15 Ways AutogenAI Users Are Winning Faster with Research Assistant

The Evolution of Proposal Research in the Age of AI 

Effective research is critical for any successful bid response, yet 37% of professionals say they spend the most time conducting research—often sifting through endless reports, spreadsheets, and scattered internal documents. Despite the potential of AI, there’s still a major hurdle: leading AI models have an average hallucination rate of 20%, making their reliability a serious concern. 

As a result, only 5% of organisations are using AI for proposal-related research, fearing inaccuracies and misinformation. 

But, this is changing with the introduction of Agentic AI. Unlike traditional AI, which just pulls data, Agentic AI finds relevant information, checks its accuracy, and puts it into context. That means less time spent digging through reports and spreadsheets—and more reliable insights.

At AutogenAI, we are actively helping businesses, governments, and proposal professionals respond to tenders faster and more effectively with this new approach. 

How AutogenAI is Redefining Research for Bids and Proposals 

AutogenAI is transforming how businesses manage proposal research by using multiple large language models (LLMs) instead of relying on a single AI model. This approach helps deliver more accurate, relevant, and context-aware information tailored to each prompt. 

Traditional search engines and AI tools focus on retrieving information or filling gaps when data is missing—often leading to inaccuracies or hallucinations. Agentic AI takes it further by actively navigating, interrogating, and synthesising data from multiple sources. AutogenAI’s intelligent search agents assess credibility, cross-reference information, and refine results in real time, delivering source-backed research that’s both reliable and tailored to each bid. 

AutogenAI’s agentic-powered Research Assistant—the first AI-driven tool built specifically for proposal development—helps users access accurate, relevant insights faster. This streamlines the research process, reduces manual effort, and enables organisations to create more competitive, high-quality bids with confidence.  

How AutogenAI’s Research Assistant Leverages Agentic AI: 

1. Multi-Source Retrieval: Searches across internal CRMs, internal knowledge bases, and trusted internet sources for a comprehensive view.

2. Intelligent Cross-Checking: Compares data across multiple sources to ensure accuracy before presenting a response. 

3. Adaptive Model Selection: Uses specialised LLMs for different tasks, like financial data, regulatory research, and competitor analysis. 

4. Real-Time Monitoring: Continuously tracks regulatory updates, industry shifts, and competitive movements, and alerts teams proactively. 

5. Insight Generation, Not Just Extraction: Structures findings into actionable intelligence, supported by credible evidence, rather than just raw data. 

Here are some of the innovative ways you can use AutogenAI’s Research Assistant: 

1. Identifying your own capability to bid for or deliver a service 

❝ Analyse past project data, performance metrics, and internal expertise form [My library] to assess our capability to bid for and successfully deliver [service]. Compare against industry benchmarks and competitor offerings, highlighting key differentiators and proven success factors. ❞ 

2. Incorporating trends & potential developments 

❝ Search across internal reports, CRM data, and verified industry sources to identify the latest trends in [industry]. Summarise key shifts and their potential business impact. ❞ 

3. Recent regulatory & policy changes 

❝ Retrieve updates from [government portals, legal databases, and internal compliance teams] to summarise the latest regulations affecting [industry]. Provide key compliance takeaways and how it might affect [my project]. ❞ 

4. Customer’s stakeholders & goals 

❝ Analyse internal CRM records (Salesforce, HubSpot) alongside executive interviews, public statements, and annual reports to extract the top priorities of stakeholders at [customer’s company].❞ 

5. Identifying customer’s unspoken challenges 

❝ Search the web for customer reviews, news, and leadership statements to uncover their main business challenges. Summarise and suggest relevant solutions.❞ 

6. Competitors 

❝ Summarise competitor activities using competitive intelligence reports form my library, and recent news published by competitors. Identify key competitive advantages and gaps we can integrate into [RFP]. ❞ 

7. Incumbent provider analysis 

❝ Assess the current service providers for [customer’s company] using market analysis, industry reviews, and internal sales records. Identify performance gaps. Give me expertly specific advice on how to use that info in my proposal, including specific responses. ❞ 

8. Finding relevant case studies & evidence 

❝ Search internal project archives and external case studies to identify ROI metrics from similar projects in [industry]. Provide data-driven success stories. ❞ 

9. Keeping an eye on compliance & regulatory requirements 

❝ Cross-reference our internal compliance records, legal databases, and government regulations to ensure our proposal meets all necessary compliance standards. ❞ 

10. Key Performance Indicators (KPIs) 

❝ Retrieve industry-standard KPIs from benchmarking reports and compare them with internal success metrics to refine our proposal’s performance expectations. ❞ 

11. Leveraging essential public information  

❝ Identify recent government white papers, policy updates, and funding initiatives related to [industry]. Summarise key findings and their implications. Make me a SWOT analysis relative to this information.❞ 

12. Incorporating trusted market data 

❝ Search internal CRM and third-party market research to analyse the demographic landscape of [target audience]. Provide insights into evolving consumer behaviour. ❞ 

13. Identifying common pitfalls in your losing RFPs 

❝ Identify failure points in similar projects using internal post-mortem reports, industry analysis, and case studies. Summarise lessons learnt and risk mitigation strategies. ❞ 

14. Finding the best way to position yourself to win 

❝ Aggregate internal performance data, past proposal results, and customer success stories relevant to [RFP] to showcase our expertise and successful project outcomes in [industry]. ❞ 

15. Leveraging essential public information  

❝ Identify recent government white papers, policy updates, and funding initiatives related to [industry]. Summarise key findings and their implications. Make me a SWOT analysis relative to this information.❞ 

AutogenAI’s Edge: Selecting the Right AI Model and the Right Source for Every Task 

A key reason AutogenAI is at the forefront of Agentic AI usage is its ability to dynamically select the best LLM for each specific task. Unlike generic AI models that attempt to do everything with a single neural network, we analyse 16+ models to find the most optimised model for each task. 

This tailored approach ensures that every research request is handled by the most specialised AI model available, improving accuracy, reducing hallucinations, and delivering deeper insights. 

The Future of Research is Here 

Agentic AI is not just a tool—it’s a shift in how businesses use intelligence. By seamlessly integrating internal knowledge bases, external sources, and multi-model AI selection, AutogenAI is pioneering a research revolution that is faster, smarter, and more actionable than ever before. 

For organisations navigating competitive markets, regulatory landscapes, or high-stakes bidding processes, leveraging AutogenAI’s research assistant can be the difference between a winning proposal and a missed opportunity. 

The future of research isn’t just AI-powered—it’s Agentic. And AutogenAI is the only bid writing platform harnessing it for our users. 

Contact AutogenAI today to learn how we can help you create smarter, more competitive bids.