From a five-day RFP to a 90-minute review

Most presales teams already hold the answers. The material is simply scattered: a security whitepaper in one folder, a proposal from last year's deal in another, approved FAQs on a wiki page nobody maintains. SiftHub works from all of it.

Connect those sources and SiftHub indexes them, tracks which answers your team has signed off and keeps the supporting context next to each claim. Nothing needs reformatting, and nothing moves into a new system. Existing material becomes searchable where it already sits.

Supported source types:

• Security whitepapers and compliance documents (PDF, DOCX)
• RFPs and security questionnaires your team has already completed
• Product documentation and feature specifications
• Confluence and SharePoint pages via integration
• Google Drive files and Notion pages
• Approved answer sheets in Excel or CSV

Each source carries a trust level, which lets the system tell a confirmed answer from one that still needs a reviewer's sign-off.

An RFP question usually means more than the words it uses. "Do you support role-based access control?" might be asking about the access architecture, about governance maturity, or simply whether an IT team exists. A keyword search on "role-based" will miss half the relevant material.

SiftHub classifies intent before it searches. It sorts the question into a topic such as access control, encryption at rest, incident response or data retention, then retrieves passages that address that topic instead of chasing the exact phrasing. Candidates are ranked by relevance and given a numeric confidence score.

A strong match returns a single recommended answer. When two passages each cover part of the question, both appear with their scores so a reviewer can choose. With no supporting material at all, SiftHub marks the item unanswered and routes it to the right SME rather than writing something plausible.

The score decides where a reviewer looks first. It is a routing signal, not a substitute for approval.

The drafted answer covers 80-90% of the writing, never the final call. In the shared workspace each question sits beside its draft, so reviewers see the original ask and the proposed reply in the same view.

Editors change wording in place without losing sight of the question. Anything that scores poorly goes into the SME queue, where the named owner receives the original text, the relevant knowledge base passages and a due date. Comment threads keep the debate attached to the answer it concerns.

An approval step stops unreviewed answers from leaving the building. After the AI pass, most teams get through a 100-question questionnaire in under 90 minutes, where the manual version ran two or three days of back-and-forth.

Approved responses feed back into the knowledge base. The next similar question arrives with a higher score and less work attached.

Buyers rarely adopt your format. What arrives is an Excel sheet with unusual columns, a PDF form or a Word file whose instructions sit in the margins. SiftHub writes the finished answers straight into that original structure.

Export choices:

• Excel, with cells mapped to the buyer's column layout
• PDF, formatted for procurement portals or email
• Word, for narrative questionnaires that still need editing

The output travels into the tools the team already uses. Salesforce opportunity notes update when a questionnaire goes in, Slack notifies presales that a response is ready for final review, and approved answers sync back to Confluence or SharePoint so the source material stays current without anyone tidying it by hand.

The buyer gets their own file back, filled in. Nobody spends the last hour of a deal fighting with column widths.