Ian Roberts / Portfolio 2025

AUTOGENAI: Designing a Human-in-the-Loop Workflow for Federal RFP Automation

June 2025

Enable proposal teams to streamline RFP responses while maintaining full compliance control through human-in-the-loop design

01 / Project overview

I led the design of an AI-powered workflow that automates RFP analysis and response outline generation for US federal contracting. Over a 6-week period (1 discovery sprint + 2 technical sprints), I guided the design from initial research through to beta release. From the outset, the project was framed around human-in-the-loop design: ensuring that automation accelerated the process while preserving compliance accuracy, user trust, and human judgment.

02 / Challenge

Federal RFPs are dense, compliance-heavy documents. Bid writers spend 2–3 days manually reading, extracting requirements, mapping sections, and building response outlines and compliance matrices. This manual process creates bottlenecks, increases error risk, and slows down proposal timelines.

The challenge was not only to automate these steps but to design an AI workflow that users would trust. In a high-stakes compliance environment, automation without human oversight is unacceptable. The solution required a collaborative model where AI accelerates extraction and structuring, and humans validate, refine, and ensure compliance.

03/ Process

With only a single discovery sprint to understand the problem space and pull together a design I leveraged internal experts, existing research and insights and AI prototyping tools to accelerate my progress.

1. Understanding Workflows & Risks

• Conducted stakeholder interviews with internal bid professionals from our US team to uncover pain points, trust thresholds, and compliance concerns.

• Observed current workflows to identify which tasks could safely be automated and where human checkpoints were essential.

• Collaborated with tech and AI engineers to map the technical flow from ingestion → parsing → requirement extraction → outline generation, overlaying intervention points for human review.

2. Validating Data Foundations

A key principle when designing with generative AI is to first explore the boundaries of what the model can and cannot do. Before committing to interface design, I make it a priority to understand the accuracy, consistency, and structure of the outputs. In this project, where AI was used to extract requirements from RFP documents, I began by assessing how well requirements could be identified and categorised.

• Requirement Extraction Modelling: Worked with engineers to generate early sets of AI-extracted requirements.

• Data Quality Review: Evaluated whether outputs aligned with the standard US federal bid structure, checking accuracy, completeness, and categorisation.

Findings:

When looking at the test generation data I saw a couple of challenges. The LLM often misinterpreted requirement boundaries: sometimes combining two distinct requirements into one, other times splitting a single requirement into multiple fragments. This led for me to consider adding in editing functionality for users to ‘clean up’ requirements if needed prior to generating the response outline.

3. Journey Mapping and lo-fi wireframes

I mapped end-to-end user flows for a 2-step flow that alternated between automation phases and guided validation steps and shared these internally for review before moving into a quick lo-fi wire framing exercise (done as a group exercise with the product trio) to visualise the flows and functionality across the views.

4. Rapid Prototyping & Validation

The screens I designed included complex interactive elements, so I leveraged newly available AI-powered prototyping tools to create high-fidelity, testable prototypes. This allowed me to validate interactions and gather feedback at speed without waiting for full development.

• Figma Make for Speed: Leveraged Figma Make to rapidly create high-fidelity prototypes of critical workflows.

• Team Testing: Used these prototypes to present and test ideas with cross-functional team members, quickly gathering feedback on flows, interactions, and usability.

• Client Validation: Shared selected prototypes with Discovery Partnership clients, enabling us to validate assumptions with real bid writers and gather early feedback on usability, trust, and fit within existing workflows.

• High-Fidelity Validation: Able to test screen designs and interaction patterns at near-production quality without lengthy build cycles.

• Developer Collaboration: In some cases, developers worked directly from prototypes, reducing translation overhead and ensuring alignment between design intent and implementation.

04/ Solution

The result was an end-to-end human-in-the-loop workflow for RFP response preparation:

AI accelerates: ingestion, parsing, requirement mapping, and automated compliance matrices.

Humans validate: guided checkpoints allow users to confirm extractions, correct errors, and ensure compliance.

Signalling Expertise: The workflow was deliberately designed to signal to users where their expertise was most needed—for example, validating nuanced requirements, correcting categorisation, and ensuring compliance fit. This reduced cognitive load while reinforcing users’ role as the quality gatekeepers.

Shared Control: The flow intentionally alternated between AI taking control in automation-heavy phases (extraction, structuring) and humans exercising agency in validation steps. This balance ensured efficiency while keeping the final output trustworthy and high-quality.

Highlighting Vision vs. MVP Compromise: The original design called for colour-coded highlighting of requirements directly within RFP text, closely mirroring how writers naturally annotate documents. Due to technical limitations, the MVP shipped with chunk-based selection instead. While this compromise allowed the feature to launch on time, highlighting remains a future design goal to better match user mental models and deliver a more intuitive validation experience.

05/ Impact

Beta Release: Launched to early users, who responded positively to the ability to move faster without losing control.

Client Validation: Discovery Partnership clients played a key role in testing early prototypes, providing feedback that shaped critical HITL features such as requirement clean-up (merge/split/delete) and multi-category assignment. This early involvement increased confidence in adoption.

Adoption Drivers: Feedback confirmed that users valued how the workflow clearly signalled when their expertise was needed. This reinforced their sense of control while reducing the burden of unnecessary oversight.

Trust in Collaboration: Users reported greater confidence in AI outputs when they understood the alternating flow of AI handling automation and humans providing quality assurance.

06/ Key Takeaway

This project demonstrates how human-in-the-loop design transforms automation from a risk into a partnership. By intentionally designing intervention points, transparency mechanisms, and override controls, we created an AI system that bid writers could trust in a high-stakes compliance environment.