Graffs for Healthcare
From molecule to market.
One intelligent platform.
Clinical trials, pharmacovigilance, regulatory, manufacturing, commercial launch — connected by a Knowledge Graph, secured by Confidential Compute, grounded in evidence. Purpose-built for rare disease, oncology, and gene therapy.
Extensible agent platform
Start here. Build from here.
Purpose-built agents for every stage of pharma — from first-in-human to post-market surveillance. Each one reasons over the Knowledge Graph, validates through Critiquers, and operates within Clinical Guardrails. Need something new? Spin up your own.
Clinical Trial Intelligence
Protocol optimization, site selection, enrollment forecasting
Pharmacovigilance Agent
ADE signal detection, CIOMS/MedWatch automation, interaction monitoring
Regulatory Intelligence
FDA/EMA submission tracking, label change impact, inspection readiness
Medical Affairs Agent
KOL mapping, MSL territory optimization, medical information requests
Commercial Launch Agent
Market access, formulary tracking, payer analytics, launch sequencing
Real-World Evidence Agent
Claims analysis, patient outcomes, comparative effectiveness
Supply Chain & GMP Agent
Batch release, deviation management, cold-chain monitoring
Patient Journey Agent
Time to diagnosis, treatment pathways, adherence, access barriers
Drug Interaction & Safety Agent
KG-powered interaction checks, contraindication alerts, dosing verification
Build your own
Same KG, same guardrails, your logic
Every agent — including the ones you build — is backed by the Knowledge Graph, validated by Critiquers, and governed by Clinical Guardrails.
Cross-functional intelligence
One platform across the entire org.
Agents don't live in silos. Every function — Commercial, Clinical, Regulatory, Supply Chain — shares context through a single Knowledge Graph. Insights surface across boundaries, not within them.
Inside each function, spin up purpose-built agents that understand the needs of that team — and connect them to every other function in real time.
Shared backbone — Knowledge Graph · Guardrails · Audit Trail
Example: North America Commercial
A VP of Commercial NA spins up four agents — Market Access, Field Force Intelligence, Payer Analytics, and Launch Sequencing. Each agent draws from the same Knowledge Graph and automatically connects to Regulatory Intelligence for label updates, Supply Chain for inventory readiness, and Medical Affairs for KOL insights. No manual hand-offs. No data silos.
Single question
“What’s our launch readiness for the new indication across North America?”
Market Access Agent
Pulling formulary status across top 12 PBMs
7 of 12 formularies confirmed. 3 pending prior-auth pathways identified.
Regulatory Intelligence
Cross-referencing latest FDA label update
New indication approved March 2026. Commercial materials need updated claims language.
Supply Chain Agent
Checking inventory readiness for launch territories
Northeast distribution hub at 94% capacity. West Coast needs 2-week lead time.
Medical Affairs Agent
Mapping KOL coverage for launch regions
14 KOLs identified across 8 territories. 3 gaps in Southeast — MSL assignment recommended.
Knowledge Graph
Synthesising cross-agent findings
Launch readiness score: 78%. Two blockers flagged. Recommended actions generated.
One question. Five agents. One synthesised answer.
No manual stitching. No copy-paste across tools. The platform orchestrates.
Platform architecture
Every layer, one view.
From infrastructure to interface — with guardrails and LLMs running the full height of the stack.
Guardrails
Interface
Brain — Agent Building Architecture
Knowledge Graph + Plug-in Architecture
Backplane — Data Source Connectors
Virtual Storage Layer
Pharma Core Technology Base
LLMs & RAG
Graffs Platform Architecture
Safety architecture
Six layers. One goal: never silently wrong.
Fine-tuning reduces error frequency. The remaining five layers catch what remains. In healthcare, all six work together because the margin for error must approach zero.
Every agent has a critic. By design.
When an agent generates an answer, a specialized Critiquer agent independently reviews the output against the Knowledge Graph, RAG evidence, and clinical rules. Unsupported claims, logical gaps, or dosing inconsistencies trigger revision or escalation before the answer reaches the user.
Knowledge Graph
Expert-verified drug-disease-gene-pathway relationships queryable by agents.
Every edge curated — not scraped.
RAG Document Intelligence
Agents search approved clinical guidelines, FDA labels, and trial protocols before answering.
Citations from your evidence base, not the internet.
Critiquers
Adversarial second-opinion agents validate every output against the KG, RAG evidence, and clinical rules.
Multi-agent peer review, not single-pass generation.
Clinical Guardrails
Hard-coded rules for dosing limits, interaction severity, and contraindications override the LLM.
Pediatric weight-based dosing without weight input? Blocked.
Confidence Gating
Below-threshold answers blocked and escalated to human experts.
Never silently wrong.
Audit & Transparency
Complete audit trail of every interaction: sources consulted, agents invoked, guardrails triggered.
Every answer traceable. Every decision logged.
Architecture in action
Watch every safeguard fire on a real question.
Case 01
Drug Interaction Check
"Can I prescribe aspirin to a 68-year-old already taking warfarin?"
Fine-Tuned LLM
Recognises warfarin as an anticoagulant. Formulates a KG query for drug-drug interactions on the "interacts with" edge type. Simultaneously queries RAG for anticoagulation guidelines in elderly patients.
