Prepare the data. Connect the evidence. Reason in your domain. Three components work together to bring pharmaceutical context to every answer.
Your knowledge stays yours. Client data is retrieved at query time, never used to train the model.
01 / THE DATA FOUNDATION
Stream prepares scientific and operational data so it can be used reliably by AI.
The same compound can have different measured values because the experimental procedures differ. Stream retains that context while bringing databases, written records and scanned documents into one preparation process.
01 / SOURCE MATERIAL
Same compound. Different procedures.
Methods and experimental context.
Evidence outside the database.
02 / STREAM
03 / READY TO USE
TraceableGroups measurements according to the experimental procedure that produced them, then selects the value supported by the strongest body of evidence rather than averaging conflicting results.
Standardises structures and units, resolves duplicates, and applies plausibility rules.
Brings information from scanned documents and written records into the preparation process.
Keeps closely related molecules from appearing on both sides of a training and test split, helping prevent artificially inflated model performance.
Every dataset leaves Stream with a data card showing its origin, preparation, test split, and version.
02 / THE ORGANISATIONAL MEMORY
Lattice turns prepared data into searchable organisational knowledge, connecting events, causes, actions and verification.
Meaning-based retrieval finds relevant records, even when the wording or language differs. A knowledge graph connects those records to the history and relationships around them.
01 / RETRIEVE BY MEANING
Deviation record
Related investigation
Comparable event
Previous case
02 / CONNECT THE CONTEXT
Follow the relationships across records to understand the pattern behind individual events.
Knowledge graph + meaning-based retrieval03 / EVIDENCE IN CONTEXT
What the investigation identified
What was done in response
How effectiveness was checked
Four deviations may represent four separate events, but they can also point to one recurring cause. Lattice keeps these relationships connected, making recurring problems easier to identify.
At query time, meaning-based search finds the relevant entry points and the knowledge graph is followed outward to assemble the surrounding historical and causal context. That evidence is what Elyndra receives.
03 / THE PHARMACEUTICAL LLM
Elyndra applies pharmaceutical reasoning to evidence retrieved from Lattice. Your organisational facts remain in your knowledge system.
Elyndra 1.0 supports quality and manufacturing work: pharmaceutical terminology, GxP, ALCOA+, root cause analysis and supporting statistical methods. It works in English and Bahasa Indonesia.
01 / EVIDENCE FROM LATTICE
The original event and its context.
Related causes, actions and outcomes.
References attached to the question.
02 / ELYNDRA 1.0
03 / STRUCTURED OUTPUT
DraftProblem & context [1]
Comparable case history [2]
Supporting rationale [3]
A named person accepts, edits or replaces the suggestion.
Applies pharmaceutical terminology and methodology to structure problems and support investigation.
Where a method has to be chosen, the user chooses it. Elyndra supports the choice rather than making it.
Client data is used at query time and is not used to train the model.
Relevant information is retrieved from your organisational knowledge and provided with supporting evidence.
AI can support investigation, analysis, drafting, and review. The final decision stays with the named accountable person.
EXTEND WHEN YOU NEED TO
Most clients begin with one workflow and one or two components. Additional models can be introduced when they solve a defined problem.
THE REASONING LAYER
Domain knowledge at the centre of the workflow.
Choose the models that answer your next question.
Start with the problem. Add models when the workflow needs them.
Explore the workflowsTHE SCIENCE BEHIND THE SYSTEM
Published methods, reproducible work, and evaluation designed for pharmaceutical tasks.
Multiscale Discovery of MDM2 Inhibitors
Akmal, M.F. and Wong, M.W.
Read the publicationREADY FOR REGULATED WORK
Where a system touches regulated records, validation runs alongside the build. Decisions stay with a named, accountable person.
ALONGSIDE THE BUILD
Specification, qualification, traceability and reporting — done during the build, not after.
Audit trails, access control and e-signatures from day one, not bolted on before an inspection.
Every source kept, and every accepted, edited or replaced suggestion recorded against a named person (ALCOA+).
It may read, retrieve, draft, score, flag and cite. It may not approve, sign, release or decide.
A dedicated in-country environment, or entirely inside your infrastructure.
Tell us what you are trying to improve. We can show you which components are relevant and what evidence supports the approach.
Bring your challenge.
Let’s find the right next step.