POs; draft POs & alerts — role-based & cited.
Every insight arrives as a ranked, explainable action — by ₹ impact & confidence. Decisions, not dashboards.
Measured on a representative diagnostic-network engagement
Figures from an anonymized engagement, cross-checked against published industry benchmarks. Client references available under NDA.
Launch Interactive DashboardBeyond static consumption models & fixed "inventory-days" rules
It's the same idea your team already trusts — order enough to cover the next N days — but with days-of-cover the model learns and adapts, instead of a number set by hand.
Designed for regulated, DPDP-era enterprise healthcare
Inside your own infrastructure / private cloud. No SaaS, no data egress.
Connectors only read source systems. Production is never put at risk.
A phlebotomist, planner & pathologist see different data. Sensitive fields anonymized.
Every model decision & assistant answer is logged, explainable & citable.
Because nothing leaves your walls, a pilot needs far less legal & data-sharing friction than a cloud vendor — your data stays under your governance the entire time. NABL- & DPDP-friendly by design.
Nobody trusts the AI on day one — it's validated before it's relied on
Train on 18–24 months; test on the latest 3 it never saw. Report MAPE, bias & stockout-catch.
6–8 weeks alongside today's process, changing nothing. "Model said X, reality was Y" — weekly, zero risk.
AI vs. the old method on a subset. Success metric agreed up front. Only winners graduate.
The system proposes; your team approves. Nothing is auto-ordered without sign-off.
This is the answer to "what about the 5%": you see the accuracy on your own data, in shadow mode, before anything goes live — and you set the bar it has to beat.
A 4–6 week, on-premise Proof of Concept
On-prem deploy, read-only connectors. Pick 2–3 reagent categories + a metric.
Train on history; run alongside, changing nothing. Report accuracy.
AI vs. current method, like-for-like, human-in-the-loop.
Results vs. baseline in ₹. Clear go / no-go + roadmap.
Reagent demand forecasting + expiry control, at a single reference lab. Lowest data lift, fastest ₹.
Read-only data, on your servers. A few hours of an IT/LIMS admin's time. Workflows unchanged.
If it doesn't beat your current process on the agreed metric, no production risk was taken — and no data ever left the building.
Commercials: the PoC is a fixed-scope, paid engagement. Kick-off begins once we have your internal approval and a purchase order — keeping scope, timeline and deliverables clear and committed on both sides.
From capability overview to a scoped pilot
| Action | Owner | When |
|---|---|---|
| Review this deck | Leadership | This week |
| Pick a candidate use case | Operations | Next 2 weeks |
| Scoping workshop | Both | On agreement |
| Finalize PoC scope & metric | Both | During workshop |
Avinash Mallik
Co-Founder & CEO, Trinesis Technologies
avinash@trinesis.com
+91 70309 99223
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