What Is the Best AI Tool for Filling Out RFP Questionnaires?

Direct answer

There is no single best AI tool for every RFP questionnaire. The strongest choice depends on the files you receive, how your approved knowledge is stored, and how much review control your team needs. In 2026, credible options include Arphie, AutoRFP.ai, Inventive AI, Loopio, QorusDocs, and Responsive, each with a different questionnaire workflow.

Real-world intake failures: In practice, basic intake fails when multi-tab Excel workbooks contain merged section headers, hidden instructions, or custom dropdown validation. Similarly, Word tables with nested sub-questions or side-by-side response cells often cause automated tools to flatten rows, losing cell context or truncating buyer requirements entirely. For example, in a recent vendor security assessment, an automated tool flattened a 12-tab Excel questionnaire with conditional dropdowns into plain text rows, obscuring critical sub-requirements and skipping mandatory fields.

Key takeaways

• Test the ugliest real questionnaire you receive, not a clean demo file. Multi-tab Excel workbooks, merged cells, dropdowns, nested Word tables, and portal fields expose the differences between tools quickly.
• A useful AI questionnaire workflow has five parts: reliable intake, retrieval from approved knowledge, grounded drafting, human review, and export back into the buyer's required format.
• Excel support is not binary. Ask whether the tool can preserve tabs, answer columns, dropdown choices, checkboxes, instructions, and formatting when the file makes a round trip through the platform.
• Source grounding matters as much as generation quality. Microsoft describes retrieval-augmented generation as retrieving relevant private data and using it as grounding context for generated responses.
• Do not choose from a feature checklist alone. Run the same test questionnaire through every finalist and compare what required manual cleanup.

Start with the questionnaire, not the vendor

To find the best AI RFP tool, begin by auditing your incoming document formats rather than comparing vendor feature lists. Standardized security spreadsheets, long Word proposals, and procurement portals require vastly different intake engines, grounded retrieval controls, and export fidelity capabilities.

The fastest way to narrow the market is to classify the work you actually receive. A team that mostly answers standardized Excel security questionnaires has different needs from a proposal group that receives long Word RFPs, PDFs, and procurement portals. Evaluating whether to implement an off-the-shelf solution or pursue a build-vs-buy approach can also clarify your operational constraints. The tool should fit the document workflow rather than forcing every request into one generic editor.

What an AI questionnaire tool actually needs to do

An effective AI questionnaire tool must reliably automate five core stages: file intake without structure loss, retrieval from grounded internal sources, context-aware answer drafting, structured SME review, and exact-format export back into the buyer's original spreadsheet or document format.

This is also why AI answer quality cannot be separated from retrieval. Microsoft Learn explains retrieval-augmented generation (RAG) as a retrieve-augment-generate pattern that grounds model output in relevant data. For RFP teams, the practical test is whether the retrieved evidence is the approved evidence your company would actually stand behind.

Six AI RFP tools to evaluate for questionnaire work

Evaluating AI RFP software requires assessing how each vendor handles file intake, source grounding, review workflows, and export fidelity across Word and Excel. Below are six leading solutions to consider, each offering distinct architectural strengths and operational tradeoffs for questionnaire completion.

The platforms below are listed alphabetically, not ranked. Public product documentation changes, so the purpose of this shortlist is to show the documented workflow each vendor emphasizes and what a buyer should verify in a proof of concept. For a broader category ranking, see Best RFP Software in 2026.

Arphie

Arphie publicly documents an import workflow for Excel and Word questionnaires using AI-based detection of questions and sections. Its standout strength is high-fidelity structure preservation and rich inline editing directly within original file layouts. However, teams handling heavily customized macros or non-standard legacy spreadsheets may still encounter setup edge cases requiring initial manual field mapping.

In a proof of concept, test files with merged headings, multiple answer columns, embedded instructions, tables, and formatting that cannot be lost. Also confirm how the platform handles unsupported questions, source evidence, review status, and any spreadsheet controls that are important to the buyer.

