Questionnaire Review
Map a real buyer questionnaire into a controlled review workflow.
MODULES
Start with one guided diligence workflow, then enable the modules that match how your buyers ask for proof — questionnaires, evidence, claims, AI diligence, renewals, owner routing, integrations, and enterprise access.
Shared graph
Claims, evidence, owners, freshness, and disclosure rules.
Guided Pilot Stack
Most teams begin with a guided workflow around one real questionnaire, one evidence bundle, and one proof-pack request. As the workflow becomes clear, ProofRelay modules can be configured around the same claim/evidence graph.
Map a real buyer questionnaire into a controlled review workflow.
Identify recurring answers and turn them into reusable claim candidates.
Attach the policies, reports, attestations, and diagrams that support each claim.
Prepare a buyer-specific packet with missing evidence and disclosure checks visible.
Keep buyer-facing output gated by approval, freshness, and disclosure rules.
Module Library
Every module works around the same claim/evidence graph. Use the category filters to scan the platform surface without treating the modules as separate products.
Modules
32
Categories
6
Guided starting stack
5
The operating layer for questionnaires, claims, evidence, approvals, proof packs, and release safety.
6 modules
Core TrustOps
Ingest buyer questionnaires, map recurring questions, and review cited suggestions before anything becomes buyer-facing.
Problem it solves
Security questionnaires arrive in different formats, but many questions repeat. This module turns them into a structured review workflow instead of copy-paste chaos.
Best for
Teams responding to enterprise security questionnaires, DDQs, RFPs, and AI diligence forms.
Core TrustOps
Turn repeated answers into reusable claim candidates with owners, approval status, freshness, disclosure state, and version history.
Problem it solves
The same answer appears in many buyer forms. The claim graph makes the underlying truth reusable without treating prior answers as proof.
Best for
Teams needing consistency across buyers and renewals.
Core TrustOps
Organize policies, reports, attestations, pentests, COIs, subprocessors, architecture docs, and proof artifacts.
Problem it solves
Evidence lives across drives, GRC tools, Slack, email, and owners. Evidence Vault gives proof a governed home.
Best for
Teams constantly hunting for supporting documents.
Core TrustOps
Build buyer-specific evidence packets for follow-up requests, renewals, audits, and AI diligence.
Problem it solves
After a questionnaire, buyers ask for proof. Proof packs turn the ask into a reusable packet with missing evidence, disclosure, freshness, and sharing state.
Best for
Teams that send policies, pentest reports, SOC 2 status, COIs, subprocessors, and architecture proof to buyers.
Core TrustOps
Route claim reviews and evidence gaps to security, product, privacy/legal, finance, and operations owners.
Problem it solves
Founders and CTOs get pulled into every review because ownership is unclear.
Best for
Cross-functional assurance workflows.
Core TrustOps
Gate buyer-facing output by claim approval, evidence, freshness, disclosure rules, and audit history.
Problem it solves
AI and copy-paste can produce risky answers. Safety controls prevent stale, unsupported, or over-shared output.
Best for
Security-conscious vendors.
Modules that turn scattered documents, old answers, chats, and follow-ups into reviewable proof work.
7 modules
Evidence Intelligence
Convert buyer follow-up requests into missing-evidence categories, owners, and proof-pack blockers.
Problem it solves
Teams do not know what proof is missing until the buyer asks again.
Best for
Proof-heavy follow-ups and enterprise diligence.
Evidence Intelligence
Extract reusable claim candidates from old questionnaires, RFPs, security emails, and review history.
Problem it solves
Prior answers are valuable, but they are not governed truth. This module turns history into draft claims and review tasks.
Best for
Teams with spreadsheets, PDFs, and old responses scattered across drives.
Evidence Intelligence
Surface potential claims and evidence leads from approved Slack or Teams channels, without treating chat as truth.
Problem it solves
Security and product truth often lives in employee discussion threads, but nobody converts it into reusable assurance content.
Best for
Startups where decisions happen in chat before docs catch up.
Evidence Intelligence
Turn buyer follow-up emails and internal diligence threads into proof requests, evidence gaps, and owner tasks.
