Context AI/
CSR Studio
CSR Studio
An AI-powered CSR workspace that transforms existing clinical data and study documents into an evidence-linked first draft aligned with ICH E3, ensuring source traceability, consistency, and medical writer control.
6 agents
Gated, with 5 human-in-the-loop checkpoints
Part 11
Audit trail and e-signature by design
Sentence-level
Evidence link and H/M/L AI confidence
CDISC-aware QC
Text checked against TLF, ADaM and SDTM
What We Do
Challenges We Solve
1. Commercial AI
1. Commercial AI
1. Commercial AI
Sales operations agent
Market access agent
Marketing Intelligence Agent
Market Affairs agent
Numbers are retyped, not referenced
Writers transcribe results from about 150 TLFs into Sections 10–12 and build summary tables by hand.
Transcription errors surface at QC or regulatory review; rework after every TLF rerun.
Consistency is checked by reading
Synopsis, body, Section 14, protocol, SAP, ADaM and SDTM are compared manually.
Mismatches such as randomised N between synopsis and disposition reach final review.
Changes are invisible
Protocol amendments, SAP updates and TLF reruns arrive without a record of which CSR text depends on them.
Outdated text survives into the final CSR; late re-review cycles.
No audit-ready trail
Reviewers and auditors cannot see where a statement came from or how AI was used.
Low trust in AI-assisted text; slower sign-off; inspection exposure.
Key Capabilities
Agent Capabilities
Six agents run in a fixed, gated order: Study Intelligence/Definitions → Discovery → Readiness → Evidence → Section Authoring → QC / Compliance. Five human-in-the-loop gates.
1. Commercial AI
1. Commercial AI
1. Commercial AI
Sales operations agent
Market access agent
Marketing Intelligence Agent
Market Affairs agent
Guided study setup
Start from a digitised protocol, a protocol upload or manual entry. Controlled terminology (CDISC CT) for phase; picklists for TA and indication; duplicate checks.
Source intake with human confirmation
Word, PDF, Excel, RTF TLFs, SAS XPT / Dataset-JSON and define.xml. Rule-based and AI classification side by side; writers confirm type, version and authoritative source.
Readiness before generation
A matrix of ICH E3 sections against required sources. Draft-ahead mode for methods sections before database lock.
Evidence-linked drafting
Section-by-section drafting in dependency order, with a source pointer and confidence for every statement and cell-level links in summary tables.
Key Capabilities
Measurable Outcomes
These are design targets, benchmarked against published industry results. They will be confirmed on pilot CSRs.
First draft in days, not weeks - industry benchmarks show drafting cut from 2–3 weeks to 3–4 days.
Writer effort per first draft cut by roughly half - freeing medical writers for interpretation.
Fewer numeric and terminology mismatches reaching final review - because every number is checked against its source.
Every numeric statement traceable to its source - ready for reviewers and inspectors.
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