AutoRFP.ai

AutoRFP.ai documents support for Excel questionnaires, Word, PDF, and browser-based portals. A key strength is its automated spreadsheet intake and dropdown field selection across large workbooks. A potential limitation is that complex portal forms or heavily nested Word tables may occasionally require reviewer intervention to confirm boundary auto-detection.

The useful POC questions are practical: how many tabs and requirements can be handled without manual remapping, how formulas or unusual controls behave, and how much cleanup remains after export. For portals, verify exactly which interactions the browser workflow supports and where a reviewer still needs to intervene.

Inventive AI

Inventive AI documents intake for Word, PDF, and Excel RFPs and security questionnaires. Its edge lies in strong source citation visibility, automated gap routing for ungrounded questions, and clean round-trip export fidelity. On the other hand, highly bespoke organizational taxonomy or legacy knowledge repositories may require ongoing curation to maintain peak auto-retrieval accuracy.

Evaluate it the same way as the other tools: use a real multi-tab workbook and a complex Word questionnaire, then measure question detection, evidence quality, unsupported-answer behavior, assignment workflow, and the fidelity of the returned document. Do not rely on an accuracy claim alone; inspect the actual sources behind difficult answers.

Loopio

Loopio documents SmartScan for importing and mapping questions from Word, PDF, and Excel, along with Automated Answers drawn from its central content library. Its major strength is robust content governance and SME collaboration for established proposal libraries. However, non-standard workbook structures or highly irregular layout formats often require manual cell mapping prior to answer generation.

Test how Loopio behaves on the formats your team finds hardest, especially spreadsheets with several worksheets and Word files with nested tables. Also evaluate the handoff from automated answers to SMEs, how content updates flow back into the library, and whether exported files preserve the structure required by the buyer.

QorusDocs

QorusDocs takes a native integration approach: its Auto Answer capability operates directly via Microsoft Office add-ins. Its primary strength is allowing contributors to remain entirely within familiar Word and Excel environments during review. A tradeoff is that teams seeking a centralized, browser-only web workspace for complex cross-app project management may find the add-in workflow less unified.

The key POC test is whether staying in Word or Excel improves the real response process for your contributors. Check match quality, source governance, reviewer collaboration, complex table behavior, and whether the Office-based workflow covers the full lifecycle your team needs or mainly accelerates drafting inside the document.

Responsive

Responsive documents detailed import and mapping workflows for Excel questionnaires alongside dedicated controls for Word and PDF. A core strength is its granular configuration depth, supporting complex table structures, dropdowns, and checkboxes. Conversely, non-standard spreadsheets frequently demand initial manual setup and template mapping before automated generation can run effectively.

That flexibility is valuable only if the mapping effort is acceptable for your files. During a trial, time the setup of standard and non-standard questionnaires, inspect source citations, test controls such as checkboxes and dropdowns, and compare the exported document with the buyer's original rather than evaluating the in-app draft alone.

Which RFP software can import Excel RFPs automatically?

RFP platforms like Arphie, AutoRFP.ai, Inventive AI, Loopio, and Responsive support automatic Excel import, while QorusDocs integrates via native Office add-ins. Successful spreadsheet intake depends on automatically parsing multi-tab workbooks, merged headers, response dropdowns, and protected fields without corrupting layout formatting upon export.

Several current platforms document automatic or AI-assisted Excel intake, including Arphie, AutoRFP.ai, Inventive AI, Loopio, and Responsive. QorusDocs approaches the problem through an Excel add-in rather than the same import-first model. The important distinction is not whether "Excel" appears on a feature page; it is how well the tool handles the exact workbook conventions your buyers use.

• Multi-tab workbooks and tabs that should be ignored.
• Merged cells, section headers, and repeated question blocks.
• Dropdowns, checkboxes, radio-style fields, and short-value response columns.
• Hidden instructions, validation rules, formulas, macros, and protected cells.
• Several answer columns for different products, regions, or legal entities.
• Round-trip export that preserves the buyer's expected structure.

