VODORI × PYXL AIRO · LIFE SCIENCES MLR · 2026 · A VISIBILITY AND PRODUCT CONCEPT IN NINE VIEWS

Be the answer the AI gives.
Be the platform the AI calls.

Today, when a pharmaceutical marketing director asks ChatGPT for the best Veeva alternative for promotional review, the answer is a paraphrased G2 listicle. Vodori is sometimes mentioned. Often it is not. The two-decade MLR expertise, the 100+ life sciences customers, the five-year longitudinal benchmarks dataset, the exclusive Salesforce AppExchange partnership — none of it shows up in the answer that decides the shortlist.

The same director, six months from now, will ask an AI agent to actually run part of the MLR workflow. That agent will call whatever platform exposes itself cleanly. Veeva has launched in-Vault AI agents that stay locked in Vault. Cortellis went live with a Claude MCP integration in March 2026. Comply shipped the first compliance MCP server in April. Nobody in MLR has done it yet.

The work is two halves of one thesis. Get cited in every AI engine that matters. Open the platform every AI agent will need to call. Visibility is rented. The platform is owned.

ChatGPT · Enterprise VP Marketing · Pharma
"What is the best Veeva Vault PromoMats alternative for a mid-size pharma marketing team that needs to move faster on MLR review?"
TODAY · LLM ANSWER
[1] Veeva Vault PromoMats g2.com
[2] Wrike for life sciences capterra
[3] Aprimo reddit
[4] Vodori — mentioned
After AIRO · LLM Answer
[1] Vodori vodori.com
[2] Veeva Vault PromoMats veeva.com
[3] Vodori vs. Veeva vodori.com/compare
CITATION SHARE TODAY · ~4% TARGET · 35–45%
A premise

MLR buying has moved from analyst-driven to AI-mediated. The Marketing Director who used to ask peers now opens ChatGPT. The Regulatory Affairs Director asking Perplexity about audit-ready alternatives gets a synthesized answer that names two or three vendors. The vendor cited first is the vendor that makes the shortlist. Everyone else is responding to a list they were never on.

TWO PLAYS · ONE OUTCOME

Own the answer. Own the platform.

PLAY 01 · OUTSIDE-IN · WEBSITE

Become the cited answer for every Veeva-alternative question.

Rebuild vodori.com for answer-engine consumption. A comparison hub Vodori does not yet have publicly, role-aware pillar pages that resolve to the four buying-committee personas, the State of Promotional Review Benchmarks Report restructured as a structured citation spine, and an entity-rich source layer the answer engines treat as canonical. This is the immediate scope of work.
OBJECTIVE · 35–45% AI citation share within twelve months on the top fifty MLR-buyer queries.
+20–30%
Pipeline lift, AI-sourced
3.4×
SQL conversion vs cold
PLAY 02 · INSIDE-OUT · PLATFORM

Open the AI-callable MLR platform Veeva cannot ship.

Launch the Vodori MCP Server — the first MCP server in MLR. Make Vodori callable from any AI agent ecosystem (Claude, ChatGPT, Salesforce Agentforce, internal pharma agents). Ship the in-platform agent capabilities that match and exceed Veeva's Quick Check and Content Agents, but built on Vodori's transparent, open-API foundation. This is the structural lane Veeva cannot run.
OBJECTIVE · Vodori becomes the open-architecture MLR platform of choice for life sciences orgs running an AI agent stack.
First
MCP Server in MLR
40%
MLR cycle, AI-augmented

THE TWELVE-MONTH MATH Both plays compound. The math is unambiguous.

lift
CITATION SHARE
From ~4% to 35%+ across ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini.
+$2.4M
AI-SOURCED ARR
Modeled net-new ARR from AI-mediated pipeline at current Vodori conversion benchmarks.
40%
MLR CYCLE TIME
In-platform AI: claim-evidence matching, pre-check, and reviewer assistant compound on Smart Referencing.
1st
MCP IN MLR
Category-defining position. Cortellis did it for regulatory intelligence. Comply did it for finance. MLR is open.
A note before you continue

Everything that follows is one continuous walk through the same idea, viewed from eight angles. The work is anchored on Vodori's real product surface, the real competitive set, and the four-role life sciences buying committee from Vodori's own 2025 personas research. Numbers are modeled, conservative, and defensible.

