AI-Powered Clinical Trial Intelligence Platform
A single Phase II trial generates 3,000-5,000 documents across 48+ TMF sections, managed across multiple disconnected systems. Nobody has a complete picture.
Each deviation triggers rework, queries, potential regulatory findings, and timeline delays. Most deviations stem from document management failures — missing signatures, outdated versions, missed filings.
The Trial Master File is the single source of evidence for regulators. Two-thirds fail on first inspection — missing documents, incomplete filing, version control failures, unsigned forms.
Clinical research associates spend nearly half their time on manual document tracking, status chasing across systems, duplicate data entry, and filing — not on patient safety or data quality.
Root cause: Clinical trial data lives in silos — Veeva for documents, Medidata for EDC, SharePoint for local files, SAS datasets for SDTM/ADaM submissions. No single system understands all of it. Nobody is connecting the dots.
Clinical trials operate across multiple data standards, each serving a different purpose. No single platform today connects all three layers. That's the gap Kyber fills.
What it is: The CDISC standard for structuring raw clinical trial data for regulatory submission. Every patient visit, lab result, adverse event mapped to standard domains (DM, AE, LB, VS, CM).
Who requires it: FDA, EMA, MHRA — mandatory for drug approval submissions.
Format: Static tabular datasets (SAS transport files / CSV). End-of-study. Not real-time. Not interoperable.
What it is: Analysis-ready datasets built on top of SDTM. Adds derived variables, statistical metadata, and analysis groupings so statisticians can run analyses without restructuring.
Who requires it: Also mandatory for regulatory submissions alongside SDTM.
Format: Static datasets. Used at study end for statistical analysis and submission. Contains the "truth" that regulators verify against.
What it is: HL7's modern standard for real-time healthcare data exchange. REST API, JSON payloads, standard resource types (Patient, Observation, ResearchStudy, DocumentReference).
Who's adopting it: NHS England, MHRA, FDA, Epic, Cerner, Veeva, Medidata. Becoming mandatory for NHS interoperability by 2027.
Format: Live, real-time API. How systems talk to each other during a trial — not just at the end.
Today's clinical trial stack has three disconnected data layers. Nobody connects them. Kyber does.
Tabular datasets submitted to FDA/EMA at study end. Structured, validated, static. SAS datasets, define.xml. Nobody looks at these during the trial — they're built after database lock.
TMF document intelligence, rules engine, AI copilot, audit trail. Reads documents (TMF), ingests datasets (SDTM/ADaM), communicates via FHIR with live systems. Single source of truth across all three layers. The brain that connects everything.
Hospital EHRs (Epic, Cerner), EDC platforms (Medidata Rave, Oracle InForm), eTMF systems (Veeva Vault), CTMS, IWRS. Real-time data flowing during the trial. FHIR is how they talk to each other.
Why this is our moat: Carelane and other competitors are trying to replace the bottom layer (be the EDC). We're building the middle layer that connects everything. Different buyer, different sales cycle, much harder to displace once installed. Every system that connects to us makes us more valuable — network effects.
Everything in a clinical trial maps to a FHIR resource. This is how Kyber creates a single source of truth — regardless of which system the data came from.
Once everything is a FHIR resource, you can query "show me all SAEs at Site 003" regardless of whether the data came from Veeva, Medidata, or a spreadsheet upload. The query is the same because the data model is the same.
The AI copilot doesn't need to understand Veeva's schema AND Medidata's schema AND SharePoint's folder structure. It queries FHIR resources. One data model to learn, infinite sources to connect to.
FDA, EMA, and MHRA are all moving toward FHIR-based submissions. When our data is already FHIR-native, regulatory readiness becomes a byproduct of how we store data — not a separate submission exercise.
DEOX-MOCK-002, our mock study, mapped to a FHIR ResearchStudy resource. This is what the JSON actually looks like.
// Current Kyber internal data { "study_id": "DEOX-MOCK-002", "study_name": "NOVA-STAR", "phase": "Phase II", "indication": "NSCLC, EGFR-mutant", "drug": "Bica-Xi (bispecific antibody)", "sites": 6, "enrolled": 47, "target_enrollment": 120, "tmf_docs": 318 }
{
"resourceType": "ResearchStudy",
"id": "DEOX-MOCK-002",
"title": "NOVA-STAR",
"status": "active",
"phase": {
"text": "Phase II"
},
"condition": [{
"text": "NSCLC, EGFR-mutant"
}],
"description": "Bica-Xi bispecific antibody",
"enrollment": [{
"reference": "Group/DEOX-MOCK-002-enrolled"
}]
}
What this enables: Any FHIR-compliant system can now query this study. The AI copilot can say "Study DEOX-MOCK-002 is Phase II, NSCLC, 47 of 120 enrolled" by querying a single resource type — regardless of whether the data came from our database, Veeva, or a spreadsheet.
Every document filed in the Trial Master File maps to a FHIR DocumentReference. This is how we track 524 artifact types across 11 zones.
