Material trend analysis
Spot parameter drift and patterns across samples, batches and time that are invisible while reports sit disconnected from each other.
Use case · Intelligent Document Agent"Which samples failed pH specification in the last 30 days?"
Analytical Lab Reports
Lab reports already hold the answers. But test results for multiple samples and batches, spread across test methods and mixed between tables and narrative text, are exactly the shape of data that generic AI cannot read.
The problem
Why standard RAG fails on lab reports.
A lab report is not prose and it is not a clean table - it is both, interleaved. Standard retrieval can find a document that mentions a parameter. It cannot reliably link that parameter to the test method that produced it, the sample it came from, or the batch that sample belongs to. And it cannot filter by batch number or parameter value.
So the analyst does it by hand: open the report, find the table, re-key the value, repeat. Hours of lookup for a question that should take seconds.
Prove us wrong ;-)The approach
A custom parsing agent, not generic retrieval.
The Lab Agent identifies samples and batches, detects test methods, extracts parameters from both tables and narrative text, maps every relationship explicitly, and stores the result as structure - not as a blob of text.
ParameterTest MethodSampleBatch
These relationships are the product. Once they are explicit, the questions below stop being research projects.
Real demo queries
Questions it answers today in multiple labs, with source backed answers.
These are queries run against real deployments - not mock-ups.
- "Which reports contain Sodium?"
- "Which reports contain Sodium and Chloride?"
- "I am getting complaints about sample order 010002838482 - what test types were run on it in 2023?"
- "Give me the value of Linolenic acid, Margaric acid and Stearic acid for batch p021508801"
- "Which samples failed pH specification in the last 30 days?"
- "List all reports where heavy metal content exceeded 0.1 ppm"
What it unlocks
Structure changes what you can ask.
Better NPD
Structured historical test data speeds up formulation and reduces redundant testing.
Root cause analysis
Parameter → Test Method → Sample → Batch relationships make it possible to trace a deviation back to its source.
Faster, more accurate decisions
Answers pulled live from structured lab data, instead of manual report review.
Nuance and tribal knowledge
Captures the informal know-how - why a result was flagged, what a borderline value means in context - that usually lives only with senior analysts.
Niche terminology and validation
Built for the test methods, parameter naming and validation logic of analytical chemistry - not generic document QA.
On-demand, personalized answers
Any analyst or scientist gets a precise answer in plain language, scoped to their query, without waiting on a specialist.
Who it's for
Built for the people who live in the data.
Where it runs
Entirely inside your infrastructure. Reports, results and batch data never leave your boundary - which is what makes it deployable in a regulated lab at all.
Don't take our word for it.
Take the dataset, run the same queries in the AI tools you already use, and try to prove us wrong. No form, no gate.
On-prem. Your data never leaves your boundary.