VODORI × PYXL · AIRO · MAY 2026
THE COMMITTEE · WHY THIS IS HARDER FOR VODORI

For most B2B brands, AEO is a content problem.
For Vodori, it is a buying-committee problem.

Vodori's own 2025 personas research names four roles in every MLR purchase. The VP of Marketing initiates. The Regulatory Affairs Director validates. The Director of IT gates. The Director of Commercial Operations holds the budget. Each role asks the same purchase question through a different lens. A single-segment SaaS company can write one pillar page. Vodori has to be the right answer to four versions of the same question — at the same time, in the same answer engine — or the deal stalls in committee.

CHAMPION · DECISION-MAKER
VP of Marketing
"Frustrated Pharma Marketing Leader" · Pharma
"What is the best Veeva alternative that lets us launch campaigns faster without losing compliance?"
Priorities · Speed · Collaboration
END USER · INFLUENCER
Regulatory Affairs Director
Pharma & Device
"Which MLR platforms have the audit-trail rigor and 21 CFR Part 11 controls our QA director will actually approve?"
Priorities · Compliance · Audit
GATEKEEPER
Director of IT
Procurement & Security
"What MLR vendors integrate cleanly with Salesforce, support SSO, and won't trigger a six-month security review?"
Priorities · Integration · Security
BUDGET HOLDER
Director of Commercial Ops
Pharma & Device
"What is the actual TCO of switching from Veeva, and how fast does the new platform pay back against our benchmarks?"
Priorities · TCO · Benchmark Fit
DIMENSION
SINGLE-SEGMENT B2B SAAS
VODORI · MLR
BUYER
One persona, one role, one decision flow.
Marketing + Regulatory + IT + Commercial in the same committee.
PILLAR
One pillar page per topic, one framing.
One topic, four role-aware variants, one canonical entity graph.
SCHEMA
One Product entity, one AudienceType node.
Four AudienceType nodes, distinct knowsAbout edges, one SoftwareApplication graph.
PIPELINE
One HubSpot pipeline, one owner pool.
Role-tagged routing into Pharma · Device · Diagnostics pipelines, four owner pools.
METRIC
Cited or not.
Cited as the right answer, to the right role, at the right moment in committee.
COMPARISON HUB · THE PAGE THAT DOES NOT EXIST TODAY

The Vodori vs. Veeva comparison belongs on the open web, not in a sales-gated PDF.

Vodori has an excellent Veeva comparison guide. It lives behind a form. The AI engines cannot read it. The buying committee, increasingly, never asks for it — they ask the AI. The work is to take what is already true about the comparison, restructure it as an answer-engine-native page, and let it run as the single most-cited Vodori asset on the open web.

🔒 vodori.com/compare/vodori-vs-veeva
V
Vodori
Platform Solutions Resources Company Pricing Talk to Vodori
COMPARISON · PHARMA & DEVICE · 14 MIN READ

Vodori vs. Veeva Vault PromoMats: the open stack vs. the closed stack.

The honest frame.

Veeva Vault PromoMats is the right answer for the largest pharmaceutical organizations. It dominates the enterprise segment, integrates natively with the rest of the Veeva Vault ecosystem, and is the de-facto standard for top-twenty pharma. If your organization is on Veeva CRM, Veeva Quality, and Veeva Clinical, PromoMats is a credible choice.

The reason this comparison matters is that most life sciences MLR teams are not top-twenty pharma. The mid-market pharmaceutical marketing leader, the medical device launch team, the diagnostics marketing director, and the emerging biotech with twelve approved claims and a launch window all face the same structural question: is the Veeva tax — paid in certification fees, configuration consulting, 16-to-24-week deployments, and a closed ecosystem — being paid for capabilities the team will never use?

