Erandi Attanayake | UX & Product design portfolio

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Erandi Attanayake

Tender AI – Transforming Bid Management Through Intelligent Automation

Overview

Purpose:

To streamline and intelligently manage the end-to-end tendering process for organizations by automating evaluation, collaboration, and bid response tasks.

Goal:

Accelerate tender decision-making.
Improve governance and compliance.
Increase bid success rates through structured, AI-driven workflows.

Extended Features Planned:

Advanced Bid Scoring Models: Customize AI-based evaluation criteria tailored to the organization's needs.
Smart Collaboration Hub: Role-based access, internal comments, and auto-reminders for smoother teamwork.
Governance Rules Engine: Auto-validate against internal policies and compliance standards.
Integrated Risk Assessment: Real-time analysis of bidder credibility and risk profiling.
Bid Performance Dashboard: Track past bids, win/loss metrics, and response times.

The problem statement

The tender response process is often inefficient, fragmented, and labor-intensive:

Teams rely on spreadsheets, documents, and email threads, making collaboration cumbersome.
Extracting key information from lengthy tender documents is time-consuming.
There is no standardized governance process to evaluate bid viability.
Teams struggle to assess win probability or understand strategic fit.

These issues result in delayed responses, increased risk of non-compliance, and lower win rates.

The solution

Tender AI solves these problems with a centralized, AI-driven platform:

Upload tender documents and automatically extract key data, including budget, deadlines, and scope.
Set up bid teams with clearly defined roles, cost structures, and partner visibility.
Run governance reviews based on custom rules and risk analysis.
Utilize AI to assess win probability (PWin), identify competitors, and evaluate strategic fit.
Manage AI knowledge base, responses, and documents in one integrated system.

UX Process

STEP 1

Define the problem statement

We identified the core user frustrations around manual workflows, lack of collaboration, and unclear governance in the tender process. From initial uploads to final decision-making, each step was riddled with inefficiencies.

Objectives

User

Access assigned tenders and related documents.
Provide inputs and collaborate on responses.
Track progress and tasks.

Admin

Set up teams, assign roles, and tasks.
Monitor timelines and cost estimations.
Manage governance review workflows.

Super Admin

Configure AI settings, rules, and access control.
Oversee governance compliance and audit logs.
Evaluate win/loss outcomes and optimize strategies.

STEP 2

Market Research

We explored top competitors and conducted interviews with bid managers, consultants, and SMEs to understand current workflows. Key findings:

Most teams use email, Excel, and SharePoint to manage bids. Tools like RFPIO, Loopio, and Proposal Software exist but lack governance intelligence. AI integration for document parsing and win probability is rare

User Personas

User persona

Admin persona

Super admin persona

HMW Analysis

User Stories

User

Admin

Super Admin

STEP 3

Identify Key Features

Tender Document Upload & Parsing
Upload tender documents and automatically extract structured data using NLP-powered parsing.
Bid Plan Timeline
Define and visualize deadlines, milestones, and action items in a centralized, shareable timeline.
Team Roles & Cost Matrix
Assign contributors, set their responsibilities, and estimate cost implications for each team member.
Document Tagging & Knowledge Base
Tag documents with metadata for easy retrieval and contribute to an evolving organizational knowledge base.
Governance Questionnaire
Auto-generate governance and compliance checklists tailored to each tender’s criteria.
Risk Analysis (PWin & SWOT)
Evaluate bid risks with built-in Probability of Win (PWin) scoring and SWOT analysis tools.
Competitor & Customer Research
Integrate insights on past wins/losses, competitor intelligence, and customer preferences for strategic advantage.
AI Persona & Tone Configuration
Configure the AI assistant to adopt specific personas and communication styles aligned with your proposal tone.

Identifying Assumptions and Constraints

Assumptions:

Users are familiar with basic document upload and collaboration tools.
Each organization has a defined governance structure.
AI models can be customized to organization-specific language and tone.

Constraints:

Limited budget for external APIs.
Need to comply with GDPR and regional data privacy laws.
AI accuracy depends on training data quality.

