How AI agents helped create a comprehensive RFP response with data visualizations, documentation, and analysis—while teaching us about the future of work
On November 5, 2025, Tata Trusts sent out an RFP for a "Design & Development of Geographical Data Visualisation Dashboard." The deadline? November 17—just 13 days away.
But what if AI agents could handle most of this work—not replacing human expertise, but amplifying it? This is the story of how one person, leveraging AI agents, created a comprehensive RFP response in just 10 days, spending only ~23 hours of human time.
From receiving the RFP to creating a comprehensive response with interactive visualizations, here's how the 10 days unfolded. Each milestone represents a collaboration between human judgment and AI capabilities.
The Challenge: The RFP document was bureaucratic and dense—typical for large organizations but difficult to quickly parse.
The AI Solution: Used ChatGPT to draft a formal acknowledgement email. Instead of spending 30 minutes crafting the perfect corporate response, the AI generated it in seconds.
Human Role: Reviewed and personalized the response, ensuring it matched professional standards. Time spent: 5 minutes.
The Challenge: RFPs often lack crucial details. Experienced pre-sales teams know to ask clarifying questions early, but identifying all gaps requires deep domain knowledge.
The AI Solution: ChatGPT analyzed the RFP and generated 129 detailed questions across 11 categories—from technical architecture to payment terms.
Human Role: Reviewed questions for relevance, removed duplicates, and prioritized based on experience. Identified that some questions revealed assumptions about their technical maturity. Time spent: 30 minutes.
The Challenge: Pre-bid meetings are crucial for understanding unstated requirements and building relationships, but they're also opportunities to reveal (or hide) your expertise.
The AI Solution: Claude analyzed the RFP and generated strategic questions to ask during the meeting, focusing on understanding the organizational context and true needs.
Human Role: Used AI insights to guide conversation strategy. During the call, actively listened for signals about organizational maturity, technical constraints, and decision-making processes. Time spent: 1 hour prep + 1 hour meeting.
The Challenge: The RFP was a 47-page PDF. Working with PDFs is painful—you can't easily search, reference, or version control them. Plus, sample datasets were needed for visualization demos.
The AI Solution:
Human Role: Reviewed datasets for realism. Asked AI to add missing fields (partner names, realistic geographic distributions). Verified data patterns made sense for the domain. Time spent: 1 hour.
The Challenge: Creating materials that match a client's brand requires extensive research into their visual identity, tone, and design patterns—normally a designer's multi-day task.
The AI Solution: Claude Code analyzed Tata Trusts' website, extracted brand guidelines (colors, typography, design patterns), and created a comprehensive style guide. Then generated an interactive "RFP Story" website explaining the opportunity.
Human Role: Reviewed visual consistency, requested refinements to match brand tone, fact-checked claims about Tata Trusts' history and impact. Time spent: 2 hours.
The Challenge: Creating professional data visualizations requires: analyzing data patterns, choosing appropriate chart types, implementing interactive features, ensuring responsive design, and weaving it into a compelling narrative. This is typically a 3-5 day task for a skilled developer.
The AI Solution: Claude Code created:
Human Role: Defined analysis priorities, reviewed chart effectiveness, tested interactivity, identified bugs (especially the radius issue), and requested UX improvements. Time spent: 4 hours across multiple iterations.
Read full visualization development story • Chart creation template
The Challenge: Stakeholders need to understand not just what was delivered, but what insights are possible. Plus, documenting the process itself for future reference and learning.
The AI Solution: ChatGPT analyzed the generated datasets and:
Human Role: Created this process documentation (process.md) capturing every step, every prompt, every iteration, and every lesson learned. This document you're reading now. Time spent: 3 hours.
The Challenge: Transform all the work—analysis, visualizations, and insights—into a professional proposal that addresses every RFP requirement while showcasing the innovative AI-driven approach.
The AI Solution: Claude Code created a comprehensive proposal response including:
Human Role: Made strategic decisions about tech stack (Azure over AWS, OAuth over basic auth), refined assumptions, ensured alignment with organizational constraints, and iterated through multiple revisions to perfect the proposal. Time spent: 3 hours across multiple iterations.
The Challenge: All the deliverables exist, but they need to be woven into a cohesive learning experience that teaches three distinct audiences how to leverage AI for RFP responses.
