--- title: "AI-powered DDQs" url: "https://www.arphie.ai/glossary/ai-powered-ddqs" collection: glossary lastUpdated: 2026-08-10T17:00:10.358Z --- # AI-powered DDQs [Due diligence questionnaires (DDQs)](https://www.arphie.ai/glossary/due-diligence-questionnaire) concentrate sales, security, compliance, product, and financial knowledge into one high-stakes response. Their repeated questions make them a strong fit for AI, while each answer still needs evidence and an accountable reviewer. A well-designed workflow gives you a source-backed first draft, routes uncertainty to the right subject matter expert (SME), and preserves final sign-off. Here is how to build that workflow from intake through submission. ## What AI DDQ Automation Actually Does AI DDQ automation parses an incoming questionnaire, retrieves relevant company knowledge, drafts responses, and coordinates review. It turns a research-and-copying project into a review workflow, shortening turnaround while giving response teams more time for the opportunity or investor relationship behind the request. People still own the facts, commitments, exceptions, and final submission. We built Arphie, our [DDQ automation platform](https://www.arphie.ai/platform), for this response-side workflow. Our AI agents use approved Q&A content and connected company sources to draft answers, show the supporting sources and confidence signals, and help reviewers collaborate before export. This applies when sales and security teams answer customer due diligence, and when investor relations teams answer questionnaires from limited partners and other investors. | Stage | Manual DDQ workflow | AI-assisted DDQ workflow | | --- | --- | --- | | Intake | Copy questions into a tracker and fix formatting. | Detect questions, sections, and response fields. | | Research | Search old DDQs, shared drives, and messages. | Retrieve relevant content from approved sources. | | Drafting | Copy, paste, reconcile, and rewrite. | Generate a source-backed first draft in the requested style. | | Review | Send broad requests to multiple SMEs. | Route exceptions and uncertain answers to the right owner. | | Delivery | Merge versions and rebuild the source file. | Complete sign-off and export in the required format. | Automation quality depends on the controls around the AI. The workflow needs an authority hierarchy for sources, instructions that define acceptable output, risk-based review, and a feedback loop for approved knowledge. ![Source-grounded AI DDQ workflow from trusted knowledge to human-approved submission.](https://cdn.prod.website-files.com/672fc2345132970736914b73/6a79deb688443ae340d54716_5d871f6b-a636-46b0-97cc-ab7698c71446.png) ## A 7-Step Workflow to Automate DDQs With AI ### 1. Map the DDQ, Owners, and Approval Path Start with the business context. A customer assurance DDQ usually involves sales or solutions engineering, security, privacy, product, and legal. An investor DDQ usually involves investor relations, compliance, finance, operations, and investment teams. The exact route depends on the questions and the representations your company is being asked to make. Assign one response owner, one final approver, and a named owner for each knowledge domain. Record the deadline, submission format, confidentiality level, and any requester instructions. This stops a fast draft from entering the wrong approval path. ### 2. Build a Source Hierarchy A folder full of old DDQs is a weak knowledge base. Previous submissions are useful precedents, but an earlier answer may describe an old control, product capability, fund figure, or policy. Give the AI a clear order of authority. | Priority | Source type | How the AI should use it | | --- | --- | --- | | 1 | Approved Q&A language | Reuse exact or near-exact wording when the context still applies. | | 2 | Current systems of record | Synthesize an answer and cite the supporting policy, product document, report, or data source. | | 3 | Recently approved submissions | Use as precedent for scope, tone, and likely evidence. | | 4 | Expired, conflicting, or unapproved material | Exclude from drafting and route the question to an owner. | Give each source an owner, approval status, effective date, and review date. Keep security policies, legal positions, product documentation, financial reporting, and investment information separate when access or approval rules differ. ### 3. Connect Sources and Set Drafting Rules Connect the source systems your reviewers already trust. We connect Arphie to systems such as Google Drive, SharePoint, Confluence, Notion, Seismic, Highspot, Vanta, and approved web pages through [live integrations](https://www.arphie.ai/integrations). You control which sites, folders, and files the AI can use. Then encode the rules that people otherwise apply from memory: - Allowed sources and their order of authority. - Required voice, answer length, terminology, and formatting. - Customer, fund, product, geography, and time-period context. - Questions that always require security, legal, compliance, or finance review. - Conditions that require an explicit gap instead of a generated claim. Apply least-privilege access to both sources and projects. Sensitive DDQ content should stay inside an approved enterprise environment with clear retention, model-training, encryption, access-control, and audit terms. Our [security controls](https://www.arphie.ai/security) include role-based access, encryption in transit and at rest, SOC 2 Type 2 compliance, and zero-data-retention agreements with model providers. ### 4. Import and Reconcile the Questionnaire Importing is more than extracting sentences. DDQs can contain hidden instructions, parent questions, conditional subparts, dropdown responses, character limits, and formulas across several spreadsheet tabs. Preserve that structure before generating answers. When you import a Word or Excel DDQ into Arphie, we detect questions and sections and keep the work in one project. Reconcile the imported question count with the source file, retain required yes-or-no fields, and keep explanatory text attached to the question it qualifies. We then export the completed response to the original Word or Excel format. ### 5. Generate Evidence-First Drafts Use one safe drafting rule: **no source, no factual assertion**. A fluent answer without supporting evidence creates more review work because the reviewer must research every sentence again. Consider this incoming question: “Describe how customer data is encrypted in transit and at rest, including key management.” The drafting workflow should: - Retrieve the