Case Study: TSS
Total Specific Solutions leverages raia to accelerate M&A due-diligence with an Agentic Workforce
Project Goals
Objective
AI-Driven Approach with raia
Screen more acquisition targets without adding head-count
Auto-extract 80 + KPIs from Confidential Information Memoranda and run a proprietary Fit-Score™ model inside each AI agent
Cut first-pass memo drafting time from days to hours
Have the Deal Screener Agent assemble an executive summary, red-flag list and benchmark tables immediately after upload
Provide on-demand answers across thousands of historical deals
Use a vectorized knowledge base of 10 000 + memos, diligence packs and post-acquisition reviews, searchable via chat
Create a learning loop between post-deal performance and new screenings
Deploy a Post-Deal Insight Agent that ingests monthly portfolio data, updates benchmarks and retrains scoring weights
1 | Company & Challenge
Total Specific Solutions (TSS) acquires and grows vertical-market software businesses throughout Europe. Each potential deal arrives with hundreds of pages—financials, tech audits, HR reports—that analysts must parse quickly. As the pipeline widened, three pain points emerged:
Document Overload – Manual review could take several days per target.
Metric Normalisation – KPIs like NRR, gross retention and Rule-of-40 vary wildly across accounting structures.
Knowledge Drift – Lessons from prior deals were buried in nested folders and unavailable at the moment of analysis.
2 | Why TSS Chose raia
High-capacity ingestion—Launch Pad can bulk-vectorize thousands of PDFs and spreadsheets in minutes.
Custom code hooks—TSS’s proprietary Fit-Score™ Python script plugs directly into each agent’s reasoning chain.
Granular security—Project-level silos ensure live deal data is visible only to the assigned pod.
CoPilot feedback—Analysts can up-vote, down-vote and edit AI outputs in real time, tightening accuracy before full rollout.
3 | Solution Blueprint
Agent
Role
Key Integrations
Deal Screener Agent
Parse new CIMs, extract KPIs, calculate Fit-Score™, draft one-pager.
Vector KB, scoring script, email/Teams
Deep-Dive Analyst Agent
Answer ad-hoc questions (“Compare churn to peer set”), cite sources.
SharePoint, Snowflake data marts
Post-Deal Insight Agent
Pull monthly portfolio metrics, flag variance vs. acquisition case, feed learnings back to the KB.
Power BI API, email digests
4 | Project Roadmap
Weeks 1-3 – Bulk-upload historical memos & post-mortems; tune scoring model on ten-year IRR data.
Weeks 4-6 – Pilot in CoPilot with two sector teams; capture thumbs-up/down feedback.
Week 8 – Firm-wide release of Deal Screener Agent; Deep-Dive Agent follows.
Month 4 – Go-live for Post-Deal Insight Agent; establish continuous learning loop.
Results coming soon — the TSS team is currently in the pilot phase and will report quantitative outcomes after the first full quarter of live use.
Last updated