As generative AI becomes an increasingly critical factor in staying competitive, organizations are faced with a pressing decision: build custom solutions in-house or invest in tools designed by industry experts. We want to share what we have told many potential customers who are weighing these options so you can make the best decision for your organization.
Why Build vs. Buy Matters
Your firm’s success depends on focusing resources where they drive the most value. While building an AI-assisted proposal writing tool in-house might initially appear appealing and achievable, it’s essential to understand the hidden complexities and costs that may accompany such a decision.
Here are some factors to consider as you weigh your options:
- Specialized Skills and Resources: Building an AI-assisted tool requires highly specialized skills, particularly from data scientists—a talent pool that is in short supply. Without the right expertise, you risk creating a product that is too general to deliver real value, and instead ending up with only a minimally viable product (MVP) that falls short of expectations.
- Cost consideration: AI evolves rapidly, requiring continuous expense on updates, improvements, and maintenance. This is not a “one-and-done” effort but an ongoing commitment that will require considerable resources diverted away from your core competencies and revenue-generating activities.
- Dedicated Focus: You can’t assign a project of this complexity to engineers and SMEs who can only work on it part-time. A successful solution requires a focused product vision, and a team dedicated to staying current with the fast-changing landscape of AI.
- Time-to-Value: Developing a tool from scratch can take years, delaying ROI and putting you behind competitors already leveraging off-the-shelf solutions.
- Evolving Technology: Generative AI is still a relatively new and rapidly evolving technology that demands specialized expertise to navigate effectively. It brings both unique risks and rewards that organizations must carefully and continuously evaluate, bringing an additional layer of complexity.
Essential Practices for Building an AI Proposal Writing Tool
For organizations considering the “build” option, understanding the complexities involved is crucial. Each step requires careful planning, resource allocation, and ongoing investment. Below is a breakdown of the critical practices involved in building an AI-assisted proposal writing tool from scratch.
Caution: This list is just a start and won’t result in a fully built AI-assisted proposal writing tool!
Prerequisites
Before starting, you must ensure familiarity with:
- Large Language Models (LLMs), prompt engineering techniques, and managing hallucinations.
- Security and compliance requirements, for example handling Controlled Unclassified Information (CUI).
- Tools like Vector Databases, SQL, and Blob Storage.
- The Federal Acquisition Regulation (FAR), especially the Uniform Contract Code (UCC).
- Proposal writing best practices, solicitation shredding, and compliance validation.
What you need to do
1. Build a Multidisciplinary Product and Engineering Team
Assemble a skilled team across product, analysts, UX, UI, integration, data scientists, AI/prompt engineers, QA, and more that are experienced in the approaches necessary to build a Generative AI tool.
Why you need to do it
The lack of a diverse development team will lead to a longer development cycle and fewer advanced features needed to maximize ROI.
2. Set Up the AI Environment and LLM infrastructure
Set up secure access to data ingestion and processing tools, such as vector databases for semantic search, blob storage for artifact repository, SQL database for metadata and reporting, and pipelines for pre-processing and serving LLM queries.
Without deep expertise and investment in cloud architecture and AI model deployment, organizations face a greater risk of unauthorized access to sensitive client information.
3. Categorize, Chunk, and Classify Proposal Artifacts
Build a system to categorize and break down proposal artifacts into manageable chunks to support semantic search, classification, and AI-powered analysis.
Failing to process and categorize proposal artifacts effectively can lead to sub-par responses and missed opportunities, as critical requirements may be lost in unstructured data.
4. Implement Data Security and Compliance
Configure your system to handle security and compliance requirements like encryption, access controls, secure workflow, and CUI compliance.
Without having all aspects of data security covered, firms risk data leakage, security incidents, and/or IP theft.
5. Parse RFx Documents
RFx documents have a high degree of variability, and you need an entire subsystem to convert highly unstructured data into a structure that is accessible by LLMs.
Without being able to accurately shred solicitation documents, your users won’t be able to easily create a compliant response.
6. Mitigate Hallucinations
LLMs can produce inaccuracies. Given the emphasis on accurate information in proposal drafts, you need patterns in place to reduce inaccuracies.
Without robust subsystems to minimize hallucinations, your users won’t trust the output the tool produces.
7. Generate Reports and Analytics
Develop subsystems for tracking the proposal lifecycle, team collaboration, compliance processing, and more.
Gaps in these subsystems can lead to missed deadlines and inefficiencies.
8. Plan for Underlying LLM Updates
Plan for, test, and implement ongoing updates to LLMs with new versions to increase output and processing quality.
Failing to keep underlying models updated leads to outdated capabilities and a competitive disadvantage.
9. Build the Workflow Orchestration and User Experience (UX)
Ensure the tool aligns with and enhances existing user workflows, minimizes disruptions, and is user-friendly.
This process requires hundreds of hours of research to align with users’ workflow as a poor UX can lead to low usage rates and limit ROI.
10. Improve Writing Quality, Tone, and Personalization
LLMs tend to produce homogenized content, lacking the diversity and unique perspectives found in human writing. You will need to build a writing quality subsystem that not only deals with these inherent limitations but also infuses the best practices of proposal writing. i.e. Shipley.
Neglecting to refine writing quality, tone, and personalization can result in generic proposals with too much fluff and repetitiveness that fail to resonate with evaluators.
11. Train Users and Provide Tech Support
Develop a robust user training program and provide ongoing technical support so users can leverage the tool effectively.
Without ongoing commitment training, organizations could be met with higher user error rates and increased resistance to usage.
12. Integration with External Tools
Integrate with additional tools (e.g. SharePoint, CRM, etc.) users use in their workflow.
Organizations have data spread across systems. Generative AI solutions need access to it or risk going unused and disconnected.
13. Continuously Improve
Allocate resources for ongoing improvement ensuring the tool evolves with user needs and advancements in AI technology.
By not investing in continuous improvement, organizations might miss out on opportunities to innovate, enhance features, or face greater security risks.
Conclusion
Building an AI-assisted proposal writing tool is a significant undertaking that requires diverse expertise, skilled resources, and a long-term commitment, or risks not achieving the intended results. The considerations outlined above highlight why many organizations opt for specialized off-the-shelf tools that allow them to focus on their core competencies and achieve quicker results.
Investing in a tool like pWin.ai minimizes upfront development costs, ensures continuous updates, and enables quicker deployment, saving both time and money compared to building a custom solution.
by Vishwas Lele and Drew Hartley