The Challenge
A global FinTech company with geographically distributed teams was losing competitive ground due to a broken RFP response process. Siloed knowledge held by individual stakeholders across Bulgaria and London created significant communication latency. Responses were slow, generic, and inconsistent — taking 38+ hours to produce. The risk: losing enterprise deals to competitors able to respond faster and more compellingly to procurement requests.
What They Built
The Grin Labs built a custom knowledge database centralizing the FinTech company's institutional expertise and configured Custom GPTs governed by the firm's own rulesets — replacing siloed expert routing with a unified knowledge layer that generates tailored, contextually relevant RFP responses.
The Grin Labs began with a knowledge curation phase — working with the FinTech company to extract, structure, and centralize the institutional expertise that had previously lived with individual stakeholders across Bulgaria and London. That knowledge base became the foundation for Custom GPTs configured to generate RFP responses governed by the firm's own response rulesets and quality standards. Rather than routing each RFP request through multiple siloed experts with communication latency across time zones, the system pulls from the unified knowledge layer and produces tailored, contextually relevant responses tuned to the specific customer's procurement language and priorities. The build ran within a 4–8 week window without requiring custom model training or complex infrastructure. Response time dropped from 38+ hours to 8–12 hours — a 70–80% reduction — and key-person dependency across time zones was effectively eliminated. The unexpected outcome: the client team described the AI-generated RFPs as among the best they had ever submitted, combining speed with the specificity that generic AI tools can’t provide without institutional knowledge grounding.
AI Role
Client team described AI-assisted RFP responses as 'one of the best RFPs we've ever responded to' — a qualitative leap from prior outputs that were slow and generic.
Infrastructure
• ChatGPT (underlying AI platform) • Custom GPTs (configured with company-specific knowledge and rulesets) • Custom knowledge database (centralized institutional expertise)
Integration Points
• Custom knowledge database connected to Custom GPT context layer • RFP intake routed to Custom GPT for knowledge-grounded response generation • Response output governed by firm-defined rulesets embedded in GPT configuration • Human review step before final submission preserved for quality control