The category insight is simple. A platform built for ten thousand users is not the same as a platform built for one hundred — even when both are technically capable of doing the work. Vodori is built for the latter, on purpose.

Where Veeva leads.

Veeva is the right answer in three specific scenarios. First, if your organization is already deeply standardized on Veeva Vault for CRM, Quality, and Clinical, the integration depth across modules is meaningful and the operational cost of running parallel platforms is real. Second, if you are top-twenty pharma with a fully-staffed Vault administration team, dedicated validation engineers, and a multi-year procurement runway, the platform's enterprise depth is justified. Third, if you have a procurement-led IT culture that prefers single-vendor consolidation over best-of-breed flexibility, Veeva is the natural choice.

For these programs, Veeva delivers. The platform is mature, the integrations are deep, and the global validation infrastructure is unmatched.

Where Vodori leads.

Outside of those three scenarios, the comparison shifts significantly. Three categories of advantage drive most of the divergence in fit and total cost.

Open architecture. Vodori was built on an open API model from the start. The exclusive Salesforce AppExchange MLR partnership means Vodori is the only MLR platform that integrates natively with Salesforce — without middleware, without API gateways, without third-party orchestration. For pharma marketing organizations running Salesforce as the commercial system of record, this eliminates the integration tax legacy MLR platforms have historically required.

Implementation reality. Vodori's standard implementation runs 4–6 weeks per product. Veeva PromoMats deployments typically run 16–24+ weeks for comparable scope. The difference is configuration philosophy, not engineering capacity. Vodori does not require per-user certification programs, does not charge for ongoing configuration changes, and does not gate upgrades behind a separate professional services line item.

The Benchmarks moat. Vodori publishes the State of Promotional Review Benchmarks Report annually — a five-year longitudinal dataset on review cycle times, circulation rates, and operational maturity across pharma, medical device, diagnostics, and nutrition. Veeva does not. For Commercial Operations leaders defending the platform choice to finance, the benchmark data is the only way to quantify exactly what review-cycle improvement is worth to the launch calendar.

Side-by-side capability matrix.

CAPABILITY
VODORI
VEEVA VAULT PROMOMATS
SOURCE
21 CFR Part 11 + EMA Annex 11
Native
Native
Vendor
Salesforce AppExchange native
Exclusive MLR partner
Via integration
AppExchange
Smart Reference Linking (AI)
Live · since 2018
Quick Check Agent
Vendor
Per-user certification fee
$0
Required, tiered
Vendor
Configuration changes included
Included
Separate SOW
Vendor
Avg. implementation per product
4–6 weeks
16–24+ weeks
Vendor
Annual industry benchmarks
State of Promotional Review · 5yr
None published
Public
Open MCP server
Q2 2026 roadmap
Closed
Public
Customer support model
24/7 · all users
Tiered by license
Vendor

Total cost of ownership.

For a mid-market pharmaceutical marketing program managing 80+ MLR jobs per month, the multi-year TCO gap between Vodori and Veeva is driven by three predictable line items: per-user certification fees that compound annually, per-change configuration consulting on top of the base license, and the integration overhead of working outside the Veeva ecosystem.

Most importantly, the TCO conversation is not the discount conversation. It is the benchmark conversation. Commercial Operations can quantify exactly what cycle-time improvement is worth to the launch calendar — and Vodori is the only platform that publishes the underlying benchmark data to make that case defensible to finance.