STEP 5

Screens I Designed

1. Tender Intake

Entry Points:

Upload tender documents

Paste tender link

Manually enter tender details

System Action:

AI parses and extracts key details

User reviews and can override or edit extracted data.

Jane Thomas

Manager

Jane Thomas

Manager

2. Opportunity Creation

Extracted data populates a new Opportunity record.
User applies filters

Tender Resources

Tender Type

Jane Thomas

Manager

3. Opportunity Analysis

System displays:

Tender details

Potential response likelihood (AI scoring)

Matching internal products/services

Strategic fit & deliverability assessment

Decision Point:

Convert to Tender → proceed to tender preparation

Mark as Low Chance → archive or deprioritize

Jane Thomas

Manager

Opportunity Analysis

4. Response Planning

AI generates Response
User reviews & edits assignments.

Tender Description

Jane Thomas

Manager

5. Document & Knowledge Management

The system stores all tender-related documents.
Links to Knowledge Base for past responses, templates, and compliance docs.

Jane Thomas

Manager

6. Governance & Compliance

Governance review checklist:

Compliance requirements

Approval workflows

Governance-related Q&A

All steps tracked for audit trail.

Jane Thomas

Manager

7. AI Settings Customization

Pre-filled company profile:
Mission statement & competitor references
Company Ethos Reference

Jane Thomas

Manager

Jane Thomas

Manager

Jane Thomas

Manager

STEP 4

Prioritize and Simplify

Success Metrics

50% faster document parsing time.
80% task completion rate across teams.
70% user satisfaction in usability surveys.
Increase win rate by 20% post-launch.

Key Usability Findings

Users appreciated document parsing automation (saved hours).
Governance review created clarity on go/no-go decisions.
Some Admins were unclear about AI tone settings: added tooltips.
Team Setup table needed bulk edit: added in V2.

Learnings & Outcomes

AI customization (persona, tone, ethos) added a strategic advantage.
Embedding governance early prevented costly late-stage rejection.
Centralizing research improved team trust and focus.
MVP helped the client move from ad-hoc bidding to a scalable tender strategy.
Tender AI is now positioned as a data-driven, collaborative, and AI-enhanced platform that increases win rates while reducing operational friction.

Tender AI – Transforming Bid Management Through Intelligent Automation

Erandi Attanayake

Overview

Purpose:

To streamline and intelligently manage the end-to-end tendering process for organizations by automating evaluation, collaboration, and bid response tasks.

Goal:

Accelerate tender decision-making.
Improve governance and compliance.
Increase bid success rates through structured, AI-driven workflows.

Extended Features Planned:

Advanced Bid Scoring Models: Customize AI-based evaluation criteria tailored to the organization's needs.
Smart Collaboration Hub: Role-based access, internal comments, and auto-reminders for smoother teamwork.
Governance Rules Engine: Auto-validate against internal policies and compliance standards.
Integrated Risk Assessment: Real-time analysis of bidder credibility and risk profiling.
Bid Performance Dashboard: Track past bids, win/loss metrics, and response times.

The problem statement

The tender response process is often inefficient, fragmented, and labor-intensive:

Teams rely on spreadsheets, documents, and email threads, making collaboration cumbersome.
Extracting key information from lengthy tender documents is time-consuming.
There is no standardized governance process to evaluate bid viability.
Teams struggle to assess win probability or understand strategic fit.

These issues result in delayed responses, increased risk of non-compliance, and lower win rates.

The solution

Tender AI solves these problems with a centralized, AI-driven platform:

Upload tender documents and automatically extract key data, including budget, deadlines, and scope.
Set up bid teams with clearly defined roles, cost structures, and partner visibility.
Run governance reviews based on custom rules and risk analysis.
Utilize AI to assess win probability (PWin), identify competitors, and evaluate strategic fit.
Manage AI knowledge base, responses, and documents in one integrated system.

UX Process

STEP 1

Define the problem statement

Objectives

User

Access assigned tenders and related documents.
Provide inputs and collaborate on responses.
Track progress and tasks.

