An agentic workflow that turns long, messy meeting transcripts into structured, methodology-compliant R&D project documentation ready for submission to the IP authority — cutting manual drafting time by up to 90%.
The Business Challenge
Qualifying an activity as R&D takes more than technical accuracy. The documentation has to follow a defined official methodology, present a clear argument for why the work qualifies, and hold up to scrutiny when submitted to the IP authority.
The problem is that the source material almost never arrives in that shape. The relevant facts are scattered across long meeting transcripts that are often noisy and low-quality, full of transcription errors, half-finished thoughts, and tangents. Consultants have to read through all of it, pick out the technically relevant contributions, work out who said what, and rebuild fragmented discussions into a coherent, well-argued narrative.
A single report can run 10–20 pages, and pulling one together by hand takes several working days. That slows down turnaround and makes it hard to keep the same quality across submissions.
Industry
- Tax & Legal
- R&D Advisory & Compliance
Maturity
Production
Capabilities
- Agentic Multi-Step Pipeline
- Transcript Cleaning & Noise Removal
- Speaker-Level Multi-Label Classification
- Evidence Extraction & Narrative Reconstruction
- Methodology-Compliant Document Generation
What We Delivered
We developed an AI-powered documentation tool that automates the end-to-end transformation of meeting transcripts into structured R&D submissions.
The solution:
- Processes full meeting transcripts and recordings in a single workflow
- Classifies each speaker’s input by R&D-relevant themes and activities
- Extracts supporting evidence and reconstructs coherent technical narratives
- Generates draft, government-ready documentation aligned with formal requirements
- Applies a multi-step AI workflow to ensure consistency, traceability, and professional structure
- Keeps consultants in control through a review and validation layer
The result is a scalable approach to producing high-quality, compliant R&D documentation with significantly reduced manual effort.
Key Outcomes
Cost-Benefit Estimation
€201K
savings / year
1.2 mo
payback period
2,100 h
consultant hours reclaimed
Snet = Nsubmissions × (Tmanual − Tassisted) × Chr − Cplatform
Nsubmissions = R&D submissions/year | Tmanual = manual drafting time (h) | Tassisted = AI-assisted time (h) | Chr = fully loaded consultant rate | Cplatform = annual platform cost
Example — R&D advisory practice, 150 tax credit submissions/year, 3-person drafting team
| Manual drafting cost | 150 × 16h × €105 | = €252,000 |
| AI-assisted cost | 150 × 2h × €105 | = €31,500 |
| Platform cost | − €20,000 | |
| Net annual savings | €200,500 |
Illustrative estimate. Actual savings depend on submission complexity, transcript length, and team size. Platform cost assumes Azure-hosted deployment with consumption-based LLM pricing.
Online proposal request