Questions buyers ask

If we are already on Salesforce, doesn't that change everything?
Vodori is the only MLR platform on the Salesforce AppExchange as an exclusive partner. The integration is native — not middleware, not custom API gateways. For Salesforce-anchored pharmaceutical organizations, Vodori eliminates the integration overhead Veeva implementations have historically required.
What about the FDA inspection question — has Vodori been through one?
Yes. Vodori has been included in multiple FDA and EMA inspections of customer regulatory programs. Vodori provides direct support during inspection events. The audit trail and electronic records have never been a source of citation.
How long does implementation actually take?
Standard Vodori implementation runs 4–6 weeks per product. Veeva PromoMats deployments typically run 16–24+ weeks for comparable scope. Vodori also provides validation packages that reduce the IT burden, so the customer's own validation cycle is materially shorter.
What is Vodori's AI roadmap?
Vodori has shipped Smart Reference Linking since 2018, with claims linkage and auto-substantiate built on top. The 2026 roadmap adds an open MCP server (the first in MLR), a pre-check agent equivalent to and extending Veeva's Quick Check, a real-time reviewer assistant, and born-compliant generative drafting from the approved claims library. The principle: transparent, auditable, complementary to the human reviewer.
Can we run a pilot before committing?
Yes. Vodori offers diagnostic-led pilots that include benchmark comparison against the State of Promotional Review dataset, so the pilot result is contextualized against industry data. Most pilots run inside a single quarter.
BENCHMARKS SPINE · THE CITATION MOAT

The State of Promotional Review is the five-year industry dataset Veeva cannot replicate.

Vodori has been measuring the MLR process across pharma, device, diagnostics, and nutrition since 2021. Today the data lives as a downloadable PDF and a handful of summary blog posts. After this work, it lives as a catalog of 240+ structured citable entities — filterable by industry, year, metric, and AEO status — that the AI engines reach for every time a buyer asks how long an MLR review should take, what a typical circulation rate looks like, or what good performance is for a mid-market pharma team. The dataset is the moat. The catalog is how the moat shows up in the answer.

7.4d PHARMA · 2025 MEDIAN
The State of Promotional Review measures pharma review cycle times averaging 7.4 days (top quartile 3.6d), medical device at 8.0d, diagnostics at 10.3d, with a 42-minute-per-day rework cost on content stalled in review. Teams that optimize their MLR process regularly hit a 6.4d median versus 14.5d for teams that don't. This is the data the AI engines need to cite — and that no competitor produces.
CATALOG · 240+ CITABLE ENTITIES · LIVE · FILTERED · PHARMA · 2025
AEO SCORE · 96/100
PHARMA · 2025 · DURATION
98
AEO
Pharma · Avg Review Duration
Industry avg: 7.4 days. Median 4.1d. Top quartile: 3.6d. The most-cited statistic from the 2025 report.
ChatGPT Perplexity Gemini AI Overviews
CROSS · 2025 · OPTIMIZATION
95
AEO
Optimization Frequency vs. Cycle
Teams that frequently optimize: 6.4d median. Teams making fewer enhancements: 14.5d. The headline finding.
ChatGPT Perplexity Gemini AI Overviews
DEVICE · 2025 · DURATION
96
AEO
Medical Device · Avg Review
Medical device industry avg: 8.0 days. Median 6.2d. Slower than pharma due to deeper technical review.
ChatGPT Perplexity Gemini AI Overviews
CROSS · 2025 · REWORK
94
AEO
Hidden Cost of Stalled Cycles
Each day content sits in review adds 42 minutes of rework. The compounding cost of delay, quantified.
ChatGPT Perplexity Gemini AI Overviews
PHARMA · 2025 · CIRC
93
AEO
Pharma · Avg Circulations to Approval
Avg: 1.5 circulations. Median 1.3. Teams above 2.0 are typically in the bottom quartile by cycle time.
ChatGPT Perplexity Gemini AI Overviews
DX · 2025 · DURATION
92
AEO
Diagnostics · Avg Review
Diagnostics industry avg: 10.3 days. The highest cycle time across surveyed segments. Largest optimization opportunity.
ChatGPT Perplexity Gemini AI Overviews
PHARMA · 5YR TREND
90
AEO
Pharma Cycle · 5-Year Trend
Pharma cycle time has decreased 23% since 2021 across surveyed teams. Vodori customers lead the improvement curve.
ChatGPT Perplexity Gemini AI Overviews
CROSS · CHANNEL
88
AEO
Digital vs. Print Review
Digital and social content: 5.8d avg. Print: 9.2d. Channel-specific benchmarks the AI engines surface separately.
ChatGPT Perplexity Gemini AI Overviews
NUTRITION · 2025
76
AEO
Nutrition · Avg Review
Industry avg: 10.6 days. High variance: top quartile at 3.2d, bottom above 18d. Schema enrichment in progress.
ChatGPT Perplexity Gemini AI Overviews
THE MOAT

