An AI-powered tender analysis platform that parses high-volume, multi-source RFPs, extracts structured insights, and calculates soft win probabilities. The tool integrates with internal credential, HR, and legal systems to validate eligibility, quantify risks, and generate executive-ready summaries. Designed for speed, accuracy, and multi-department usability, it empowers faster, data-driven go/no-go decisions while increasing bid success rates.
AI RFP Parser, Admin Dashboard
Problem Statement
Rodic faced a monthly influx of 100–200 complex RFPs from multiple government portals, each spanning 200–300 pages. Manual review was slow, error-prone, and resource-heavy, with teams struggling to filter based on qualification criteria, scope, penalties, and win probability. Disparate systems for credentials, manpower, and legal compliance made quick, informed tender decisions nearly impossible, delaying responses and reducing bid competitiveness.
Problem Statement
Rodic faced a monthly influx of 100–200 complex RFPs from multiple government portals, each spanning 200–300 pages. Manual review was slow, error-prone, and resource-heavy, with teams struggling to filter based on qualification criteria, scope, penalties, and win probability. Disparate systems for credentials, manpower, and legal compliance made quick, informed tender decisions nearly impossible, delaying responses and reducing bid competitiveness.
Approach
We mapped workflows across BD, legal, HR, and operations to prioritise bottlenecks. The RFP reading process was fully automated using an agentic AI workflow combining ML clustering with LLM verification. Credential data was linked to eligibility checks, while tabular scoring frameworks quantified key RFP parameters. Soft win probability models were built from 30+ manually studied RFPs. The system delivered summaries, maps, and timelines for rapid go/no-go decisions.
Impact
The BD Tool shifted tender evaluation from manual data extraction to strategic decision-making. By automating reading, eligibility validation, and soft win scoring, it freed teams to focus on competitive positioning. The result: faster bid turnaround, improved accuracy in qualifying tenders, and significantly higher participation in viable opportunities.
90% reduction in reading time
60% more detection of critical clauses
50% increase in tender applications
90% reduction in reading time
60% more detection of critical clauses
50% increase in tender applications
