Life Sciences

Your Pharma workflows. Your model. Your intelligence.

ACTAVA helps life sciences organizations put AI agents to work across complex operational workflows, with potential starting points in regulatory-document preparation, medical-writing support, clinical-trial operations, and commercial review. We connect systems, documents, and expert judgment to reduce manual effort and accelerate work within defined review and approval requirements—while building intelligence your organization owns and controls.

The submission is governed. The tail around it is not.

A pharmaceutical organization runs thousands of distinct workflows, and vendors build for the few dozen with the biggest transaction volumes. Everything below that line stays with your people. Each remaining workflow costs too little to justify a platform purchase and repeats too steadily to staff away, so medical writers, safety officers, and brand teams absorb it year after year while the RFPs go to the head.

What the tail is worth
$18–30B

Annual value pool in commercial operations and MLR review, the largest in the portfolio.

50–60

Days of MLR review per content piece, before a single asset reaches a channel.

2 of 3

Dollars of pharmacovigilance budget spent on case processing alone.

Regulatory work already absorbs 20 to 30% of drug development spend and clinical operations roughly 60%, while more than 90% of adverse events never get reported at all. Only 10 to 15% of executives report high comfort with generative AI in commercial use, which is a governance problem rather than a capability one — the work is well understood, and what is missing is the record that proves an agent did it the same way every time.

Workflow volume, illustrative
Suites & point solutionsYour teams, by hand

Vendors build for the head of the curve. The tail is where your medical writers, safety officers, and brand teams spend their weeks.

General-purpose models cannot carry GxP work alone.

On χ-BENCH, frontier models fail 72% of complex U.S. healthcare workflows, and fewer than 8% of agents stay successful when a task repeats. Repeatability is the whole game in a validated environment, so what you build around the model decides whether an agent survives qualification.

Read the χ-BENCH study
Pharmaverse· The enterprise map

Explore where agents go to work across the drug lifecycle, function by function.

Twelve functions, two rings, one map — the tail made concrete. Every function answers the same questions: the jobs to be done, the agents on the roadmap, the tail still run by hand, and the human who holds the decision. Hover a function for its intel; open one to see what would run there.

The Pharmaverse map · Life sciences enterprise view12 functions
R&D and regulatory coreWhere the molecule becomes a filing
Commercial and patient ringEvery function that reaches a prescriber or a patient

Hover a function for intel · Click to open it below · Plum = featured

Function· 7 of 12

Commercial Operations & MLR

The largest value pool in the portfolio at $18 to $30 billion a year, and the slowest loop. Review cycles run 50 to 60 days per content piece, and only 10 to 15% of executives report high comfort with generative AI in commercial use. Agents pre-screen; the committee decides.

3 roadmap agents5 tail workflowsFeatured

Position in the drug lifecycle

  1. Discover
  2. Develop
  3. Trial
  4. Submit
  5. Approve
  6. Manufacture
  7. Launch
  8. Promote
  9. Monitor

Jobs to be done

  • Pre-screen promotional content
  • Verify claims against the PI
  • Check fair balance
  • Route to committee
  • Distribute approved assets

On the agent roadmap

Delivered in waves — see the roadmap below.
CommercialP2
MLR Pre-ScreeningClaims library verification, fair balance checks, and regulatory compliance flags with a pre-review report.
CommercialP2
HCP IntelligencePrescribing analysis, KOL influence scoring, and engagement optimization.
PlatformP0
Document DraftingCompliant alternative phrasing pulled from the approved language database.

The tail — built on KORA

Workflows no vendor's roadmap reaches
  • Agency deliverable pre-checkEvery asset screened at handoff, before it enters the review queue at all.
  • Claim-to-source traceabilityEach claim tied to its substantiation, kept current as the label changes.
  • Rejection pattern miningWhy the committee sends work back, clustered so the agency stops repeating it.
  • Congress material sweepsBooth panels, slides, and leave-behinds checked against the same rules as core assets.
  • Expired asset recallMaterials past their review expiry, pulled from every channel on schedule.