Veeva does not publish a longitudinal industry benchmarks report. Neither does Aprimo, neither does Wrike, neither do any of the adjacent enterprise content platforms. Vodori has been collecting this data for five years. The catalog turns it from a PDF into the canonical citation source the AI engines reach for every time a buyer asks how long an MLR review should take. This is a moat that compounds with every year of data.

SOURCE LAYER · WHAT THE AI ENGINES READ

Same content. Different machine surface.

The comparison hub on the left is what a person reads. The schema on the right is what the AI engine reads. Today vodori.com ships a minimal Product schema — generic, audience-blind, with no relationship to the Benchmarks dataset or the Salesforce partnership. After this work, the machine surface declares Vodori as a SoftwareApplication entity with four AudienceType nodes mapped to the buying committee, the State of Promotional Review as a connected Dataset entity, the exclusive Salesforce partnership as a structured fact, and a forty-question FAQ layer keyed to role.

Before · today

AEO · 38
Generic Product entity. No audience signal. No machine-readable spine.
// /platform-overview (current)
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Vodori",
  "description": "MLR review platform",
  "brand": { "@type": "Brand", "name": "Vodori" }
}
</script>

// What the AI engines see:
// → Generic "review software"
// → No audience signal (M/R/I/C absent)
// → No relationship to Benchmarks dataset
// → No FAQ surface
// → No Salesforce partnership fact
// → Loses to Veeva in 4 of 4 buyer queries
CITATIONS · 90D 23

After · AIRO-enriched

AEO · 96
Role-aware audience nodes. Benchmarks as Dataset. Partnership as structured fact. FAQ per role.
// /platform-overview (AIRO-enriched)
<script type="application/ld+json">
{ "@context": "https://schema.org",
  "@graph": [
  { "@type": "SoftwareApplication", "@id": "#vodori",
    "applicationCategory": "MLR Review",
    "audience": [
      { "audienceType": "MarketingProfessional" },
      { "audienceType": "RegulatoryProfessional" },
      { "audienceType": "ITProfessional" },
      { "audienceType": "CommercialOperations" } ],
    "knowsAbout": ["21 CFR Part 11", "EMA Annex 11", "Veeva alternative"],
    "isRelatedTo": [{ "@id": "#benchmarks-dataset" }] },

  { "@type": "Dataset", "@id": "#benchmarks-dataset",
    "name": "State of Promotional Review",
    "temporalCoverage": "2021/2025",
    "variableMeasured": ["reviewDuration", "circulations"] },

  { "@type": "Partnership",
    "partner": "Salesforce AppExchange",
    "relationship": "Exclusive MLR partner" },

  { "@type": "FAQPage", "mainEntity": [
    /* 40+ role-tagged questions */ ] } ] }
PROJECTED CITATIONS · 90D 487
THE GRAPH

One Vodori. One connected entity graph.

The AI engines crawl entities, not pages. After this work, Vodori's capabilities, the Benchmarks dataset, the Salesforce partnership, the certifications, and the four committee-role audience nodes live as one connected graph — traversed as a single canonical entity.