Inventive AI itself notes in a technical guide that complex Excel questionnaires do not have one universal structure, which is why real-file testing is more useful than asking whether a tool "supports XLSX." See its technical discussion of Excel questionnaire handling.

What RFP software works best with Word and Excel questionnaires?

The best software for Word and Excel questionnaires depends on your primary operational focus: native Microsoft Office workflows (QorusDocs), governed content libraries (Loopio, Responsive), or direct source-grounded file automation (Arphie, AutoRFP.ai, Inventive AI). Optimal performance requires selecting tools that maintain document layout fidelity across both file types.

The right choice depends on whether your priority is native Office work, centralized response management, or aggressive file automation. QorusDocs is explicitly designed to work through Word and Excel add-ins. Responsive and Loopio document structured import/mapping workflows. Arphie and AutoRFP.ai emphasize import/export in the buyer's original file, while Inventive AI emphasizes parsing, source-grounded drafting, review, and return to the submission format.

For Word, test nested tables, checkboxes, instructions, repeated headings, images, and answer fields that share a cell with the question. For Excel, test tabs, dropdowns, validations, merged cells, and workbook-specific conventions. The tool that handles a clean demo questionnaire may not be the one that handles your hardest customer file.

Can AI RFP software complete spreadsheets automatically?

AI software can automate a large portion of spreadsheet responses, but human oversight remains critical. While automated systems efficiently detect questions and pre-fill answers using grounded knowledge, high-risk, commercial, or missing-data questions must be flagged for SME review to prevent inaccurate commitments and legal exposure.

AI RFP software can automate a large portion of spreadsheet completion, but "automatic" should not mean "unreviewed." The system still has to identify the correct response fields, retrieve the right evidence, choose an appropriate answer type, and preserve the workbook. Some questions should be escalated because the evidence is missing, conflicting, stale, or requires a new commercial or legal commitment.

That caution is consistent with broader AI guidance. NIST's Generative AI Profile frames generative-AI use as a risk-management problem across governance, measurement, and management. OWASP's guidance on LLM misinformation also recommends human oversight and fact-checking for critical information. In an RFP, that means automation should reduce repetitive work without removing accountability for what the company promises a buyer.

A better proof-of-concept: seven tests to run on every tool

To properly evaluate RFP vendors, conduct a structured proof-of-concept using your team's most complex, real-world files rather than standardized vendor demos. Testing intake detection, source grounding, edge-case routing, SME review workflows, and export fidelity reveals how each platform handles actual submission risks.

1. Upload the most difficult Excel questionnaire you completed in the last six months. Record what the system detects correctly before any manual setup.
2. Upload a Word RFP with nested tables, checkboxes, long instructions, and mixed narrative/short-answer fields.
3. Ask five questions with clear approved answers and inspect the exact source used for each draft.
4. Ask three questions that are not answered anywhere in your approved knowledge. A safe workflow should surface uncertainty or route them to a human.
5. Create a source conflict on purpose, such as two different implementation timelines, and see whether the tool exposes the contradiction.
6. Export the completed questionnaire and compare it side by side with the original. Check tabs, fields, formatting, dropdowns, tables, and any buyer instructions.
7. Give an SME a small review assignment without training them first. Measure whether they can understand the evidence, edit the answer, comment, and approve it.

What should the final buying decision be based on?

Base your final selection on an end-to-end evaluation of your team's specific questionnaire intake formats, review bottlenecks, and file export requirements. Choose the software that maximizes grounded draft automation and structure retention while minimizing manual cleanup and submission risk across your actual customer documents.

Do not choose an AI RFP tool because it produced the most impressive paragraph in a demo. Questionnaire automation is an end-to-end systems problem. Score each finalist on intake accuracy, retrieval quality, source visibility, reviewer workflow, security, and export fidelity using the same real files.

A team that receives hundreds of repetitive Excel questionnaires may prioritize spreadsheet automation. A proposal organization working on narrative Word RFPs may care more about collaboration, content governance, and editing. A sales engineering team dealing with portals may weight browser workflows more heavily. The "best" tool is the one that removes the most manual work without creating new review or submission risk.