AI-Powered Tenders & Pricing Portal
Respond to More Tenders, Faster - With an AI-Powered Pricing Portal
Transform the way your team handles tender and bid responses. Automate pricing, streamline admin, and craft consistent, accurate replies using your own AI-trained knowledge base — all from one central platform.
Ready to transform the way you complete your tender responses? Let’s create your AI-Powered Pricing Portal today.
Tender Responses Don’t Need To Be This Hard.
Manual pricing calculations. Reformatting spreadsheets. Rewriting the same answers. Sound familiar?
Most pricing and tender teams spend far too long on:
- Complex data entry and formatting
- Manually calculating servicing and purchase costs
- Repeatedly writing the same responses
- Chasing consistency across tenders
- Delayed turnaround due to admin overload
It’s time to automate the grunt work and focus on the win.
What is an AI-Powered Pricing Portal?
It is a smart, secure, easy-to-use digital platform designed to:
- Reduce time spent completing each tender response
- Improve accuracy and consistency
- Increase tender capacity without growing your team
- Handle a wider volume of requests — faster
How It Works
Built for Real-World Tender Workflows
AI agents offer a host of benefits that can transform how your business operates:
Craft professional tender responses - using your internal AI agent trained on company documents, policy and tone.
Upload tender files and pricing information in most formats - Word, Excel, PDF, CSV, image, plain text, or specific cells from an excel sheet.
Auto-match equipment, products and servicing needs - using your internal asset master list, with smart descriptions and industry terminology.
Auto-calculate pricing - using pre-configured editable pricing and servicing tables matched to your master list and the tender requirements.
Review, export and submit - faster with greater accuracy.
Complete all this from your own designated online portal - which can be designed specifically for your user needs, user access requirements, and data security.
Building & Construction Customer Case Study
The Challenge
Responding to tenders, whether large or small, was a time-intensive and laborious process for the tenders and pricing team of our client. Key tasks such as pricing calculations, data formatting, spreadsheet entries, and crafting tailored responses required significant effort. Repeatedly answering common questions while maintaining accuracy and customisation further slowed the process, limiting the team’s ability to handle multiple tenders simultaneously.
Our Solution
Digital Mavens implemented a cutting-edge AI solution to streamline and enhance the tender response process:
- Knowledge-Based AI Agents: We developed AI-powered agents connected to company-specific documents, enabling accurate, tailored answers to tender questions.
- Automated Document Analysis: Our solution processed multi-format files (Word, Excel, CSV, PDFs) to analyse tender requirements, match resource needs, calculate pricing, and generate exportable responses.
- Custom Online Interface: We created a user-friendly portal to manage and streamline the tender response process using AI.
The Results
Our AI solution transformed the team’s workflow by significantly reducing the time and effort required to complete tenders. This resulted in:
- Increased productivity and faster response times without compromising quality or consistency.
- A higher volume of small to medium tenders successfully completed, driving revenue growth.
- Enhanced capacity for the team to take on more tenders, maximising overall efficiency.
Who It Is Ideal For?
- Tender-heavy or project-based industries
- Companies in infrastructure, manufacturing, equipment, or servicing
- Teams with high volume, repetitive pricing or information requests
- Businesses looking to future-proof internal operations with AI
- Choose the appropriate GPT model based on complexity, volume, task requirements, platform alignment, performance requirements, privacy and data security requirements, and pricing.
- Define integration points (e.g., websites, social media, business platforms or internal knowledge systems).
- Develop a conversational flow outline to structure the agent’s interactions.
- Establish metrics for success.
- Develop clear communication plan for the business (internally and externally)
- Train the AI on business-specific data.
- Set up a tone and language style guide to ensure the AI aligns with the company or brand’s voice.
- Create fallback responses and escalation processes.
- User testing to assess accuracy, tone, and functionality.
- Gather feedback from internal teams or beta users.
- Refine conversational flows, responses, and edge-case handling.
- Integrate the AI agent with chosen platforms.
- Test integration points to ensure smooth operation.
- Deploy the AI agent in a phased approach to monitor performance.
- Train staff on how to use and monitor the AI agent effectively.
- Provide guidance on updating and maintaining the AI's knowledge base.
- Educate employees on how to interpret AI-driven insights or reports.
- Provide wider business education and communications on AI Agents and their role within the business.
- Monitor metrics like accuracy, customer satisfaction, and resolution rates.
- Regularly update the AI with new business information or customer insights.
- Fine-tune the AI Agent’s responses based on feedback and performance data.
- Schedule periodic reviews to align the AI agent with evolving business goals.
- Identify your business use case/s
- Identify key stakeholders, champions, owners and advocates
- Define the key objectives and goals for the AI agent/s
- Collect the relevant business data, knowledge, workflows and FAQ
- Choose the appropriate GPT model based on complexity, volume, task requirements, platform alignment, performance requirements, privacy and data security requirements, and pricing.
- Define integration points (e.g., websites, social media, business platforms or internal knowledge systems).
- Develop a conversational flow outline to structure the agent’s interactions.
- Establish metrics for success.
- Develop clear communication plan for the business (internally and externally)
- Train the AI on business-specific data.
- Set up a tone and language style guide to ensure the AI aligns with the company or brand’s voice.
- Create fallback responses and escalation processes.
- User testing to assess accuracy, tone, and functionality.
- Gather feedback from internal teams or beta users.
- Refine conversational flows, responses, and edge-case handling.
- Integrate the AI agent with chosen platforms.
- Test integration points to ensure smooth operation.
- Deploy the AI agent in a phased approach to monitor performance.
- Train staff on how to use and monitor the AI agent effectively.
- Provide guidance on updating and maintaining the AI's knowledge base.
- Educate employees on how to interpret AI-driven insights or reports.
- Provide wider business education and communications on AI Agents and their role within the business.
- Monitor metrics like accuracy, customer satisfaction, and resolution rates.
- Regularly update the AI with new business information or customer insights.
- Fine-tune the AI Agent’s responses based on feedback and performance data.
- Schedule periodic reviews to align the AI agent with evolving business goals.
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