The AI Solution: Claude Code created a comprehensive tutorial system:
Human Role: Designed the learning experience, structured the narrative for maximum impact, ensured all links and references worked correctly, and manually created the GitHub Action for deployment (AI couldn't create workflows without permission). Time spent: 4 hours including debugging.
The Challenge: The proposal framework existed, but it needed to be filled with actual company information, team credentials, project references, and legal documents. The pre-sales team shared 580+ files (~1GB) of company documents—CVs, project reports, certifications, and more. Manually extracting and organizing this information would take days.
The AI Solution: Used Codex CLI with gpt-5.1-codex (high reasoning mode) to:
in2csv), PDFs (using pdftotext), and Word docs (using pandoc)The AI agent's reasoning process revealed interesting patterns:
Human Role: Provided the document corpus, reviewed extracted information for accuracy, made final decisions on team composition, sanitized confidential information before committing, and manually collated the final proposal information. Time spent: 3 hours across multiple iterations.
This wasn't about AI replacing human work—it was about finding the right collaboration pattern. Here's what emerged as the most effective workflow:
| Phase | Human Responsibilities | AI Responsibilities |
|---|---|---|
| Strategy & Planning |
• Define objectives • Identify constraints • Prioritize requirements • Assess organizational context |
• Generate comprehensive question lists • Identify potential gaps • Suggest analysis approaches • Pattern recognition in documents |
| Content Creation |
• Define content strategy • Review for accuracy • Ensure brand alignment • Add domain expertise |
• Draft initial content • Structure information • Format consistently • Generate variations |
| Technical Implementation |
• Define requirements • Test functionality • Identify edge cases • Verify correctness |
• Write code • Implement features • Debug (with guidance) • Optimize performance |
| Quality Assurance |
• Define quality criteria • Test user experience • Verify domain accuracy • Final approval |
• Self-review code • Check consistency • Suggest improvements • Run automated tests |
The most effective prompts weren't single shots—they were conversations:
Through this intensive week of collaboration, clear patterns emerged about what AI agents excel at, where they struggle, and how humans can most effectively guide them.
What Worked:
What Didn't Work:
The Takeaway: Use AI to accelerate documentation and research, but reserve strategic decisions for human expertise. AI makes you 5x faster at execution, not 5x better at strategy.
What Worked:
What Didn't Work:
The Takeaway: AI excels at implementation but needs human oversight for edge cases and best practices. The workflow becomes: Human architects → AI implements → Human refines → AI iterates. This cycle is 10x faster than solo coding.
Where AI Excels:
Where AI Struggles:
Where Human Expertise Helps:
The Takeaway: AI doesn't replace expertise—it amplifies productivity for those who already know what they're doing. The 10x gain comes from eliminating grunt work, not eliminating knowledge requirements.
The most critical lesson: AI outputs require verification. This isn't about "trust but verify"— it's about understanding that AI confidently produces outputs without self-assessment. The Observable Plot radius bug is a perfect example: AI generated code that looked right, ran without errors, and was confidently wrong.
Best Practice: Always ask AI to verify its own work by checking documentation, taking screenshots, or running tests. "Make it work" is not enough—you need "Make it work AND prove it works."
In 10 days of part-time work (~23 human hours), here's what emerged from the human-AI collaboration:
This isn't just about speed—it's about possibility. A comprehensive RFP response with custom data visualizations was previously only feasible for well-funded teams or large agencies. Now, a knowledgeable individual with AI tools can produce comparable quality in a fraction of the time and cost.
This democratizes access to complex work. Small businesses can compete with larger firms. Individual consultants can take on ambitious projects. Pre-sales teams can explore more opportunities. The bottleneck shifts from "Do we have capacity?" to "Is this worth pursuing?"
This project demonstrates that the future of work isn't about AI replacing humans or humans refusing AI. It's about finding the right collaboration pattern—where AI handles the grunt work and humans provide judgment, strategy, and quality control.
The teams that thrive will be those that learn to orchestrate AI effectively—knowing what to delegate, what to verify, and when to override. This isn't a skill you can ignore anymore. It's becoming as fundamental as knowing how to search Google or use a spreadsheet.
Start experimenting. Start learning. The tools are here. The opportunity is now.