Problem it solves
The highest-signal proof requests often arrive in email after the questionnaire is submitted.
Best for
Teams managing enterprise reviews through email threads.
Evidence Intelligence
Track expiring reports, stale policies, outdated diagrams, and evidence that needs re-review.
Problem it solves
A claim may be true when answered and stale by the time the buyer asks again.
Best for
Pentests, SOC 2 reports, COIs, policies, subprocessors, and AI posture.
Evidence Intelligence
Classify artifacts by public, NDA-only, restricted, or internal-only sharing rules.
Problem it solves
Not every proof artifact can be sent to every buyer.
Best for
Teams sharing pentests, architecture diagrams, raw reports, or sensitive control evidence.
Evidence Intelligence
Create admin-controlled redaction rules and templates for emails, IPs, identifiers, secrets, and restricted fields.
Problem it solves
Evidence often contains details that should not be shared externally.
Best for
Security teams sharing screenshots, reports, logs, diagrams, or raw artifacts.
AI diligence modules for model providers, data boundaries, training posture, risk context, and AI proof packs.
5 modules
AI & Governance
Manage AI asset profiles, model/provider records, training posture, AI risk register links, and AI diligence packs.
Problem it solves
Buyers increasingly ask whether customer data trains models, which providers are used, how data is retained, and how AI risk is governed.
Best for
AI vendors and AI-enabled SaaS.
AI & Governance
Track model providers, regions, data sent, training posture, retention, and review ownership.
Problem it solves
AI diligence questions often require provider-specific answers and evidence.
Best for
Companies using OpenAI, Anthropic, Azure OpenAI, custom models, or AI infrastructure vendors.
AI & Governance
Map what customer data enters which system, model, vendor, region, and retention path.
Problem it solves
Buyers ask where their data goes and whether it is used for training or retained.
Best for
AI/data infrastructure, security, and privacy-heavy vendors.
AI & Governance
Connect AI claims to risk register items, mitigations, owners, review dates, and evidence.
Problem it solves
AI governance claims are hard to prove unless linked to actual risk-management records.
Best for
Regulated or enterprise AI vendors.
AI & Governance
Create buyer-ready AI assurance sections covering model usage, data training, retention, human access, risk, and incident posture.
Problem it solves
AI sections in security reviews are becoming their own diligence track.
Best for
Teams repeatedly answering AI/data-handling questionnaires.
Workflows for renewals, re-sends, buyer memory, change impact, and long-running obligations.
5 modules
Lifecycle
Generate 'what changed since last review?' packs for renewals, annual reassessments, and repeat buyer requests.
Problem it solves
Renewals ask for the same proof plus what changed.
Best for
Annual security reviews and customer reassessments.
Lifecycle
Reuse previously reviewed proof packages when buyers ask for the same evidence again.
Problem it solves
Buyers often ask for everything to be re-sent. This tracks versions, freshness, and shareability.
Best for
Repeated follow-up requests from the same buyer or procurement team.
Lifecycle
Track customer-specific security commitments, contractual obligations, and renewal-sensitive promises.
Problem it solves
Teams make commitments during sales and contracting, but those promises are hard to track later.
Best for
Mature customer trust, legal, and security teams.
Lifecycle
Show which claims, evidence, proof packs, and buyers are affected when architecture, policy, model, or vendor posture changes.
Problem it solves
Teams do not know which external answers become stale after internal changes.
Best for
Fast-moving product and AI teams.
Lifecycle
Remember what was sent to each buyer, which versions were used, what was restricted, and what was asked again later.
Problem it solves
Each buyer relationship develops its own assurance history, but teams lose track after the deal cycle.
Best for
Renewals, expansions, and strategic enterprise accounts.
Controlled intake and roadmap integrations for evidence sources, deal context, tickets, docs, and buyer requests.
5 modules
Integrations & Intake
Give buyers a controlled intake path for evidence requests instead of unmanaged email threads.
Problem it solves
Trust centers do not eliminate custom follow-up requests.
Best for
Teams that already publish a trust center but still handle custom proof requests manually.
