A multi-agent AI for a measurable IP strategy
First Line Software, through its Clinovera healthcare practice, built a multi-agent evaluation system for the client’s Research Laboratories — automating a process that previously required months of cross-disciplinary expert review.
INDUSTRY
Healthcare
Corporate R&D / IP
TIMELINE
~2 months POC
The problem: patent review at the speed of research
The client is a compact but prolific research lab, with over 50 Ph.D. researchers producing a continuous stream of innovations. Turning that output into defensible, high-quality patents requires coordinated review across law, engineering, economics, and technical expertise. Manually, that process is slow, expensive, and fragile.
Errors in claim quality analysis were leading to rejections. Promising IP buried in research publications went undetected, because it was too costly to surface by hand. The portfolio was growing faster than the review process could scale.
| Claim rejections | Untapped IP | Scale mismatch |
|---|---|---|
| Errors and omissions in quality analysis caused avoidable downstream failures | Valuable innovations buried in publications — too labor-intensive to identify | Research output is growing faster than expert review capacity could handle |
The solution: a virtual panel of an AI focus group
First Line Software deployed its Clinovera AI Focus Group framework, a proprietary approach to collaborative AI where multiple agents, each assigned distinct expertise and responsibilities, interact in structured virtual conversations to reach consensus on complex evaluations.
Rather than querying a single model, the system runs 90 agents across 5 virtual rooms, simulating how a real interdisciplinary review panel operates: exchanging findings, surfacing disagreements, and producing aggregated outcomes with measurable quality metrics.
| AGENTS | 90 AI agents, each with defined domain knowledge and assigned review responsibilities |
| STRUCTURE | 5 virtual rooms with cross-room information sharing and a coordinated workflow |
| OUTPUT | Qualitative and quantitative patent claim quality metrics, benchmarked against human assessments |
| STACK | TypeScript · PostgreSQL · Clinovera AI Focus Group Framework · Claude Code |
| HOSTING | First Line Software infrastructure (POC phase) |
Delivery: from concept to working POC in weeks
| Speed | Team |
|---|---|
| Initial implementation completed in roughly two weeks using Vibe Coding with Claude Code. Two further weeks of iteration followed. A problem that would typically require months of custom development was operational within a single sprint cycle. | Clinovera’s lead AI architect handled framework design and implementation. A technical analyst managed communications. Clinovera’s operational and managing directors maintained close alignment with the client’s chief patent counsel throughout. |
Results: AI outputs in line with—and exceeding—expert benchmarks
The POC compared AI Focus Group assessments against the client staff evaluations for an existing patent, using metrics defined by the client project leadership. AI-generated outcomes met all defined quality benchmarks and exceeded human-produced assessments in several areas.
The AI Focus Group generates outcomes that align with the client’s expectations and exceed them in some areas.
Anatoly Postilnik
VP, Global Healthcare Consulting, First Line Software
- Ongoing claim QA: Automated, real-time quality assessment and remediation for the continuous flow of new patent claims from active research
- Patent drafting from research collateral: New patent documentation generated automatically from existing publications and research outputs — reducing time from discovery to filing
- IP discovery at scale: Systematic identification of patentable innovations buried in vast research archives — previously impossible to surface without prohibitive manual effort.
What made this possible
| No equivalent on the market | Clinovera framework advantage |
|---|---|
| Collaborative multi-agent AI at this level of structure is an emerging capability. The closest alternative—running separate AI sessions per persona and manually aggregating results—is feasible but slower and more error-prone. | First Line Software’s Clinovera healthcare practice has developed the AI Focus Group framework as a reusable accelerator. What would otherwise require months of development is deployed and configured in weeks—with a clear path to production. |
What’s next
Following positive POC outcomes, the client and First Line Software are moving to a production build—expanding beyond QA of existing claims to full patent drafting automation and proactive IP discovery across the client’s research archive.
Q2 2026