// A signed informed consent form { "doc_id": "DOC-20260420-a3f2k", "study_id": "DEOX-MOCK-002", "title": "ICF_v3.0_Signed_2026-04-20.pdf", "zone": "04 IRB/IEC", "section": "04.02 Consent", "artifact": "04.02.02", "status": "filed", "site_id": "SITE-103", "uploaded_by": "jane.smith@cro.com", "r2_key": "DEOX-MOCK-002/tmf/04/04.02/..." }
{
"resourceType": "DocumentReference",
"id": "DOC-20260420-a3f2k",
"status": "current",
"type": {
"text": "Informed Consent Form",
"coding": [{
"code": "04.02.02",
"system": "dia-tmf-v3.1"
}]
},
"subject": {
"reference": "ResearchStudy/DEOX-MOCK-002"
},
"date": "2026-04-20T14:32:00Z",
"content": [{
"attachment": {
"url": "r2://DEOX-MOCK-002/tmf/04/...",
"contentType": "application/pdf"
}
}]
}
What this enables: The TMF classifier can auto-classify uploaded documents against the DIA TMF Reference Model by setting the type.coding.code. The rules engine can query "how many DocumentReferences exist for zone 04?" to calculate TMF completeness. The copilot can answer "what's missing for inspection readiness?" by comparing filed DocumentReferences against the expected 524 artifact types.
Protocol deviations, CAPAs, and adverse events all map to FHIR resources. Here's how a deviation flows through the system.
{
"id": "DEV-2026-0047",
"study_id": "DEOX-MOCK-002",
"site_id": "SITE-103",
"type": "Informed Consent",
"severity": "major",
"description": "ICF v3.0 used after v3.1
approved by IRB",
"subject_id": "SUBJ-103-012",
"detected_date": "2026-05-15",
"capa_id": "CAPA-2026-0018"
}
{
"resourceType": "DetectedIssue",
"id": "DEV-2026-0047",
"status": "preliminary",
"severity": "high",
"code": {
"text": "Protocol Deviation"
},
"subject": {
"reference": "ResearchSubject/SUBJ-103-012"
},
"detail": "ICF v3.0 used after v3.1...",
"mitigation": [{
"action": {
"reference": {
"reference": "Task/CAPA-2026-0018"
}
}
}]
}
The chain of linked resources: ResearchStudy → Location (Site-103) → ResearchSubject (patient) → DetectedIssue (deviation) → Task (CAPA). Every resource links to the others. The AI copilot can traverse this chain: "Subject 103-012 had a major informed consent deviation on May 15th, CAPA-0018 is assigned to resolve it." All from standard FHIR queries — no custom schema needed.
Kyber is the intelligence layer that sits on top of existing clinical trial infrastructure — reading documents, classifying data, enforcing rules, and providing AI-powered insights across the entire trial.
Auto-classifies documents against the DIA TMF Reference Model (524 artifact types across 11 zones). Detects missing filings, version conflicts, unsigned documents. Flags inspection readiness gaps before regulators find them.
12 configurable clinical rules with adjustable thresholds. Monitors safety signals, enrollment velocity, deviation patterns, site performance, query aging, TMF completeness. Fires real-time alerts when rules are breached.
Context-aware clinical AI assistant. Ask "How many SAEs at Site 003?" or "What TMF gaps for inspection readiness?" Classifies intent across 17 clinical modules. Conversation memory persists across sessions.
All data normalised to HL7 FHIR resources. Ingest from any FHIR-speaking system — Veeva, Medidata, Epic. Aligned with NHS England and MHRA interoperability mandates for 2027.
Working platform with mock study data, API backend, and clinical domain scaffolding. Honest assessment.
From current state to investor-demo competitive in 4 weeks. All built on existing scaffolding.
BY MAY 28
BY JUNE 4
BY JUNE 11
BY JUNE 18
Full EDC, eCOA, ePRO, consent, FHIR-native protocols, AI CRF builder, SOC 2/HIPAA/GDPR. Working AI agent.
Weakness: Rip-and-replace model. 18-month sales cycles. Enterprise pricing. No TMF intelligence focus.
Connects to existing Veeva/Medidata/SharePoint. TMF intelligence (524 artifacts). Rules engine (12 rules). AI copilot. FHIR-native data model.
Advantage: Days to deploy. No adoption friction. We sell to clinical ops, not IT.
Carelane owns the entire clinical trial stack. We build the brain that connects it all. Different market, different buyer, different sales cycle. We sell to the Clinical Operations Director — not the CIO.
Growing at 13.8% CAGR. Legacy platforms 15+ years old, lack AI.
Veeva and MasterControl dominate. Designed for storage, not intelligence.
MHRA tightening. NHS mandating FHIR by 2027. UK-first tailwinds.
Per-study, per-site pricing. AI copilot, document intelligence, rules engine, FHIR data access.
Target: £2,000-5,000/study/month
No system replacement required. Onboarded in days.
Setup, training, validation. TMF migration, study setup, rules config, FHIR connector deployment.
Target: £15,000-30,000/implementation
Also: inspection readiness audits, TMF gap analysis.
White-label the intelligence engine and rules engine to CTMS/eTMF vendors who lack AI.
Target: Revenue share or per-seat
Every CTMS vendor becomes a customer, not a competitor.
Career relationships with Veeva, Rivia, VITA FSP. Active network across UK CROs, pharma sponsors, regulatory consultants.
Complete the 4-week build sprint, secure first pilot customers, prove the intelligence layer model with real clinical trial data.
The intelligence layer for clinical trials.