Production is going on.
US Accounting Firm Estimates 90% Faster Proposal Turnaround
BCT connected AI drafting, automated citation validation and branded Word document generation to reduce manual proposal preparation.
Client
Challenge
Preparing a proposal took approximately six hours of manual assembly. Content had to be located, adapted and presented consistently, while repeated formatting created rework and variation between documents.
Accuracy made the requirement more demanding. An incorrect or unverifiable statement in a client proposal could create professional-standards and reputational risk. Faster drafting would have limited value if teams could not trace factual claims to trusted material.
The firm therefore needed more than an AI writing tool. It needed a connected process that could use approved content, validate factual claims and produce a finished Word document in the firm’s exact style. The system also had to accommodate content and template changes without making ongoing maintenance costly.
Accuracy made the requirement more demanding. An incorrect or unverifiable statement in a client proposal could create professional-standards and reputational risk. Faster drafting would have limited value if teams could not trace factual claims to trusted material.
The firm therefore needed more than an AI writing tool. It needed a connected process that could use approved content, validate factual claims and produce a finished Word document in the firm’s exact style. The system also had to accommodate content and template changes without making ongoing maintenance costly.
Solution
BCT designed and delivered an AI-powered proposal generation platform on Microsoft Azure. Through a guided wizard, the proposal team provides the details needed for each pursuit. AI drafts the individual sections, and a document assembly engine brings them together into a complete, branded proposal.
The generation process uses the prospective client’s details and the firm’s approved content library. Retrieval-augmented generation supplies relevant source material, while the AI is required to cite every factual claim. Automated citation validation checks those claims against their sources, creating a control intended to prevent unsupported statements from entering client documents.
The document engine handles the formatting work that previously followed drafting. OOXML assembly produces a personalised cover, automatic table of contents, correct page numbering, branded footer and logo, and the firm’s prescribed styles. The output is designed to be ready for use without manual reformatting.
The architecture combines Azure OpenAI with Python-based Azure Functions, Azure Cosmos DB and Azure Blob Storage. Power Apps provides the proposal wizard, while Power Automate supports content ingestion. Metadata filtering and ranking retrieve relevant content without a recurring vector-search and embedding layer. Automated ingestion removes duplicates, and the firm can update templates without redeploying the application.
BCT delivered the platform iteratively with the customer’s marketing team. Multiple user acceptance testing rounds helped tune the workflow, content handling and document output to the firm’s requirements. Source validation and document assembly were developed as parts of the same production process.
The generation process uses the prospective client’s details and the firm’s approved content library. Retrieval-augmented generation supplies relevant source material, while the AI is required to cite every factual claim. Automated citation validation checks those claims against their sources, creating a control intended to prevent unsupported statements from entering client documents.
The document engine handles the formatting work that previously followed drafting. OOXML assembly produces a personalised cover, automatic table of contents, correct page numbering, branded footer and logo, and the firm’s prescribed styles. The output is designed to be ready for use without manual reformatting.
The architecture combines Azure OpenAI with Python-based Azure Functions, Azure Cosmos DB and Azure Blob Storage. Power Apps provides the proposal wizard, while Power Automate supports content ingestion. Metadata filtering and ranking retrieve relevant content without a recurring vector-search and embedding layer. Automated ingestion removes duplicates, and the firm can update templates without redeploying the application.
BCT delivered the platform iteratively with the customer’s marketing team. Multiple user acceptance testing rounds helped tune the workflow, content handling and document output to the firm’s requirements. Source validation and document assembly were developed as parts of the same production process.
Impact
Estimated proposal turnaround fell from approximately six hours to under 30 minutes, described as roughly 90% faster. The associated capacity benefit was estimated at approximately 65 hours per month, or more than 750 hours annually, of marketing and partner time available for client-facing work. These time-saving estimates remain subject to final client sign-off.
The document engine standardises proposals against the firm’s templates by construction. Covers, page numbering, footers and styles are assembled automatically, removing the separate formatting step that had contributed to inconsistent output and rework.
Factual traceability is also built into the workflow. Generated claims are subject to source citation and automated validation, giving the firm a structured way to control AI-generated content in accuracy-critical proposals.
The metadata-driven retrieval design avoids recurring vector-search and embedding spend, while automated ingestion and editable templates simplify ongoing updates. The result is a proposal process that connects drafting, source checking and branded document production, reducing manual assembly while keeping the firm’s content and presentation standards central.
The document engine standardises proposals against the firm’s templates by construction. Covers, page numbering, footers and styles are assembled automatically, removing the separate formatting step that had contributed to inconsistent output and rework.
Factual traceability is also built into the workflow. Generated claims are subject to source citation and automated validation, giving the firm a structured way to control AI-generated content in accuracy-critical proposals.
The metadata-driven retrieval design avoids recurring vector-search and embedding spend, while automated ingestion and editable templates simplify ongoing updates. The result is a proposal process that connects drafting, source checking and branded document production, reducing manual assembly while keeping the firm’s content and presentation standards central.
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Bahwan CyberTek is committed to providing the highest level of protection regarding the processing of their vendors’/clients’/customers’/job applicants’/website visitors’ personal data based on applicable data protection laws and regulations at respective locations. In general, you can visit the Bahwan CyberTek website without providing personal data. However, there are times when Bahwan CyberTek will request personal data.
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