Now Live in ALTI-Labs on Google’s Gemini Enterprise Platform
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Governed, human-in-the-loop agentic experiences for the workflows that run a life sciences business batch release, deviation and CAPA, HCP engagement, and CSR evidence review built on Alti AIOS™ and Google Cloud, and portable across model platforms.
In life sciences, the hardest problems don’t live inside a single model. They live in the space between a COA and a release decision, between a deviation and its root cause, between a market goal and a compliant field action, between trial evidence and a defensible CSR section. That space is full of documents, controls, fragmented data, and always a human who owns the final call.
Point solutions keep rebuilding that context from scratch. AIOS™ is our answer to why they shouldn’t have to. AI is not a product you buy. It is an operating reality you build and in a regulated industry, it has to be built with governance, traceability, and human review in the fabric, not bolted on after.
Today I’m glad to share that our team has launched Agentic Life Sciences Operations in ALTI-Labs, running on Google’s Gemini Enterprise platform and built on the Alti AIOS™ capability layer: experience, agent orchestration, a connected context graph, tooling and integration, human-in-the-loop / GxP workflow, evaluation, observability, governance, and security. These are specific business processes rebuilt as governed agentic experiences grounded in Gemini models, Document AI, Vertex AI, and Agent Studio, orchestrated with Google’s Agent Development Kit and agent-to-agent coordination, and guarded by Model Armor. And because AIOS™ separates the operating layer from the underlying model, the experiences are portable across model platforms, with Gemini Enterprise as the reference deployment.
Here is what we launched.
The portfolio at a glance: four governed business-process experiences, one reusable context foundation, one operating layer.
Agentic Batch Release Readiness — GxP Operations
CMO COAs and batch records arrive as static evidence, and release readiness ends up scattered across values, signatures, deviations, attachments, and batch status. This experience runs a governed flow COA intake, extraction and mapping, QA validation with source highlights, near-limit / OOT and trend checks, and a governed readiness state turning every validated COA into reusable batch intelligence with field-level traceability. An agent mesh (orchestrator, document-processing, semantic-layer, and conversational agents, on Document AI and a knowledge-graph-backed semantic layer) produces a validated readiness view, release checklist, OOT alerts, and a full audit trace with four-eyes review before release.
Agentic Deviation & CAPA Review — GxP Quality
Deviation closure doesn’t always become deviation learning; repeat causes stay buried in QMS comments and attachments. This experience summarizes deviation narratives and TrackWise/QMS comments into evidence-linked RCA themes, identifies recurrence by site, equipment, product, batch, and process step, and helps quality teams prioritize CAPA on high-risk, repeatable causes while final conclusions stay human-owned and every recommendation stays advisory and traceable.
Agentic HCP Engagement Planning — Commercial
Commercial signals are fragmented across CRM, field notes, campaigns, consent, and access and governance too often happens after the recommendation, creating rework. This experience lets a commercial user set a goal in plain language, then runs a mesh of specialist agents (market, targeting, ranking, competitive, channel, content, next-best-action, and consent/governance) to convert that goal into consent-aware, CRM-ready field actions. Consent, opt-out, frequency, and channel rules are applied before any action is created, human approval is required before any rep is contacted, and outcomes feed back into the loop.
CSR preparation pulls from protocol, SAP, TLFs, datasets, narratives, and review comments, and review churn spikes when numbers mismatch or sources go stale. This experience checks source readiness, maps CSR sections to approved evidence, drafts source-backed sections with retrieval grounding so every statement is cited, and runs QC for numeric, terminology, endpoint, population, and version consistency then routes the package across writing, statistics, safety, clinical, and regulatory review. Patient identifiers are removed at ingestion, handling is HIPAA-aligned, and human sign-off is required for high-risk sections such as efficacy, safety, and discussion.
The foundation: a reusable Life Sciences Context Layer
None of the above reasons reliably if product, batch, deviation, HCP, study, and regulatory context stays fragmented. Underneath the four experiences sits a reusable context layer semantic data products, knowledge graphs, ontology, and retrieval-ready pipelines with source lineage back to systems of record and role-aware access. It’s the enterprise base that makes these experiences explainable, reusable, and model-portable.
And the portfolio keeps extending
Beyond the four launch experiences, the same AIOS™ foundation is powering a manufacturing network modeling agent (where to make and ship across cost, time, risk, and market scenarios), a dedicated GxP compliance and auditability agent that enforces human review and keeps outputs evidence-backed and inspection-ready, and an adoption enablement track roles, training, playbooks, feedback loops, and usage measurement because agentic value only compounds when people trust and use the workflow.
One principle runs through all of it: the human stays in charge
Every experience is decision-support and workflow acceleration, with human-in-the-loop review, source lineage, and audit-ready evidence by design. These are patterns built in ALTI-Labs customer-specific validation, 21 CFR Part 11 / e-signature controls, and production GxP qualification remain implementation workstreams we take on with each client. In regulated operations, that discipline isn’t a limitation; it’s the whole point.
