Your judgment,
on every run.
EI Flow learns your standard from your data alone, then applies it to every result, clearing routine review and bringing you only the calls that need your attention. You stay in control, with more time for the science only you can do.
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 to review. Everything else clears on its own, so nothing routine 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.
Generate custom,
audit-ready reports.
EI Flow generates an audit-ready report in your lab’s format for scientist approval and sign-off. Build the template once, and every run follows the format your lab and reviewers expect. The finished result routes directly to your LIMS or, through MCP, to other connected systems.
Every result, fully traceable.
Every result carries its own record: the data, the model's call and its confidence, any scientist adjustment, the SOP, and the model version behind it. Nothing is reconstructed after the fact. A reviewer or an auditor can see, and replay, exactly why any decision was reached.
You control the model.
Every adjustment and correction becomes training data. In AI Creator, you curate those examples, resolve inconsistencies, and shape each training session, with a clear record of exactly how the model changed. You approve every new version and can roll back or adjust it before deployment.
The analytical workflow, automated end to end.
99.9% Accuracy.
Zero Hallucinations.
Analysis in Minutes,
Not Months
50×
Faster Review
Batch review cycles are significantly shortened while maintaining rigor and traceability.
Analyst-Validated,
Production-Proven.
7×
Increased Accuracy
Exception-only review reduces errors by eliminating repetitive manual decisions.
Actionable Results,
In Real Time.
10×
Faster Time to Value
Operational impact is achieved in days, by turning instrument data into lab intelligence.
The methods your lab already runs, automated.
EI Flow automates the review across all of them, turning raw instrument data into a SOP-aware, signed, explainable result. Your acceptance criteria, your method, your sign-off, on every one.
HPLC / UPLC
Release, purity, potency, and stability.
EI Flow handles peak review, integration, and specification checks across routine release and stability workflows, automatically clearing conforming results and surfacing new or growing degradants before they reach your queue.
LC-MS / LC-MS/MS / MRM
Bioanalysis, identity, and quantitation.
Run acceptance, quantitation review, metabolite tracking, stability, and impurity monitoring across every injection, with your integration and acceptance decisions applied consistently at every site, including your CROs.
Titer / Potency / Binding Assays
Process, release, and development.
Automated curve fitting and endpoint determination where applicable, with assay acceptance across each method. Out-of-trend potency is flagged while final judgment on higher-variability functional assays stays with your experts.
iCIEF / CE / CE-SDS / SEC
Charge variant, size variant, and aggregation.
Peak identification, integration, and system suitability for biologics release, learning your charge and size calls to clear routine review, and pairing with EI Signal to catch aggregation trends across stability timepoints earlier.
GC-MS / HRMS
Impurity, residual, and identity confirmation.
EI Flow applies your validated acceptance criteria, clears conforming results for the report, and flags out-of-spec findings, closing the detect-to-identify loop with the traceability and consistency regulated workflows demand.
Intact Mass / ADC / AS-MS
Mass confirmation, proteoform, and DAR.
Deconvolution, proteoform analysis, DAR calculation, and microheterogeneity assessment, with consistent, signed interpretation that reads the same from lot to lot.
Glycan Analysis
CMC, comparability, and characterization.
Isomer resolution, ambiguity management, and consistent annotation at scale, monitoring the critical glycan attributes that influence effector function, half-life, and immunogenicity across batches and sites.
Proteomics / Biomarkers / Lipidomics
Mechanism, translational, and clinical evidence.
Protein-, lipid-, and pathway-level confidence through alignment, interference control, and reproducible cross-batch identification, supporting the translational and clinical evidence your programs depend on.
Samples Processed
1,237,930
In production, across live pharma labs, and counting.
Case Study
Case Study
Case Study
Frequently asked questions.
-
No. You choose the level of automation. Teams can begin by reviewing every result, then allow validated routine calls to clear automatically as confidence grows. Exceptions, report approval, and sign-off remain under your control, leaving you more time for investigations and scientific decisions.
-
Your scientists train EI Flow on 30 to 50 reviewed examples showing how your lab processes and validates its data. In AI Creator, they curate examples, resolve inconsistencies, and approve every model version before deployment. Corrections can be added to future training, but they never change the model without your team’s review and approval. Previous versions remain available to roll back.
-
You benchmark EI Flow against your own reviewed work before production. Every call remains connected to the source data, model decision, confidence, and model version. Anything outside the model’s confidence is brought to you rather than hidden or guessed.
-
You do. EI Flow learns from your scientists' reviewed work and nothing else, not from other customers' data and not from public sources. Your data stays in the deployment environment you approve, is never mixed with another customer's data, and remains your institutional IP. The models your scientists build from it are yours as well, including every version and the record of how it changed.
-
A careful manual review runs about 98% accurate on a defined analytical decision. EI Flow learns that same decision from your scientists' reviewed work and fine tunes to 99.9%. What it isn't sure about goes back to a scientist.
-
EI Flow’s analytical decisions are deterministic, not generative. It does not invent a result when data is missing, unfamiliar, or uncertain. Those cases are flagged and routed to a scientist.
-
Meaningful changes can move data outside the model’s validated boundaries and trigger review. Your team can add curated examples, retrain the model, compare performance, and approve a new controlled version. The previous model and its decisions remain traceable.
-
EI Flow is vendor-agnostic and method-flexible. A workflow can be evaluated for automation when it produces interpretable instrument data and applies repeatable scientific decisions. That includes established QC methods and repeatable analysis within method development, while scientists continue to design the experiments and methods.
-
EI Flow gives EI Signal a continuous stream of governed, traceable decisions from each run. EI Signal follows those decisions across time, methods, workflows, and labs to reveal drift and relationships no single run can show. Together, they move you from automating individual reviews to understanding how the wider analytical system is changing.
-
Each result remains linked to its source data, model decision and confidence, scientist adjustments, applicable SOP, and model version. Controlled access, audit trails, electronic records, validation, and change control support data integrity and regulated workflows.
See it
in action.
Request a demo and see how trusted results compress the time and cost of drug development.