Integrations & Intake
Connect ProofRelay with Vanta, Drata, Sprinto, Jira, GitHub, Google Drive, Notion, Slack, HubSpot, and Salesforce.
Problem it solves
Assurance evidence and deal context live across many systems.
Best for
Teams ready to connect GRC, collaboration, source-control, storage, and CRM systems.
Integrations & Intake
Attach buyer, deal stage, ARR, region, and account context to questionnaires and proof-pack decisions.
Problem it solves
Security review work should map to revenue impact and buyer context without becoming a full CRM.
Best for
Sales-led teams prioritizing diligence by deal urgency.
Integrations & Intake
Pull evidence from Jira, Linear, GitHub issues, or project systems when remediation or control work is completed.
Problem it solves
Security claims often depend on engineering work that lives in ticketing systems.
Best for
Teams using tickets as operational evidence.
Integrations & Intake
Import or reference policies, diagrams, reports, and evidence from shared drives or document repositories.
Problem it solves
Most proof artifacts begin in Google Drive, Notion, Confluence, or similar systems.
Best for
Teams that need fast evidence onboarding without a full GRC integration.
Administration, reporting, portal assistance, framework mapping, and identity controls for mature trust teams.
4 modules
Enterprise Controls
Map claims and evidence to SOC 2, ISO 27001, NIST CSF, NIST AI RMF, and internal control frameworks.
Problem it solves
Buyers ask the same proof through different framework languages.
Best for
Teams reusing evidence across security, privacy, and AI frameworks.
Enterprise Controls
Browser-assisted support for completing third-party buyer portals from approved claims and evidence.
Problem it solves
Some buyers force vendors into proprietary portals.
Best for
High-volume teams dealing with fragmented buyer portals.
Enterprise Controls
Track bottlenecks, question frequency, stale-claim trends, answer reuse, review cycle time, and proof-pack workload.
Problem it solves
Leaders need to know where diligence slows deals down.
Best for
Customer trust, security, and revenue leaders.
Enterprise Controls
Enterprise identity, provisioning, role governance, and centralized access management.
Problem it solves
Production enterprise deployments need IdP-managed access and lifecycle controls.
Best for
Larger customer trust teams and production enterprise deployments.
Workflow Combinations
Pick the workflow that matches where diligence is breaking today. The surrounding modules can be enabled while keeping every workflow tied to the same claim/evidence graph.
Enterprise buyers keep asking the same questions in different formats and then asking for proof.
AI vendors need precise answers about model usage, training posture, retention, and data boundaries.
Security companies need technical assurance claims to stay current, owner-reviewed, and safe to share.
Mature trust teams need governance, reporting, identity, and obligations without losing the proof graph.
The hardest work often starts after the questionnaire, when buyers ask for evidence again.
Important assurance context lives in chat and email before it becomes reviewable proof.
Strategic Module Details
The highest-leverage modules are expanded below so teams can see how the workflow fits together without expanding every catalog item into a long page.
Problem
Buyer questionnaires arrive in different formats, but the underlying assurance questions repeat.
What it does
Converts incoming spreadsheets, forms, and DDQs into a governed review queue where AI-assisted suggestions stay tied to approved claims and evidence.
Where it fits
Fits at the front of security reviews, DDQs, RFPs, renewal questionnaires, and AI diligence forms.
Proof workflow
Buyer question
Checked against the shared graph
Claim match
Checked against the shared graph
Cited suggestion
Checked against the shared graph
Human review
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
The same answer appears in many buyer forms, but prior answers are not proof by themselves.
What it does
Creates the reusable truth layer behind questionnaire answers, proof packs, renewals, and AI diligence responses.
Where it fits
Fits wherever the same security, privacy, AI, or operational assurance claim appears across many buyers.
Proof workflow
Claim candidate
Checked against the shared graph
Owner review
Checked against the shared graph
Evidence-backed
Checked against the shared graph
Reusable proof
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
Proof artifacts live across drives, email, GRC tools, ticketing systems, and individual owners.
What it does
Gives proof artifacts a governed home that records what each file supports, who owns it, and whether it is fresh enough to share.
Where it fits
Fits once evidence lives across shared drives, GRC tools, Slack, email threads, and individual owners.
