How One FinTech Cut RFP Response Time 75%

A global FinTech proposal team bottled siloed expertise across Bulgaria and London into an AI knowledge base — compressing RFP response from 38 hours to 8–12 and lifting quality.

75%+

Cut in RFP response time

< 4 weeks

Implementation Time

Carl Miller

Head of AI

The GRIN Labs

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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 that centralized the FinTech company's institutional expertise and could generate RFP responses governed by the firm's own ruleset. Rather than routing requests through multiple siloed stakeholders, the system pulled from a unified knowledge layer and produced tailored, contextually relevant responses. The unexpected outcome: the client team described the AI-generated outputs not just as fast, but as some of the best RFPs they had ever submitted — combining speed with specificity that spoke directly to individual customer needs.

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

ChatGPT and Custom GPTs power the knowledge retrieval and response generation layer. The Custom GPT is configured with the FinTech company's centralized institutional knowledge base and governed by firm-specific rules, enabling it to generate tailored, contextually relevant RFP responses without routing requests through siloed human stakeholders.

AI Model

ChatGPT

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

Impact

RFP Response Time Cut by 75%+

Response time dropped from 38+ hours to 8–12 hours — a 70–80% reduction — eliminating dependency on key-person availability across time zones.

Best-in-Class Output Quality (Self-Reported)

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.

Institutional Knowledge Centralized Across Geographies

Knowledge previously siloed across stakeholders in Bulgaria and London consolidated into a single AI-accessible layer, eliminating timezone and communication latency from the proposal process.

Technology Utilized

Implementation Complexity

Time Savings

Revenue Growth

The implementation required building a custom knowledge database and configuring Custom GPTs with company-specific institutional knowledge and response rulesets — going beyond a generic ChatGPT deployment, but achievable without proprietary model training or complex infrastructure. The primary effort is knowledge curation, structuring, and governance design rather than engineering.

Best Fit For

Operations and business development leaders at global professional services or B2B SaaS companies whose proposal teams are bottlenecked by siloed institutional knowledge and cross-timezone coordination friction.

Carl Miller

Head of AI

The GRIN Labs

AI transformation leader and change strategist helping businesses align AI with strategy. Head of AI at GRIN Labs, ex-SVP at Nuvei, GRIN co-founder, and certified coach focused on sustainable adoption

Connect with the Expert

Industry:

Financial Services

Business Function:

Sales & Revenue

Operations

Company Size:

1,001-5,000

Project Cost:

Cost not disclosed

Ownership:

Founder-Owned

Organization Type:

Private Company

AI Pattern:

Knowledge Management & Search (RAG)

Generative Design & Content

Value Type:

Time Savings

Revenue Growth

AI Model:

Frequently Asked Questions

How did a large financial services company cut RFP response time 75% with knowledge-management AI?

The experts began with a knowledge curation phase, extracting and centralizing institutional expertise that had lived with individual stakeholders across two regions. That knowledge base fed Custom GPTs configured to generate RFP responses governed by the firm's own rulesets and quality standards, pulling from a unified layer instead of routing each request through siloed experts. Response time dropped from 38-plus hours to 8–12 hours, a 75%-plus reduction.

What AI tools and models were used at the financial services company?

The system used ChatGPT with Custom GPTs configured to the firm's response rulesets, built on a centralized knowledge database without custom model training or complex infrastructure. The approach combined knowledge management and search (RAG) with generative content.

What results did the financial services company achieve?

RFP response time was cut 75%-plus, from 38-plus hours to 8–12 hours, institutional knowledge was centralized across geographies into a single AI-accessible layer, and the client described the AI-generated RFPs as among the best they had ever submitted.

How long did the build take?

The build ran within a 4–8 week window, without custom model training or complex infrastructure.

Who is this knowledge-management AI approach best for?