Proposal Generation

UI features covering the core flows of proposal generation using Augier AI

Exhibit A

Bid Pipeline

After a user finds a relevant opportunity, they can begin the process of bidding by adding that opportunity to the ‘Bid Pipeline’. This is a repository of all solicitations (opportunities) the user wishes to create proposals for.

Additionally, it also holds all proposal drafts and ongoing bids under the ‘proposals’ tab.

Internal filters to streamline searching.

User have a habit of ‘wish listing’ or ‘favouriting’ several opportunities. Making it necessary to be able to search within their pipeline as well.

Separate tabs for proposals

A distinct space for ongoing proposals and drafts was required, so as to decrease pipeline clutter and confusion.

Exhibit B

Proposal Overview

Once a user starts generating a proposal, an overview dashboard is automatically created for them. This consists of the following things:

Quick Actions Panel

The ability to add teammates, write quick notes and more.

RFP Summary

A consolidated view of all the important details from the opportunity.

Task Manager

Assign tasks to teammates, keep track of progress, and stay up to date with all requirements.

Exhibit C

Compliance Matrix

The compliance matrix is a checklist that includes all compliance requirements and helps a user stay compliant and on track with their content in the proposal. It is automatically created using RAG models to parse through all attachments in the solicitation. One of the most important features in the entire proposal generation flow.

92% of users rated this feature as a 'necessity'

Include every compliance detail through a collapsible accordion.

No need to go to a different software for cross-referencing

Navigate to the specific document portions using chips.

Interactive chips help navigation easy, and helps with discoverability of fine-print and hidden portions.

Users hate making Excel sheets manually.

Creating a compliance matrix from scratch manually usually takes about 1-2 weeks. It is mostly created on Google Sheets, or Microsoft Excel, or other spreadsheet software.

Exhibit D

Proposal Outline + Document Editor

An detailed customisable outline/breakdown of all content sections of the proposal. A user can choose to edit the outline based on their expertise and preferences. They can choose to edit specific portions of the outline in silos as well.

This feature significantly impacted the product performance.

84% of users deemed this feature 'very important'

Working on a single document with other people on it, is very frustrating.

Due to concerns while collaborating on a single document, a separate section of editing portion wise was created. 

Editing a specific portion without affecting the main proposal document

This space allows different members of a team to work on their own on different portions of the document. Making the process of simultaneous editing hassle free.

Using AI Daemons and EPIs

This is the third version of the proposal editor interface. A major update is shifting from a docked Ai chatbot to a content aware in-text Ai agent.

Integrated Accessibility

The document editor has been specifically made to include audio descriptions, speech to text capabilities and more.

Foundations: Flows, Iterations, and Affordances

There were several iterations, and repeated rounds of testing to produce the features above.

See full design process

See full design process

See full design process

Looking back

Well, I'm still hustling and building, but let's summarize this case.

Learns:

  1. Navigating New AI UI Paradigms – Designing AI-driven interfaces required a balance between automation and user control, ensuring transparency and trust while enhancing efficiency.

  2. Task Management as a Core Workflow – Integrating task management within the proposal generation process streamlined user workflows, reducing friction in multi-step document creation.

  3. Multi-User Collaboration is Essential – Real-time co-working and co-editing capabilities significantly improved team efficiency, but required thoughtful UX considerations for role-based permissions and version control.

  4. Defining a New User Category – Targeting American small businesses as a B2B SaaS solution meant addressing a diverse range of workflows, technological familiarity, and business needs.

  5. Bridging AI and Human Input – Users valued AI assistance but needed clear affordances to guide and override AI-generated content when necessary, reinforcing the importance of hybrid AI-human interaction.

  6. Iterative Testing Drove Refinements – Usability testing revealed crucial improvements in user flow, ensuring the feature was both intuitive and adaptable to different business needs.

  7. Adoption Challenges in New Markets – Educating small businesses on the benefits of AI-powered proposal generation was key to driving adoption, requiring a strong onboarding experience and trust-building strategies.

    These insights shaped the final design, ensuring a robust, user-friendly, and scalable solution for small businesses entering the AI-powered proposal workflow space.

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