current information security policy, encryption standard, and relevant architecture documentation. - Compose only the statements supported by those sources. - Attach the source and relevant passage to the draft. - Leave any undocumented part unresolved for the security owner. - Follow the requester’s length and format requirements. This control matters because generative AI can produce confident false content. The NIST [Generative AI Profile](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) recommends comparing output with known ground truth, fact-checking generated information, and reviewing sources and citations. In a DDQ, source visibility gives the reviewer a short path from draft to evidence. ### 6. Route Review by Confidence and Risk Confidence signals help prioritize review. They do not approve an answer. A strong source match can still be wrong for a specific product, customer, fund, date, or jurisdiction. | Evidence and risk state | Workflow action | Accountable reviewer | | --- | --- | --- | | Current approved answer, low-risk context | Review fit and wording. | Response owner. | | Current sources require synthesis | Review factual scope and omissions. | Domain SME. | | Security exception, legal commitment, financial figure, or other high-risk representation | Require specialist review and final sign-off regardless of confidence. | Domain owner and final approver. | | Missing, conflicting, or expired evidence | Leave unresolved and create an assignment for a fresh answer. | Source owner. | Avoid a universal threshold such as “90% confidence means auto-approve.” Confidence is a queueing input. Evidence quality and business risk determine the approval path. ### 7. Finalize, Export, and Improve the Knowledge Base Run a consistency pass across repeated questions, dates, defined terms, product names, and yes-or-no answers with explanations. Confirm every required reviewer has signed off, then export the response in the requester’s original format. After submission, promote approved net-new answers into the Q&A library. Add the owner, supporting source, scope, and next review date. Record corrections and late SME input as knowledge updates. This is how each completed DDQ reduces research on the next one without turning old submissions into unquestioned truth. ## What to Automate and What to Keep Human-Owned A useful boundary is simple: AI handles repeated transformations and evidence retrieval. People own representations and decisions. | Activity | AI role | Human responsibility | | --- | --- | --- | | Parse the file | Extract and organize questions. | Confirm structure and requester instructions. | | Find knowledge | Rank relevant approved sources. | Decide which source is authoritative and current. | | Draft answers | Compose responses from evidence. | Approve facts, scope, and exceptions. | | Tailor responses | Apply requested tone, length, and context. | Approve customer-specific or investor-specific commitments. | | Coordinate work | Route questions, notify owners, and track status. | Resolve gaps and make tradeoffs. | | Prepare submission | Check completeness and restore the required format. | Give final sign-off and submit. | This boundary also improves adoption. SMEs review the few questions that need their judgment instead of re-answering the full questionnaire. Sales engineers, proposal teams, and investor relations teams can spend more time on the opportunity or relationship behind the DDQ. ## A 30-Day DDQ Automation Rollout ### Week 1: Establish the Baseline Choose one representative completed DDQ and one recent difficult DDQ. Capture total turnaround time, active drafting time, SME wait time, number of contributors, and post-submission corrections. Inventory the sources used for the final answers and identify conflicts or missing ownership. ### Week 2: Configure Knowledge and Governance Connect the highest-authority sources first. Add source owners, permissions, drafting instructions, review rules, and answer-expiry dates. Use a previously completed DDQ as a shadow run so the team can compare the AI drafts with the approved submission without delivery risk. ### Week 3: Run a Controlled Live Pilot Select a live DDQ with normal complexity and a real deadline. Keep full human review in place. Track which drafts are accepted, materially rewritten, rejected, or escalated, along with the reason for each outcome. This produces better information than a simple auto-fill percentage. ### Week 4: Fix the Workflow and Expand Resolve the recurring causes of rework. Typical fixes include replacing stale sources, separating content by product or geography, tightening instructions, and changing reviewer assignments. Expand to another DDQ type only after the first workflow has a stable owner and review path. Track these operating metrics over time: - Time from import to first draft. - Active reviewer time per DDQ. - First-pass acceptance rate, measured as drafts accepted without a material factual rewrite. - Source coverage, measured as drafted answers with current supporting evidence. - Questions escalated because evidence was missing or conflicting. - SME response time and final submission corrections. For a real benchmark, compare answer quality as well as speed. After BillingPlatform switched its request for proposal (RFP) workflow to Arphie, the share of AI-generated answers used without rework rose from roughly half to more than 90% on most RFPs. The team also shifted more process ownership to account executives while keeping solutions engineers available for harder questions, as detailed in the [BillingPlatform case study](https://www.arphie.ai/case-studies/billingplatform). ## Common DDQ Automation Failure Modes - **Treating old DDQs as truth.** A previous answer is evidence of what was said, not proof that it remains accurate. Current source documents should win when content conflicts. - **Optimizing only for speed.** A fast draft with poor source coverage transfers work to reviewers and increases correction risk. Track acceptance, evidence, and corrections alongside cycle time. - **Using confidence as approval.** Confidence signals help route work. High-risk claims still need the accountable specialist and final approver. - **Connecting too much content.** Broad access can introduce duplicates, expired material, and information a project should never expose. Curated permissions improve security and retrieval quality. - **Piloting only easy questionnaires.** A short, repetitive DDQ hides parsing, routing, and evidence gaps. Include a representative difficult example before expanding the workflow.