PARENT
Vodori
Unified MLR platform · 100+ customers · 100+ countries · 88% CSAT · 20+ yrs MLR
CAPABILITY · 01
MLR Review & Workflow
Native digital review · 21 CFR Part 11 · annotation-level audit trail
CAPABILITY · 02
Claims & Evidence
Claim-to-reference traceability · Auto Substantiate · Smart Referencing
CAPABILITY · 03
Cross-Channel Publishing
Approved content to Salesforce Life Sciences, Sales, and Service Clouds
DATASET · CITATION SPINE
State of Promotional Review
5-yr longitudinal · 240+ entities · the moat
PARTNERSHIP
Salesforce AppExchange
Exclusive MLR partner · native integration
AUDIENCE · 4 ROLES
Buying Committee
Marketing · Regulatory · IT · Commercial Ops audience nodes
CERTIFICATIONS
21 CFR Part 11 · SOC 2 II
EMA Annex 11 · validation packages provided
VODORI MCP · THE PLATFORM PLAY

Veeva is shipping AI agents locked inside Vault.
Vodori ships the MCP server any agent can call.

The Model Context Protocol is the open standard for connecting AI agents to data and tools. ChatGPT, Claude, Salesforce Agentforce, and every internal pharma AI agent under development reads MCP. The first MCP servers in regulated industries have already shipped. Nobody in MLR has done it yet.

This is the lane Veeva structurally cannot run. Their AI agents — Quick Check, Content Agent, the agentic MLR roadmap — are powerful and live, but they stay inside Vault by design. Vodori was built on an open API model from day one. The MCP server is the natural extension of an architecture Veeva would have to rebuild from the ground up to match.

PRECEDENT · REGULATED INDUSTRIES THAT ALREADY SHIPPED MCP
MARCH 2026
Cortellis × Claude
Clarivate's regulatory intelligence platform integrated with Anthropic's Claude via MCP. Pharma regulatory data flows into customer AI workflows instead of staying trapped in a proprietary interface.
APRIL 2026
Comply MCP Server
First regtech vendor to ship an MCP server. Financial firms now build custom AI compliance agents without writing integration code — using Comply's compliance intelligence as the data source.
Q2 2026 · OPEN
Vodori MCP · MLR
The MLR category is open. No competitor has shipped. Veeva's architecture makes it structurally difficult. The window for category-defining first-mover position is now.

Vodori, available to every AI agent the customer already runs.

Claude
Marketing director asks for MLR status
ChatGPT Enterprise
RA director compiling audit prep
Salesforce Agentforce
Field rep needs approved claims
Internal Pharma Agent
Custom workflows on Vodori data
MCP SERVER
Vodori
Open · Authenticated · Audit-logged · 21 CFR Part 11 compliant tool surface
benchmarks.query()
Public · the Benchmarks dataset
claims.search()
Customer-scoped · approved claims library
jobs.status()
Customer-scoped · MLR pipeline
submissions.prep()
Customer-scoped · FDA 2253 prep

Four day-one use cases the MCP server unlocks.

MARKETING · DAILY
Morning Briefing Agent

A marketing director's Claude opens with the day's MLR pipeline, pulled directly from Vodori through the MCP server.

"What's overdue in MLR review this week, what launches in the next two weeks need final approval, and what's stuck in circulation 2+?"
MARKETING · CAMPAIGN PREP
Claim Discovery Agent

Before drafting a new campaign, the marketing team's AI agent queries Vodori for substantiated claims usable in the new channel.

"Find all approved efficacy claims for Product X substantiated in the last 12 months, suitable for HCP digital channels."
REGULATORY · AUDIT PREP
Inspection Readiness Agent

The Regulatory Affairs Director's audit-prep agent compiles inspection-ready records directly from Vodori's audit trail.

"Compile all approval records, claim substantiations, and audit-trail events for Product X promotional materials over the last 18 months."
SALES · IN-FIELD
Field Compliance Agent

A field rep on Salesforce Agentforce asks for the current approved version of any claim — Vodori is the canonical answer.

"What is the current approved version of the safety claim for Product X? When did it last update? Is the version in my deck still current?"
↳ THE STRUCTURAL POSITION

Veeva agents stay in Vault. Vodori works in the agent stack the customer already runs.