Each of these costs too little to justify a platform purchase and repeats too steadily to staff away. Your team builds it on KORA in days, inside the same validated lifecycle. How KORA builds the tail →

The ACTAVA platform

One platform runs the whole tail. Build the agent, govern it, prove it, and own the model underneath.

Build any workflow with KORA

When a workflow is too niche for any vendor's roadmap, your operations team builds the agent on ACTAVA KORA in days across chat, voice, and background processes, with GxP controls and human gates configured at the points that need them.

Explore KORA

Govern every agent with KORA

A hundred small agents create a hundred audit surfaces. ACTAVA holds the agent registry, risk classification, validation lifecycle, and Part 11 audit trail in one place, with EU AI Act conformity drafted from the same record.

Explore compliance

Prove reliability with χ-BENCH

Qualification asks whether an agent does the same thing every time. χ-BENCH scores agents against healthcare workflow simulations before they deploy and after every model change, so revalidation rests on evidence.

Explore χ-BENCH

Own the intelligence with CURA

Long-tail economics fail when every agent rents tokens from a frontier lab. CURA, the ACTAVA trillion-parameter healthcare model, runs inside your cloud at a fraction of frontier cost and keeps the intelligence yours.

Explore CURA
See the work change· Operator's seat

Agent-assisted MLR pre-review. The committee holds the decision.

You are the promotional review coordinator. An agent has pre-worked a sales aid, checked every claim against the approved library, flagged what fails, and drafted the fixes. You decide what reaches the committee.

Illustrative prototype · sample tenantAI pattern · document intelligence + agentic workflowHuman-in-the-loop · required
MLR Pre-Review AgentSample tenant · illustrative
Agent is working A human must act
Stage 01 · Content arrives

New promotional asset in your queue

Core visual aid, oncology brand, submitted by the agency for launch readiness

The packet holds the asset, the reference pack, the approved claims library, and the annotated prescribing information. A coordinator would spend the better part of a day cross-checking every claim and citation before the committee ever sees it. The agent does that pass first.

Asset
PRM-4471 · 14-page sales aid
Brand
Sample oncology brand
Claims to verify
17 against prescribing information
Review window
Committee meets in 6 days

Full transcript of the MLR Pre-Review Agent run

You are the promotional review coordinator. An agent has pre-worked a sales aid, checked every claim against the approved library, flagged what fails, and drafted the fixes. You decide what reaches the committee.

The packet holds the asset, the reference pack, the approved claims library, and the annotated prescribing information. A coordinator would spend the better part of a day cross-checking every claim and citation before the committee ever sees it. The agent does that pass first.

  1. Ingested the asset and reference pack. 14 pages parsed, 17 claims and 24 reference annotations extracted with page coordinates. (0.9s)
  2. Verified every claim against the approved library. 17 of 17 claims matched to substantiated language in the prescribing information and the claims library. (1.7s)
  3. Checked fair balance and safety placement. Important safety information present on every spread. Page 3 balance ratio falls below the brand template threshold. (2.4s)
  4. Validated reference annotations. Two citations point to a superseded publication version, both traced to the agency's older reference pack. (3.1s)
  5. Drafted compliant alternative phrasing. Replacement language pulled from the approved database for both flags, with the source annotation attached to each edit. (4.2s)

Agent finding: the asset is substantively clean. Two mechanical defects would have consumed a full committee cycle — the page 3 balance ratio and two stale citations — and both come with approved replacement language ready for the creator to accept.

The agent cannot approve promotional content. Medical, legal, and regulatory reviewers own that call.

  • Return the two fixes to the creator. Send the flagged balance ratio and citations back with the approved replacement language, then route the corrected asset straight to the committee.
  • Route as-is with the pre-review report. Let the committee weigh the flags themselves. The report travels with the asset and every finding carries its source.
  • Override a flag. Disagree with the agent. A documented rationale is required, and the case auto-flags for quality sampling.

