The Concept

Domain expertise as infrastructure. Specialist AI for pharma commercial, built right.

The gap between general AI and pharma-ready AI is not capability. It's a domain expertise and data-plumbing problem. We've built around both.

The Problem

Pharma commercial runs expert work the expensive way.

Pharmaceutical commercial organizations operate large analytics and operations functions that govern how drugs reach patients and how companies get paid for them. Patient funnel tracking. Gross-to-net revenue management. 340B compliance. Channel performance. Market access analytics. The work is data-intensive, domain-specific, and the same questions get asked every month, for every brand, at every company.

It is expensive not because it is inherently hard — it is expensive because the expertise is scarce and the data infrastructure is fragmented. The work has always required expensive teams. The model capability now exists to do the majority of it autonomously. The bottleneck is the domain expertise needed to deploy it correctly.

The Approach

One semantic model. Pre-trained expertise. Point and go.

Baseline Synthetic agents are trained on the conceptual and semantic model of pharma commercial data — not any one client's specific schema. They already understand what 867 sell-in data means, how chargeback files are structured, what a patient funnel waterfall looks like, and how 340B eligibility interacts with Medicaid exclusion data. Onboarding is schema mapping, not implementation.

The key insight. Every pharma commercial company works with the same underlying data types. Schemas and formats vary by vendor and client. The semantic model does not. Training agents on the semantic model — not any one schema — is what makes the technology universally deployable.
Specialist Agents
Purpose-built for one pharma commercial function. Trained deep, not broad. Each agent's edge cases and reasoning are baked into its core, not bolted on with prompts.
Supervisor Agent
Coordinates specialists for end-to-end cross-functional workflows and answers questions no single agent can handle alone.
Common Data Model
A canonical semantic model for pharma commercial data, maintained at the velocity the business actually changes — HRSA refreshes, Medicaid exclusion updates, payer mechanics, regulatory shifts.
Client-Side Deployment
Runs entirely in the client's cloud — AWS, Azure, GCP. Data never leaves the client environment. No data sharing. No vendor lock on the data.
The Moat

Why this is hard to copy.

Pharma commercial domain expertise is what makes Baseline Synthetic possible. It is not, by itself, the moat. The moats are structural.

Moat 1
The semantic data model
A clean, comprehensive semantic model for pharma commercial data is the platform every future agent sits on. Designing it correctly takes years of business and technical fluency together. A tech-only org gets the structure right; a domain-only org gets the meaning right. Both are required.
Moat 2
Velocity of upkeep
HRSA updates monthly. Medicaid exclusion files change. 340B contract pharmacy mechanics evolve. Payer access patterns shift quarter to quarter. The model has to be maintained at the speed the business changes — not on a software release cycle.
Moat 3
Client-side architecture
Data never leaves the client cloud. That posture is hard to retrofit into SaaS-native platforms and is the gating architectural fit for pharma IT and security review.
Moat 4
Design partner edge cases
Eighteen to twenty-four months of real-world reconciliation cases — weird identifier collisions, eligibility edge cases, audit-driven precedents — baked into the agent's reasoning, before any competitor catches up.
First Product

GTN & 340B Optimization Agent.

The first agent is the one with the cleanest economics, the tightest data scope, and the most direct path to a design partner. Hard-dollar recovery and audit-grade compliance, on a bounded set of data sources, against a problem manual processes systematically miss.

In build · Design partner ready
Hard Dollar Recovery
GTN & 340B Optimization Agent
Automates gross-to-net reconciliation and 340B covered entity compliance — triangulating multi-source data with expert matching logic to recover revenue and eliminate compliance risk that manual processes systematically miss.
Why this is the perfect agent problem
  • Multi-source, messy data. Product movement, chargebacks, eligibility, exclusion data, and contract pharmacy rosters all need to be reconciled across inconsistent identifiers and monthly eligibility shifts.
  • Expert judgment required. The duplicate discount prohibition demands precise cross-referencing that most offshore reconciliation processes can only approximate — creating both compliance risk and recoverable revenue.
  • Hard dollars in both directions. Over-discounting leaves money on the table. Under-discounting creates federal compliance liability. Both are systematic and addressable.
What the agent delivers
  • Real-time eligibility tracking. Covered entity status validated continuously, with eligibility changes flagged before the next chargeback cycle — not after.
  • Automated reconciliation. Chargeback claims matched against product movement data with expert fuzzy matching. Reconciliation gaps surfaced and quantified.
  • Duplicate discount enforcement. 340B claims cross-referenced against exclusion data to quantify exposure and enforce the prohibition proactively.
  • Full GTN waterfall. Every deduction category with variance against prior period and budget — by product and channel, continuously updated.
  • Audit-ready outputs. Complete documentation and a reasoning trail for every determination.
Economics. Reconciliation team cost in this function typically runs $800K–$3.25M per year across offshore operations, onshore expert supervision, and consulting. Beyond labor: revenue leakage and compliance exposure on affected products is a real, addressable percentage of gross revenue. The agent is priced against the value it creates — outcome-based components are under evaluation.
Next on the Roadmap

Patient Funnel Agent.

Once the first product is in production with a design partner, the second agent extends Baseline Synthetic into the analytics layer. Same architecture, same common data model, expanded data scope — claims, specialty pharmacy, hub, and CRM.

Roadmap · Post first design partner
Analytics at Scale
End-to-End Patient Funnel Agent
Tracks patients from diagnosis through prescription, payer access, dispense, and persistence — continuously, across claims, SP/hub, and CRM. What a dedicated analytics team produces monthly, running in real time with on-demand querying.
What the agent will deliver
  • Continuous funnel visibility. The full patient waterfall — diagnosed, prescribed, access-approved, dispensed, persistent — updated in real time, not on a monthly reporting cycle.
  • Drop-off diagnosis. Statistically significant gaps identified and segmented by HCP specialty, geography, payer type, and patient cohort — automatically.
  • Real-time alerts. Automated notification when funnel metrics deviate from trend or baseline, before they appear in next month's report.
  • Conversational querying. Natural language queries against live funnel data, answered on demand without a data pull request.
Addressable cost. For a five-brand portfolio, dedicated patient funnel analytics teams represent a labor pool of $3M–$9M per year in fully-loaded people cost — before consulting. The agent delivers equivalent analytical depth, continuously.
How We Go to Market

Design partners first. Software at scale.

We are building the right product and the right customer relationships in the same motion. The go-to-market is designed to generate early revenue, validate the agents in production, and build the reference customers that accelerate software licensing.

Phase 1
Design Partners
A small number of design partners engage at favorable terms in exchange for deep collaboration. The agent does the work. We learn what production deployment requires. They get audit-grade reconciliation before anyone else.
Phase 2
Software Licensing
License agents directly for deployment in the client's cloud. Baseline Synthetic maintains the canonical codebase. Clients configure locally. Data never leaves the environment.
Initial Beachhead
Mid-size specialty pharma
$300M–$2B US revenue, with material 340B exposure in oncology, immunology, rare disease, HIV, or hepatitis. Same data complexity as Big Pharma, faster decision cycles, fewer internal resources to lean on.

Talking with a small number of design partners.

If you run market access, government pricing, or commercial analytics at a specialty pharma company — or you invest at the earliest stage and want a closer look — reach out directly.

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