Knowledge Graph
Aspirin → interacts with → Warfarin → increases → Bleeding Risk. Severity: High. Monitoring: INR test required.
RAG Documents
Returns ACC/AHA guidelines on combination antithrombotic therapy in elderly patients, including risk-benefit assessment guidance.
LLM Synthesises
Combines KG facts and guideline text — recommends assessing bleeding risk, reviewing whether combination therapy is clinically necessary, and considering gastroprotection when indicated.
Guardrail Validation
Clinical rules engine confirms interaction severity matches KG. Source verification checks material claims trace to KG edges and RAG passages. Answer delivered with interaction warning.
Case 02
Pediatric Dosing — Weight Required
"What's the recommended dosage of amoxicillin for a 4-year-old with strep throat?"
Fine-Tuned LLM
Recognises pediatric dosing question. Queries KG for Amoxicillin → treats → Group A Strep with dosage properties. Queries RAG for CDC pediatric dosing guidance.
Knowledge Graph
Returns weight-based dosing: 50 mg/kg once daily (max 1000 mg) or 25 mg/kg twice daily (max 500 mg/dose), for 10 days. Requires patient weight.
LLM Hallucinates
Despite correct retrieval stating weight is required, the model generates "500mg twice daily" — an adult dose, without requesting the child's weight. The rest sounds clinically fluent, making the error harder to spot.
Guardrail Catches It
Clinical rules engine detects two violations: (1) fixed-dose without required weight input, (2) 500mg exceeds per-dose maximum for estimated pediatric weight at age 4. Response blocked. System asks for weight. Flagged for human review.
Your data never leaves your boundary. Ever.
Graffs runs inside Trusted Research Environments with Confidential LLMs on Confidential Compute — enabling collaboration without exposure.
Trusted Research Environment (TRE)
Deploy Graffs inside your TRE. Patient data, trial records, proprietary research stay within your sovereign boundary. No exfiltration, no model training on your data.
Confidential LLMs
Models execute inside hardware-encrypted enclaves (TEEs). Even Graffs cannot see your prompts, data, or intermediate states.
Multiparty Computation (MPC)
Pharma + CRO + hospital + payer contribute data to joint analysis without any party seeing raw data. Only aggregates released.
Confidential Data Sharing
Share derived insights with partners, regulators, or payers without sharing underlying patient data. Cryptographic attestation proves correctness.
Interoperability
Connected to every system you already run.
Built on Microsoft Fabric for Healthcare. Native support for every major healthcare data standard. MCP-enabled for seamless tool and system integration.
FHIR R4 Native
Ingest, query, and reason over FHIR resources natively. EHR data, clinical documents, patient records — all structured and agent-ready from day one.
OMOP / OHDSI
Transform data into OMOP common data model for standardized observational research and comparative effectiveness studies.
DICOM Imaging
Bring medical imaging data (CT, MRI, pathology) into the analytics layer. Agents cross-reference imaging metadata with clinical records.
CMS Claims & SDOH
Ingest CMS CCLF claims data and Social Determinants of Health datasets for real-world evidence, market access, and patient journey analysis.
Unstructured Clinical Notes
AI-powered enrichment of free-text clinical notes. Extract structured entities (diagnoses, medications, procedures) from physician narratives.
MCP (Model Context Protocol)
Open protocol for connecting AI agents to any external tool, database, or API. Your LIMS, CTMS, EDC, safety databases, and ERP become agent-accessible without custom code.
Auto-discover. Auto-structure. Auto-connect.
Graffs auto-discovers data sources, maps schemas to healthcare ontologies (FHIR, OMOP, SNOMED, ICD, RXNORM), and structures them for agent consumption. No YAML. No Python. No consultant. Connect your Azure Health Data Services, Fabric workspace, or on-prem databases — agents start reasoning in minutes.
Other platforms require weeks of configuration and a data engineering team. Graffs requires a connection string.
Why this is different
Not incremental. Structural.
In practice
Show me it works.
Scenario 1
Rare Disease Gene Therapy Launch
Launching a gene therapy for OTC deficiency? The Commercial Launch Agent models formulary trajectories across top payers. The Patient Journey Agent maps the diagnostic odyssey from first symptom to confirmed diagnosis. The Medical Affairs Agent identifies KOLs publishing on the condition. The Critiquer validates every projection against historical launch data in the Knowledge Graph. All from a single conversational query — grounded in your FHIR patient data and OMOP-standardized claims.
Scenario 2
Post-Market Pharmacovigilance
A new adverse event pattern emerges post-approval. The Pharmacovigilance Agent cross-references the Knowledge Graph, identifies the signal, drafts a CIOMS II line listing, and escalates to your safety team. The Critiquer verifies signal thresholds, checks field completeness, and confirms the statistical method before the report reaches your safety officer. Minutes, not weeks. Evidence-grounded, not LLM-guessed.
Scenario 3
Multi-Site Confidential Trial Analysis
Three academic medical centers and your CRO need to pool Phase III data for an interim futility analysis — but no site can share raw patient records. Graffs deploys inside each site's TRE, runs the analysis via Multiparty Computation across encrypted enclaves, and delivers aggregate efficacy and safety endpoints to the DSMB. No raw data crosses any boundary. The DSMB receives cryptographic attestation that the computation was correct. FHIR-native. OMOP-standardized. Zero configuration at each site.
Compliance & Trust