Whatever you choose, the committee still makes the final determination with the pre-screening report in hand. The agent narrows the work to medical and legal judgment rather than mechanical checking.

  • The manual path: 3 systems, 1 day. A coordinator opens the claims library, the reference pack, and the annotated prescribing information, checks 17 claims by hand, writes the findings into a review form, and books the next committee slot.
  • The agent-embedded path: 5 steps, 4 minutes. The agent verifies, flags, and drafts. The coordinator reviews the report and routes. The committee spends its cycle on medical and legal judgment instead of citation checking.

Industry MLR cycles run 50 to 60 days per content piece. Removing mechanical defects before the first committee read attacks the rework loop that drives most of that time, and every check stays in the audit trail for the next inspection.

Illustrative prototype. The brand, asset, claims, and timings are fictional sample-tenant data built to show the interaction pattern, not customer telemetry or a performance claim.

MLR Pre-Screening is a roadmap agent (wave P2), shown here as the interaction pattern rather than a library agent you can deploy today. The drafting step runs on Document Drafting, wave P0.

The pharma roadmap· Build waves

Start from a library, not a blank page.

The map above places 35 agents across the drug lifecycle, from regulatory submission through field engagement, and they arrive in waves. Each wave puts the next set of functions inside the same validated lifecycle, and the tail around them is built on KORA from day one. Pharmaceutical teams also draw on the payer, care management, and sales and marketing libraries today for the shared operations agents that sit under market access, patient services, promotional content, and field work.

  1. Q3–Q4 20268 agents
    P0 · FoundationDocument drafting, compliance validation, data extraction, cross-reference QC and escalation, plus the three governance agents that put every later agent inside a validated lifecycle.
  2. Q1–Q2 20278 agents
    P1 · Core operationsLiterature surveillance, coding and classification, submission assembly, regulatory intelligence, and the pharmacovigilance set from triage through E2B(R3) submission.
  3. Q3–Q4 202715 agents
    P2 · ExpansionClinical operations, MLR pre-screening, HCP intelligence, and the precision medicine, site-of-care and care management agent sets.
  4. 2028 and beyond4 agents
    P3 · StrategicManufacturing monitoring, root cause analysis, provider outreach voice, and sales research and account fit.

Wave counts are read off the map above, so the roadmap and the functions cannot disagree. Nothing here is deployable today — an agent becomes a library agent when it is seeded, tested on χ-BENCH, and given a page of its own.

90-day value realization

Start quickly.

Plan your agent roadmap and digital workforce budget against the mapped use cases.

Build agents immediately with the ACTAVA team beside your medical writers and safety officers.

Integrate your RIM, safety, clinical, and promotional systems, then launch from what already exists.

Run safely.

Establish AI policies, GAMP 5 risk classification, and evaluation rubrics before the first deployment.

Evaluate agent performance on χ-BENCH, inspect behavior, and document what improved.

Govern every agent from one registry with the Part 11 audit trail written as work happens.

Grow intelligently.

Control model costs with real-time consumption monitoring per function and per agent.

Distribute agent resources as submissions, launches, and safety volumes shift.

Measure ROI for every agent on a dashboard your finance team configures.

Next step

Pharma agents you can validate, prove, and own.

Every agent tests before it deploys, every action lands in the audit log, and the intelligence stays inside your cloud. Bring one workflow from your tail and we will show you the build.

The agentic future · For healthcare

From production workflows to customer-controlled intelligence.

Don't give your agentic future away to a single model provider. Don't mistake consumer tools for real, safe Enterprise Agentic Tools. Enable your citizen developers to create and manage the AI Agents they need to run their part of your business.

Build complex agents. Test their reliability. Learn from every workflow. Own your intelligence.