Your judgment,
on every run.

99.9% accurate.
Zero hallucinations.

EI Flow learns how you read your data and applies it to every batch. It clears the routine and brings you the calls that need real judgment, so your expertise reaches every run and your time goes to the science only you can do. You set the rules.

Review by exception

Instrument data is ingested, processed, and validated automatically, against your SOP and your thresholds, in about five minutes to a generated report. Anything outside the model's confidence is raised in the dashboard for you. Nothing else asks for your time.

Investigate and adjust

When something is flagged, EI Flow gives you native tools to look into it fast and make the call. Apply a judgment or an adjustment across the whole batch, or mark it to train the model. You decide, in place, on your own data.

The signed result

Audit trail

Every result carries its own record: the data, the model's call and the confidence behind it, any review or adjustment a scientist made, the SOP it ran against, and the model version that produced it. Nothing is reconstructed after the fact. Because the model keeps learning, the trail ties each result to the exact version that made the call, so a reviewer or an auditor can see, and replay, why any decision was reached.

Active learning

Every adjustment and correction you make becomes training data. The model creator curates those examples, resolves inconsistencies, and sharpens a training session with further instruction. Each session shows exactly how the model changed, so you approve a new model version with eyes open, or roll back and adjust the training. The model only ever learns what your scientists decide to teach it.

Frequently asked questions.

EI is scientist-trained AI for regulated laboratories. Your scientists train it on their own reviewed decisions, and it applies that judgment to every run: signed, audit-ready results at 99.9% accuracy. [See how it works →]
No. EI is trained by your scientists, governed by them, and signs nothing without them. It clears the routine review so their judgment reaches every run. Scientists move up, not out.
Most AI reasons on top of decisions the lab already made. EI starts underneath, at the instrument data, with EI-LSM: a foundational model for the lab that learns from 30 to 50 of your experts' reviewed examples and routes uncertain calls to them instead of guessing. It is not a large language model. [See how it works →]
EI Flow automates the batch: instrument data to signed, regulatory-ready results in minutes, pushed to your LIMS. EI Signal reads across every instrument, method, and site to surface drift and risk no single batch can see. Two products, one system. [Explore EI Flow →] [Explore EI Signal →]
Every decision is traceable, explainable, and replayable, with the expert decision behind it on record. Routine runs clear automatically; exceptions route to your experts and carry their signature. [See how EI Flow signs results →]
Nowhere. EI trains only on your data, it never leaves your walls, and it never mixes with outside data or shared models. The model your scientists build is your institutional IP.
Production-grade from 30 to 50 expert-reviewed examples, which typically means 10x faster time to value than conventional approaches. The fastest way to gauge fit is a demo. [Request a demo →]

Your judgment, on every run.

99.9% accurate. Zero hallucinations.