Verticals Life sciences
Life sciences on one shared ontology
Sequencing labs, diagnostic networks, discovery teams, and manufacturing quality don’t share systems — but they must share meaning. Zenith turns samples, runs, QC, and obligations into verified decisions — then software that executes.
The problem
Life sciences AI fails when every site keeps a private picture of the same sample
LIMS projects digitize one lab. Dashboards chart one feed. Agents encode one team’s SOP. Turnaround still breaks at the seams — fragmented schemas, tribal pooling knowledge, and report chains that never became a shared model.
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Shared ontology
Samples, specimens, orders, libraries, runs, instruments, QC results, analyses, and reports become first-class entities — the same meaning across sites, LIMS instances, and handoffs.
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Deterministic policy
Intake rules, QC thresholds, SLA clocks, pooling constraints, and release criteria become measurable gates. Unverified drafts never weigh the same as asserted knowledge.
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Verify, then execute
Model a pooling plan, capacity shift, retest, or report change against graded knowledge before it hits LIMS, sequencers, or customer delivery. Humans approve evolution of the operating rules.
Who we build for
Same platform. Different operating reality
One ontology, tuned per domain — from high-throughput sequencing floors to regulated manufacturing and multi-site control towers.
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Genomics & sequencing operations
Intake, library prep, multiplexing, sequencing, analysis, and reporting span fragmented LIMS, instruments, and tribal playbooks — TAT slips where the seams do.
- → Ontology across sample lifecycle, library, pool, run, QC, and report
- → Policy packs for SLA, coverage, index constraints, and release gates
- → Human-in-the-loop recommendations for pooling and exceptions — with evidence retained
One meaning of the sample from accession to delivery — not a private picture per desk.
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Diagnostic & clinical lab networks
Multi-site labs run different schemas, status definitions, and exception paths — so “where is this stuck?” becomes a scavenger hunt across systems.
- → Shared sample and order identity across sites and referral partners
- → E2E lineage from accession through assay, QC, and result release
- → Control-tower views for TAT, backlog, and QC failure patterns
Network visibility without forcing every site onto one brittle LIMS cutover.
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Biopharma R&D & discovery
ELN, LIMS, instruments, and assay pipelines produce data without durable context — so reproducibility and reuse stall when people leave the project.
- → Link experiment, material, method, instrument, and outcome in one graph
- → Capture decision context alongside results — not just raw values
- → Reuse protocols and exceptions as institutional knowledge, not tribal memory
A living model of how discovery work actually runs — ready for agents and audit.
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CDMO, CMC & quality operations
Batch release, deviations, and method transfers still stitch MES, LIMS, QMS, and email — while regulators expect lineage, not reconstruction.
- → Ontology of batch, material, process step, deviation, and disposition
- → Policy packs for release criteria and change control that can be simulated first
- → Traceability from release decision back to the evidence that justified it
Faster release and clearer audit trails without rip-and-replace of every adjacent system.
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Cell & gene therapy workflows
Patient-specific and lot-sensitive chains multiply handoffs — identity, chain of custody, and process deviations leave no room for ambiguous state.
- → End-to-end identity across donor/patient material, process steps, and release
- → Guardrails for scheduling, capacity, and exception escalation
- → Replayable lineage when a deviation or inquiry needs the real timeline
Operational coherence as volume and complexity grow — without betting on hero operators.
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Multi-site control towers
Global operators see local dashboards, not a shared operating model — so capacity, QC, and customer commitments never reconcile in one place.
- → Unify regional LIMS/ERP sample and order signals into one foundation
- → TAT, QC, and SLA alerts on a shared ontology — not another BI slide
- → Natural-language ops questions against governed objects and lineage
A control tower that answers “why” — not only “what happened yesterday.”
Capabilities
From intake foundation to lineage you can defend
The same progression shows up across life sciences estates: unify sample meaning, encode operating judgment, then close the loop from analysis to customer-ready output.
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Sample & order data foundation
Normalize intake from mixed customer formats — spreadsheets, PDFs, emails, portal dumps — into a shared sample schema. Detect missing fields, unit mismatches, and duplicates early. People review exceptions; clean work registers without retyping.
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Ops optimization with evidence
Turn pooling, scheduling, and capacity know-how into recommendable policy — quality, cost, and TAT in one decision. Senior operators approve; the system keeps the rationale so the next shift inherits the model.
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Analysis-to-report & E2E lineage
Connect sample → library → run → analysis → QC → report as one chain. Automate repeatable reporting templates; reverse-trace any customer deliverable to the run, instrument, and accession that produced it.
Engagements
From high-volume benches to capital-scale programs
Attractive work lives on both sides: operators who need TAT and accuracy now, and enterprises running multi-year modernization. Same ontology layer — different engagement shape.
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Lab & sequencing throughput programs
High-volume accession, prep, and reporting desks where TAT and accuracy are the product — ontology + guarded automation on the workflows you already run.
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Multi-site integration foundations
Global or multi-LIMS estates where the hard part is shared meaning — sample identity, status, and QC — before another integration map.
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Regulated ops & modernization
CMC, quality, and therapy manufacturing programs that need lineage and policy you can defend — encode how work actually runs before rewriting every adjacent system.
Position
The layer under the tools the market already sells
LIMS suites, ELN platforms, and lab automation each solve a slice. Automata is the ontology and policy environment those slices need to stay consistent across sites, instruments, and obligations.
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Not only another LIMS project
Replacing LIMS rarely fixes fragmented meaning across sites, instruments, and partners. We layer a shared ontology and policy environment on the stack you already run — then automate where the model is strong.
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Not only a dashboard or data lake
Charts without governed objects still leave “where is this stuck?” unanswered. Lineage and policy on an operating graph turn monitoring into decisions you can execute.
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Not only unconstrained agents
Agents that copy SOPs speed a team up. Ontology makes those SOPs shared, auditable, and safe to automate across benches, sites, and customer commitments.
Next
Bring a lab, a network, or a regulated chain
We’ll map the entities and constraints that already run your operation — then show where ontology, policy packs, and simulation remove the next year of one-off work. Start with a conversation; leave with a scoped outline.