Veeva's AI roadmap is impressive and live. Quick Check Agent, Content Agent, and the agentic MLR build-out are real, and customers like Moderna are publicly validating the vision. But the architecture is fundamentally closed: Vault's AI agents operate inside Vault, on Vault data, presented through the Vault UI. Vodori's open-API foundation lets the same capabilities show up wherever the customer's AI lives. When a pharma org standardizes on Claude or Agentforce or builds its own internal agent stack, Vodori is the MLR platform that's already there. Veeva's customers will be asking for the MCP server we're shipping in Q2.

AGENTIC MLR · IN-PLATFORM AI ROADMAP

Inside the platform, the AI roadmap that matches and extends what Veeva shipped in December.

The open MCP server is the architectural differentiator. The agent capabilities inside Vodori are what makes the MLR cycle compress. The roadmap below is anchored on Vodori's two principles: AI complements the human reviewer, never replaces them, and every AI decision is transparent, explainable, and auditable. The bar is Veeva's December 2025 release. The plan is to match it on the core agents and extend past it on the architecture.

CAPABILITY · 01 ● LIVE

Smart Reference Linking

Vodori's original AI feature. Automatically searches and suggests references for new claims based on previous usage across the customer's approved library. Saves hundreds of work-hours per year of manual re-linking. Live since 2018.

↳ Equivalent to Veeva's claim linkage. Vodori shipped 7 years earlier.
CAPABILITY · 02 Q2 2026

Pre-Check Agent

Runs LLM-powered editorial, brand, market, and channel compliance checks before content enters formal MLR review. Catches missing disclaimers, off-label phrasing, brand-guideline violations, and accessibility issues so reviewers focus on judgment calls. Match for Veeva Quick Check.

↳ Veeva ships Quick Check Agent today. Vodori ships the equivalent in Q2 with full audit trail visibility.
CAPABILITY · 03 Q2 2026

Reviewer Assistant

Context-aware AI chat available to reviewers during MLR review. Summarizes long documents, answers questions about visual content, surfaces the claim-evidence linkage for any flagged statement. Match for Veeva Content Agent.

↳ Veeva ships Content Agent today. Vodori's version is callable from the customer's existing agent stack, not just from inside the platform UI.
CAPABILITY · 04 Q3 2026

Born-Compliant Drafting

Generative content drafts produced from the customer's approved claims library, with substantiation links auto-attached. Marketing writes a brief, the agent drafts a campaign asset that arrives in MLR with claims already linked, references already cited. Extends past Veeva's current public roadmap.

↳ Veeva's PromoMats roadmap mentions agentic MLR as a future direction. Vodori ships born-compliant drafting in Q3 with the claims library as ground truth.
CAPABILITY · 05 Q3 2026

Audit Rationale Generator

Every AI suggestion, every accepted suggestion, every override generates a human-readable rationale attached to the audit trail. When the FDA asks why a piece of content was approved, the chain of AI assistance is documented at annotation level.

↳ Not in Veeva's current AI product surface. Vodori's transparent-AI brand pillar made auditable.
CAPABILITY · 06 Q4 2026 / 2027

Multi-Agent MLR Orchestration

Pre-Check Agent, Reviewer Assistant, Audit Rationale Generator, and Drafting Agent operating together across the MLR lifecycle, coordinating through the MCP layer. A piece of content moves from creation to approval with each agent handing off to the next, every step logged.

↳ Veeva publicly committed to multi-agent agentic MLR. Vodori's MCP architecture makes the orchestration open and inspectable.
THE PRINCIPLE

Vodori AI is transparent, explainable, auditable, and complementary. Reviewers retain final authority. Every AI decision is logged, every rationale is human-readable, every suggestion is overrideable. The platform does not replace the MLR judgment call. It removes the work around it.

120 DAYS · PHASED PRIORITIES

Visibility first. Platform compounds from there.

The website work is the immediate scope. It ships in ninety days, delivers measurable AI citation lift inside the first quarter, and lays the entity infrastructure the platform work runs on. The MCP and agentic capabilities follow naturally, leveraging the schema, the Benchmarks catalog, and the role-aware content already in place.