Proof workflow
Artifact
Checked against the shared graph
Metadata
Checked against the shared graph
Linked claims
Checked against the shared graph
Share decision
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
After the questionnaire, buyers ask for supporting proof, version context, and evidence packets.
What it does
Assembles buyer-specific evidence packets without rebuilding each response from memory, shared folders, and one-off email threads.
Where it fits
Fits after questionnaires, during procurement follow-up, and whenever a buyer asks for supporting proof.
Proof workflow
Buyer request
Checked against the shared graph
Selected claims
Checked against the shared graph
Evidence packet
Checked against the shared graph
Controlled share
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
Security and product truth often appears in chat before it becomes governed documentation.
What it does
Chat can surface draft claims and evidence leads. It does not create approved buyer-facing truth.
Where it fits
Fits for startups where decisions happen in chat and customer assurance needs a controlled path back to approved claims.
Proof workflow
Approved channel
Checked against the shared graph
Draft lead
Checked against the shared graph
Owner review
Checked against the shared graph
Claim candidate
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
AI diligence now asks about training posture, model providers, data boundaries, retention, human access, and risk governance.
What it does
Treats AI diligence as its own review track, with approved answers about model providers, training posture, retention, access, and risk governance.
Where it fits
Fits for AI vendors and AI-enabled SaaS teams facing model, data handling, and AI governance diligence.
Proof workflow
AI question
Checked against the shared graph
Provider record
Checked against the shared graph
Training posture
Checked against the shared graph
AI proof pack
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
Renewals ask for the same proof plus what changed since the last review.
What it does
Prepares 'what changed?' context for annual reassessments, customer audits, and repeat reviews without rerunning the whole proof project.
Where it fits
Fits for annual security reviews, enterprise renewals, re-assessments, and customer audit cycles.
Proof workflow
Prior pack
Checked against the shared graph
Changed proof
Checked against the shared graph
Freshness check
Checked against the shared graph
Renewal packet
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
Public trust centers do not eliminate custom follow-up requests, NDA-specific evidence asks, and buyer-specific routing.
What it does
Private beta — focused on seller-side request routing, not replacing your public trust center.
Where it fits
Fits for teams that already publish a trust center but still handle custom enterprise proof requests manually.
Proof workflow
Buyer request
Checked against the shared graph
Intake route
Checked against the shared graph
Owner queue
Checked against the shared graph
Proof workflow
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
Evidence, tasks, and deal context live in systems that do not share a common trust workflow.
What it does
Roadmap — scope will be integration-by-integration, starting with read-mostly evidence sources.
Where it fits
Fits after teams validate the core workflow and need stronger operational connections across the enterprise stack.
Proof workflow
Source systems
Checked against the shared graph
Evidence sync
Checked against the shared graph
Claim graph
Checked against the shared graph
TrustOps queue
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Problem
AI-assisted drafting and copy-paste workflows can create stale, unsupported, or over-shared buyer-facing output.
What it does
Keeps AI-assisted drafting and buyer-facing output behind review, evidence, freshness, disclosure, and audit gates.
Where it fits
Fits across every module because trust workflows need controlled release, not silent AI-created truth.
Proof workflow
AI draft
Checked against the shared graph
Human review
Checked against the shared graph
Policy gates
Checked against the shared graph
Buyer output
Ready for controlled reuse
Buyer-facing output remains gated by review, evidence, freshness, disclosure, and audit context.
Module Maturity
ProofRelay is modular by design. Guided pilots start narrow so teams can validate the highest-friction workflow without a steep learning curve. As the process becomes clear, modules can be configured around the same claim/evidence graph.
Validate the highest-friction workflow before expanding the platform surface.
Turn recurring assurance work into a durable operating layer.
Add buyer-specific AI and framework evidence without becoming a broad GRC clone.
Connect identity, reporting, portals, and system context as the workflow matures.
Keep proof current as buyers, products, policies, models, and contracts change.
Build around proof
Tell us where your review process breaks — questionnaires, evidence, AI diligence, renewals, portals, owner routing, or integrations — and we’ll recommend the right starting configuration.