Predict behavioral change before it shows in your data
CentroidAI identifies which patients, accounts, batches or channels are about to change — months before traditional analytics notice. Every prediction carries specific actions, a lead time, a confidence level and a dollar impact calculated from your own cost configuration.
What the engine sees first
Four products. One behavioral engine.
The same engine reads behavior in a different setting each time — a patient panel, a bioreactor, a hospital account, a distribution channel. What changes is the data it reads and what a missed signal costs.
FyndEm Shield
Counterfeiting is fought with takedowns and remembered as incidents. Shield predicts where it is forming, measures whether your packaging defenses are still holding, and assembles the evidence — so the work your team prevents is finally on the record.
- Predict: where a signature is forming, by product and market, with a lead time
- Prevent: feature-level evidence on what deters and what only costs
- Prove: triaged findings, the actors behind them, and case packages that hold up
- Built from your own data, inside your own environment
FyndEm Quality
Predicts batch deviations from the data your PI, PAS-X, LIMS and MES already collect — and prices every one against your own facility rates. Four builds: biologics, plasma fractionation, solid dose and corrugated.
- Deviations predicted before release testing
- Cost economics per deviation, per batch, per plant
- Audit-defensible CAPA narratives for your eQMS
- Biologics build runs on SAP BTP
FyndEm Clinical
Disease prediction, years ahead of diagnosis, read from the record a health system already keeps. Our first target condition is Parkinson’s disease.
- Risk surfaced while intervention is still feasible
- Behavioral signal where the clinical record goes quiet
- Converge: the research loop, run in minutes rather than months
- Stratification for outreach and research, not a diagnostic
FyndEm Account
Detects hospital account defection three to six months early from ERP order history — and separates the accounts worth defending from the ones ready to expand.
- Defection risk with a lead time, not a lagging report
- Expansion candidates ranked by behavior, not past volume
- Revenue at risk quantified per account
- No IT integration required to start
From your existing data to a ranked action list
No data warehouse migration, no new sensors, and no data science team on your side.
Send a standard export
From your EHR, ERP, MES, LIMS or CRM. Secure upload, SFTP or API. On-premise deployment available for regulated environments.
The engine builds profiles
Behavioral profiles are derived without tagging or manual labeling. You configure the cost assumptions that turn a signal into a dollar figure.
You receive ranked outputs
Prioritized lists with the reasoning behind every score — the signals that fired, when they started, and what acting is worth.
Act, and measure against baseline
Results are tracked against your starting point. The model updates as new data arrives.
Read our white papers
Each paper sizes the problem from data you already file or already collect — no product tour, no form, and no obligation to speak to anyone. Read any of them in the browser, or take the PDF.
You Are About to Lose Your Best People Twice
Two departures at once, one gap: the retirement everybody forecasts, and the one nobody does. Why the plant’s leadership ladder is the thing actually at risk.
The Signal You Already Own
A field guide for finance and operations leaders: how four separate-looking losses share one cause, and how to size yours from HRSA UDS or your CMS Cost Report.
Yield, and What a Deviation Really Costs
What a batch deviation really costs once every driver is counted — and why finance sees the total only after the quarter has closed.
Comparability Under Pressure
Predicting deviations against innovator-equivalent gold standards, where comparability margins are tight and a failed campaign costs a filing window.
Save the Cycle. Improve Yield.
Cohn-fraction yield, chromatography selectivity and viral-inactivation deviations — and why a pool that fails carries every donation in it.
Converting Line Economics
The same prediction, re-priced for a converting line: substrate waste, downtime and re-run rather than batch yield.
Delivered with partners who already sit inside our customers
Come, Talk to us
Tell us which of the four problems you recognize in your own numbers. We will tell you what our engine would look for, what it would cost to find out, and whether we are the right fit.