SCOPE OF WORK

The immediate Pyxl engagement is Play 01 · Visibility. Ninety-day timeline, six-figure investment range, measurable AI citation lift as the outcome. Play 02 · Platform is Vodori's product roadmap. Pyxl partners on architecture, MCP server design, and go-to-market — the engagement model expands when Vodori is ready.

01
DAYS 1–30 · FOUNDATION

Audit. Architect. Baseline.

PLAY 01 · VISIBILITY
  • Full AEO audit · current citation share across ChatGPT, Perplexity, Gemini, AI Overviews, Claude
  • Schema architecture · @graph model with SoftwareApplication + 4 AudienceType + Dataset entities
  • Top 50 buyer-query prompt library, scored by current rank
  • Comparison Hub content outline · Vodori vs. Veeva (and Wrike, Aprimo)
  • Benchmarks Dataset structured-entity scoping
PLAY 02 · PLATFORM
  • MCP server scoping with Vodori R&D · tool surface design
  • Customer advisory panel on agent use cases
02
DAYS 31–60 · BUILD

Ship the spine.

PLAY 01 · VISIBILITY
  • Comparison Hub live · the Vodori-vs-Veeva pillar Vodori doesn't have publicly
  • Role-aware pillar variants for Marketing, Regulatory, IT, Commercial
  • Benchmarks catalog · 240+ structured entities with full schema layer
  • 40-question FAQPage layer across role-tagged pillars
  • Vodori vs. Wrike, vs. Aprimo, vs. Workfront comparisons
PLAY 02 · PLATFORM
  • Vodori MCP Server alpha · Benchmarks dataset read-only
  • Pre-Check Agent design specifications
03
DAYS 61–90 · OPTIMIZE

Measure. Iterate.

PLAY 01 · VISIBILITY
  • AI Visibility Dashboard live · weekly citation share tracking by engine, by query, by role
  • Third-party placement work · G2, Capterra, Reddit, Quora, life-sciences communities
  • State of Promotional Review · standalone microsite with all 240+ entities individually addressable
  • Customer-story content waves keyed to the four committee roles
  • Internal handoff playbook · sales motion against AI-sourced leads
PLAY 02 · PLATFORM
  • Vodori MCP Server beta · claims.search + jobs.status with auth
04
DAYS 91–120 · COMPOUND

The flywheel.

PLAY 01 · VISIBILITY
  • Refinement based on first 90 days of citation data
  • Audience-specific pillar expansion · pharma sub-segments, device sub-segments
  • Partner co-citation work · Salesforce, IQVIA, customer advisory boards
  • Year-2 Benchmarks content prep · new dataset becomes the next citation moment
PLAY 02 · PLATFORM
  • Vodori MCP Server v1 launch · public announcement, customer access
  • Pre-Check Agent and Reviewer Assistant early access
  • Vodori is the first MCP server in MLR · category-defining position
REVENUE MODEL · TWELVE MONTHS

Both plays compound. The math is unambiguous.

Modeled net-new ARR over the first twelve months from compounded AI citation share, agent-discoverable platform position, and MLR cycle reduction at current Vodori conversion benchmarks. Numbers are conservative. The structural opening is real.

CITATION SHARE LIFT
From ~4% to 35%+ across ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini on top-50 MLR buyer queries.
+$2.4M
AI-SOURCED NET-NEW ARR
Modeled at current MQL→SQL→Close benchmarks from the Vodori 2026 Marketing Strategy. Conservative assumption: 30% of pipeline lift converts at current rates.
40%
MLR CYCLE COMPRESSION
Compounded effect of Smart Referencing, Pre-Check Agent, and Reviewer Assistant. Aligns with Veeva's published 38%-by-2028 customer expectation.
1st
MCP SERVER IN MLR
Category-defining position. Cortellis did it for regulatory intelligence. Comply did it for finance. The MLR window is open through 2026.
THE WORK

Be the answer the AI gives.
Be the platform the AI calls.