Admin

Set up teams, assign roles, and tasks.
Monitor timelines and cost estimations.
Manage governance review workflows.

Super Admin

Configure AI settings, rules, and access control.
Oversee governance compliance and audit logs.
Evaluate win/loss outcomes and optimize strategies.

STEP 2

Market Research

We explored top competitors and conducted interviews with bid managers, consultants, and SMEs to understand current workflows. Key findings:

User Personas

User persona

Admin persona

Super admin persona

HMW Analysis

User Stories

User

Admin

Super Admin

STEP 3

Identify Key Features

Tender Document Upload & Parsing
Upload tender documents and automatically extract structured data using NLP-powered parsing.
Bid Plan Timeline
Define and visualize deadlines, milestones, and action items in a centralized, shareable timeline.
Team Roles & Cost Matrix
Assign contributors, set their responsibilities, and estimate cost implications for each team member.
Document Tagging & Knowledge Base
Tag documents with metadata for easy retrieval and contribute to an evolving organizational knowledge base.
Governance Questionnaire
Auto-generate governance and compliance checklists tailored to each tender’s criteria.
Risk Analysis (PWin & SWOT)
Evaluate bid risks with built-in Probability of Win (PWin) scoring and SWOT analysis tools.
Competitor & Customer Research
Integrate insights on past wins/losses, competitor intelligence, and customer preferences for strategic advantage.
AI Persona & Tone Configuration
Configure the AI assistant to adopt specific personas and communication styles aligned with your proposal tone.

Identifying Assumptions and Constraints

Assumptions:

Users are familiar with basic document upload and collaboration tools.
Each organization has a defined governance structure.
AI models can be customized to organization-specific language and tone.

Constraints:

Limited budget for external APIs.
Need to comply with GDPR and regional data privacy laws.
AI accuracy depends on training data quality.

STEP 5

Screens I Designed

1. Tender Intake

Entry Points:

Upload tender documents

Paste tender link

Manually enter tender details

System Action:

AI parses and extracts key details

User reviews and can override or edit extracted data.

Jane Thomas

Manager

Jane Thomas

Manager

2. Opportunity Creation

Extracted data populates a new Opportunity record.
User applies filters

Tender Resources

Tender Type

Jane Thomas

Manager

3. Opportunity Analysis

System displays:

Tender details

Potential response likelihood (AI scoring)

Matching internal products/services

Strategic fit & deliverability assessment

Decision Point:

Convert to Tender → proceed to tender preparation

Mark as Low Chance → archive or deprioritize

Jane Thomas

Manager

Opportunity Analysis

4. Response Planning

AI generates Response
User reviews & edits assignments.

Tender Description

Jane Thomas

Manager

5. Document & Knowledge Management

The system stores all tender-related documents.
Links to Knowledge Base for past responses, templates, and compliance docs.

Jane Thomas

Manager

6. Governance & Compliance

Governance review checklist:

Compliance requirements

Approval workflows

Governance-related Q&A

All steps tracked for audit trail.

Jane Thomas

Manager

7. AI Settings Customization

Pre-filled company profile:
Mission statement & competitor references
Company Ethos Reference

Jane Thomas

Manager

Jane Thomas

Manager

Jane Thomas

Manager

STEP 4

Prioritize and Simplify

Success Metrics

50% faster document parsing time.
80% task completion rate across teams.
70% user satisfaction in usability surveys.
Increase win rate by 20% post-launch.

Key Usability Findings

Users appreciated document parsing automation (saved hours).
Governance review created clarity on go/no-go decisions.
Some Admins were unclear about AI tone settings: added tooltips.
Team Setup table needed bulk edit: added in V2.

Learnings & Outcomes

AI customization (persona, tone, ethos) added a strategic advantage.
Embedding governance early prevented costly late-stage rejection.
Centralizing research improved team trust and focus.
MVP helped the client move from ad-hoc bidding to a scalable tender strategy.
Tender AI is now positioned as a data-driven, collaborative, and AI-enhanced platform that increases win rates while reducing operational friction.