Trusted Agents for
Life Sciences
Prometheux helps life-sciences leaders cut cycle times, lower cost and reduce risk across research, clinical development, manufacturing and commercial teams. AI runs on your own data and rules, where they already live, and every answer comes with the rules, rows and references behind it. Same question, same answer. Nothing to migrate.
Proven in production
Plenty of Data.
Few Answers You Can Use.
Research, clinical, plant and commercial data sit in separate systems, internal, partner and public. Every new question becomes a data project, AI pilots stall before production, and the answers that do arrive are hard to explain to the people who must sign off on them.
Where Life-Sciences Teams
Start
Pick the area you run. Each use case starts on your data in place and reuses the same rules and connections for the next, with the evidence behind every answer.
Research & Discovery
Bring siloed chemistry, biology, omics and literature together so scientists can find, reuse and trust what's already known.
Clinical Development
Shorten the time from protocol to insight by connecting trial, site and partner data without moving it.
Manufacturing & Supply
See across plants, suppliers and logistics, anticipate disruption, and trace any lot in either direction.
Commercial & Medical
Give brand, field, access and medical teams shared definitions and a clear view of each account and market.
The Questions Other
Tools Can't Answer
Repurposing With a Route
Which compounds bind a target whose pathway reaches this disease, directly or through pathway crosstalk at any depth, and which assay and reference support each step?
Batch and Lot Genealogy
If a supplier's raw-material lot fails quality, which intermediate and finished lots used it, at any depth, traced through your ERP batch and material tables?
One Compound, Many IDs
Which registry, vendor and public records are the same compound or gene under different identifiers? Every match cites its rule. See entity resolution.
Hubs and Mechanisms
Which targets are bound by the most distinct compounds, and which compounds share a mechanism? Centrality and connected components run on your tables, with no graph database, and every result keeps its lineage.
One Model From
Lab to Market
Typical life-sciences platforms ingest literature and data into their own store and put agents on top. Prometheux sits between your research, manufacturing and commercial data and the teams, tools and agents that use it, and runs the logic where the data already lives.
Analytics
Dashboards, graph and BI
Integrations
Back into the databases behind research, ERP, quality and CRM systems
Agents
Assistants and agents, deterministic answers
Models
Define the ontology, read answers, and power analytics, integrations and agents
Your life-sciences data
Registries, assays, omics, LIMS, ERP batch data, CRM, trials and literature, connected in place. Nothing to migrate.
-
Research to Market
- Target identification, repurposing, batch genealogy, launch readiness and quality reporting on one model
- Agents call deterministic rules over MCP and API, with the derivation behind every answer
- Results written back to the databases behind research, ERP, quality and CRM systems, on a schedule or when data changes
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The Life-Sciences Rules Engine
- One model of compounds, targets, batches, products and accounts, instead of a pipeline per question
- Recursion and graph algorithms: pathway crosstalk and lot genealogy at any depth, target centrality, compounds grouped by shared mechanism
- Same input, same output, with lineage to the rules, source rows and references
-
Cloud Agnostic
- Run in Prometheux cloud, your VPC or your own Kubernetes, under your own controls
- Scale horizontally on clusters as research and operational data grow, with optional GPU acceleration
- 1-click deployment anywhere: EKS, AKS, GKE, Databricks Clusters, Snowpark Containers
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Data Agnostic, Lab to Market
- Read and write registries, assay results, omics files, LIMS, ERP and CRM data in any database, format and schema
- Connect PostgreSQL, MariaDB, Neo4j, object storage, data lakes and warehouses across clouds and on-premises
- No new data store or knowledge-graph copy to migrate into and keep in sync
Same Layers.
Better Built.
Map a typical life-sciences data or AI platform to Prometheux and the difference is where the work happens: in place, in rules you own, with an answer you can check.
Any Question.
No New Pipeline.
A European biopharma's scientists used to export, transform and assemble data from chemistry, biology, genomics and pharmacology repositories, internal and public, for every new question. With Prometheux, each source is connected in place with a declarative rule instead of a custom pipeline. Researchers start from a gene, a disease or a compound, follow drug, target and disease links across every source, find clusters and hub proteins, and check repurposing candidates against a validated set.
Owned, Not Rented
The model and its rules stay with you, in readable form your scientists can review and version, not in a vendor's graph.
Deterministic and Explained
The same question gives the same candidate list, with the rules, source rows and references behind each one.
Built for Biomedical Scale
Rules run as distributed jobs with optional GPU acceleration: 9× faster neurosymbolic reasoning for AstraZeneca drug repurposing, on NVIDIA GPUs.
Portable by Design
Run in Prometheux cloud, your VPC or your own Kubernetes, so research data stays in your environment. See on-premises.
Ask across every repository at once. Show the evidence behind every candidate. Reuse the logic for the next question.
One Question.
Then the Next.
Connect in Place
Point Prometheux at the systems behind one question: registries and assays, LIMS and ERP batch tables, or CRM and market-access data. The data is processed where it lives; nothing is copied.
Codify the Knowledge
Your scientists and analysts, our forward deployed engineers and the AI assistant turn potency thresholds, specifications, SOPs and expert judgement into readable rules in days, not months.
Run One Use Case
Repurposing, lot genealogy, launch readiness or a quality report, on live data, for teams and agents alike.
Expand Use Case by Use Case
Each new source or question reuses the rules and connections already in place, so the next team starts further ahead.
Questions,
Answered
Is this another data platform or knowledge graph to migrate into?
No. Prometheux reads your registries, assay results, LIMS, ERP, CRM and public sources where they already live, across clouds and on-premises, and runs the rules there. The model is logical: there's nothing new to load or keep in sync.
We already have LIMS, quality and CRM systems. Why add Prometheux?
Keep them. Each is strong inside its function. Prometheux runs your logic across them, from research to plant to field, reads from the tools and data you license, and writes results back to the systems your teams already use.
Our science is exploratory. Won't a fixed model slow us down?
The model grows with your questions. Adding a source is one rule, and changing a definition such as a potency threshold is one edit that every downstream result follows. Exploration can stay open; the parts you need to defend run as rules.
Is everything deterministic, including the AI?
The rules are: the same data and rules give the same result, with its derivation. LLM steps, such as drafting a rule or turning a question into a query, are not, which is why your team reviews the rules and every answer links back to them.
How do quality and audit teams review it?
Rules are plain, versioned text your team can read. Every result links to the rules that fired and the source rows they used, and the same input always gives the same output, so a result can be replayed and checked.
Can it run inside our own environment?
Yes. Run it in your VPC, on your own Kubernetes, or inside Snowflake and Databricks. Data is read in place under the access controls you already have, and Prometheux is SOC 2 Type II certified.
What does it cost?
It depends on your data and use cases, so we measure it on yours rather than quote a generic figure. Most teams start with one question, prove the value, then expand.
Related Capabilities
Bring One Question
That Must Hold Up.
Repurposing, lot genealogy, launch readiness or a quality report on your own data. Our forward deployed engineers will run it with you, in place, on your infrastructure.
