Automate the wet lab.

Un-silo the decision.

In silico design moves in seconds. Proving it true still takes years, because analytical review gates every decision and does not scale. EI automates that review and weaves your labs into one intelligence fabric, compressing development time and making every decision better informed.

Purpose-built for the wet lab, from the architecture up.

EI-LSM, EI's Limited Sample Model, is the foundational model the whole platform runs on. Its architecture is shaped by the lab itself, purpose-built to be accurate, safe, and sovereign in a regulated environment from the start.

Outcomes

Reads raw instrument data.

It learns directly from the chromatograms, spectra, and plate data your instruments produce. That is the language of the lab, so the model reasons about your results at the level of the actual signal, the way your analysts do.

Learns from expert judgment.

It trains on your scientists' own reviewed decisions, not a generic average, so it encodes how your lab reads its data. The judgment your experts built over years becomes something the model applies on every run.

Domain specific.

Built for the lab and nothing else, it reaches expert accuracy from 30 to 50 of your own reviewed examples, not the thousands a general model would demand. That is where the Limited Sample name comes from.

Review by exception.

Confidence is built into the architecture, not bolted on. Conforming results clear on their own, and any uncertain call routes to a scientist rather than being guessed. It does not fabricate.

Trained only on your data.

Each model is built in house on your data alone, so your methods stay your institutional IP. Every version is recorded and traceable to the results it produced, ready for any reviewer or audit.

The bottleneck,
cleared end to end.

Review at machine speed.
Data is processed, validated, and quantified in minutes, not the hours or days of manual review the pipeline waits on. The review stops gating the line.

Fewer failed runs.
EI catches a bad prep and reruns it corrected at the source, before it costs you a batch, a reagent budget, or a place in the schedule.

Right the first time.
Calibration and QC clear up front, so instrument time goes to runs worth keeping and drift is caught early, not three hundred injections later.

Judgment where it counts.
Your experts review only the exceptions, and every call further refines the model. This is the closed loop, judgment that improves every stage upstream.

Audit-ready by default.
Every result ships signed, governed, and traceable to the sample, straight into your systems of record. Quality is built in, not reconstructed at inspection.

Fewer failed runs.
EI catches a bad prep and reruns it corrected at the source, before it costs you a batch, a reagent budget, or a place in the schedule.

One platform. Every lab, unified.

Three layers turn isolated labs into oe connected system, so the truth your labs produce stops living in silos.

Every method runs on EI Flow, where your in-house experts train the model and review by exception. Each lab produces trusted, validated results.

Oversight.

Those results flow into one unified data lake, cleaner and more precise, where EI Signal reads across every lab for portfolio-level foresight. From a signal at the top, you drill to the sample that drove it, governed by privilege.

Action.

Shared, multi-user access makes the network one platform for method transfer, data exchange, exception review, and documentation.

Decisions.

“Every long-term study is now watched as it runs. We catch a trend forming and act months before it could become a warning letter...”

— Head of Quality, world's largest CDMO

Case Study

Frequently asked questions.

Yes. Every result carries a full audit trail: the data, the decision, the confidence, any human review, the SOP, and the exact model version that produced it. Nothing is reconstructed after the fact. A reviewer or an inspector can see, and replay, why any decision was reached.
No. EI trains only on your data, it stays inside your environment, and it is never mixed with outside data or shared across customers. Your methods and your data remain your institutional IP.
No. EI automates the routine review that does not scale with headcount and routes the exceptions to your experts. Your scientists move from clearing queues to the judgment calls only they can make, and the platform learns from every one.
EI reaches expert accuracy on a method from 30 to 50 of your own reviewed examples, not thousands, so a first workflow can be in production in weeks, not quarters. Rollout is sequenced by value, starting with the highest-volume release methods.
Yes. EI ingests data from the instruments you already run and pushes signed results into your LIMS and systems of record. It sits on top of your lab, not in place of it.
Your scientists add examples and the model relearns. You control the rules and the thresholds; the platform follows them and records every version.
Yes. The intelligence fabric is governed by privilege end to end. A leader can drill from a portfolio-level signal down to the individual sample that drove it, with access controlled at every layer.