AutoRFP.ai

Breadth across classes, and the vendor makes it the pitch. The features page states semantic search across 15+ connected sources and the trust center counts 18+ integrations included on every plan.

Named on the property: SharePoint, Google Drive, OneDrive, Box, Dropbox and Confluence and Notion for documents and wikis, Slack and Microsoft Teams for collaboration and for the Q&A agent, Intercom for support content, Google and Microsoft for single sign-on, and an MCP connection to external AI tools. Retrieval spans them by meaning rather than by keyword, and a conflict resolver compares recency and authority across sources before drafting. No CRM or conversation intelligence connector is published, so nothing learned in a discovery call or held in the pipeline reaches a response.

Sourceautorfp.ai/features, autorfp.ai/trust and autorfp.ai/integrationsread 2026-09-04

Named agents running a chain from intake to submission.

The AI Document Importer extracts every requirement from Word, Excel or PDF; the Portal Agent pulls questions straight out of RFP and security portals; go/no-go analysis scans for deal-breakers before the team commits; the Project Agent creates documents, analyzes content and edits responses across a whole project; the Q&A Agent answers one-off questions in Slack or Teams from the library with sources; project management tracks who is blocked and what is overdue; approvals move requirements to an approved state with a compliance breakdown across exceeds, compliant and non-compliant; and export returns answers in the prospect's exact Excel or Word file with macros, formulas and templates preserved. Approved answers flow back into the library automatically, closing the loop.

Sourceautorfp.ai/featuresread 2026-09-04

A self-building library over the customer's own systems, searched by meaning and cited on every answer. Retrieval runs semantically across 15+ connected sources including SharePoint, Google Drive, OneDrive, Box, Confluence and Notion plus past submissions and uploaded files, so a question maps to existing material without manual tagging, and the vendor's stated position is library-less: approved answers are auto-tagged and flow back in rather than being curated by a dedicated librarian.

Every draft is cited back to the source document and page with a Trust Score, and the platform will not draft what it cannot source, handing those requirements to a person instead. Governance is part of the grounding rather than bolted on: where two sources conflict the system compares recency and authority, marks one superseded and drafts only from the current one. Live web research supplies external context a library cannot hold. What the library accumulates across projects is the customer's own content.

Sourceautorfp.ai/featuresread 2026-09-04

A real approval surface with a gate in front of it, both the vendor's own. The agent refuses rather than guesses: where it cannot find a source it hands the requirement over to a person, which the vendor states as a design principle and shows in product, so an unsupportable question is routed to a human instead of being drafted around.

Around that sit the mechanics of approval: editor and reviewer roles on a response, requirements moving to an approved state with progress tracked as a count of approved items, subject-matter experts pulled in by mention in Slack and Teams, comments needing resolution, and a full audit trail on every approval with unlimited users included.

Sourceautorfp.ai/featuresread 2026-09-04

Certification and access controls are both documented, and the access controls come with every plan. Identity integration is single sign-on with Google and Microsoft, included on every plan, with a dedicated Microsoft SSO integration page covering Azure AD authentication, and the trust center states strict access controls based on role-based permissions with data boundaries between clients. The company is ISO 27001:2022 certified and SOC 2 Type II audited annually by external auditors.

Data is encrypted with AES-256 in transit and at rest, and each customer gets a logically separated environment. Regular security assessments and penetration testing are carried out, with critical vulnerabilities addressed within 24 hours. There is also a named data protection officer, a published transfer impact assessment and a trust center at autorfp.ai/trust. Customer content is stated never to be used to train public models.

Sourceautorfp.ai/trust, autorfp.ai/features, autorfp.ai/pricing and autorfp.ai/integrations/microsoft-ssoread 2026-10-01

Strong per-answer transparency, a human-side trail, and no account of the agent's process. Every answer carries a citation back to the source document and page, a Trust Score for source confidence, and a Feedback Score for how fully it answers what was asked, so a reviewer sees what a draft rests on and how sure the system is before reading it. Source conflicts are shown being resolved rather than resolved silently, with the superseded document named alongside the current one and recency and authority given as the reason.

On the human side there is a full audit trail on every approval, plus project dashboards, ROI reporting on AI usage and effort, capacity and win-rate reporting, and a gap analysis report tracking non-compliant requirements across projects. None of that reconstructs how the agent worked. There is no step by step account of why a passage was produced and no run trace, and the audit trail records approvals by people rather than actions by agents. Citations and confidence scores show what an answer rests on, not why the agent answered as it did.

Sourceautorfp.ai/featuresread 2026-09-04

A governed content library, not agent memory. Answers that clear review flow back into the library, are auto-tagged and become the material later questions map to, with unlimited storage, past submissions searchable alongside connected sources, ownership and renewal tracking, and a conflict resolver that supersedes older sources. That is the customer's content, and a content store is not memory. The vendor also says the system learns from each approved response. No per agent state across sessions, retention setting, inspection view or forgetting control is documented.

Sourceautorfp.ai/featuresread 2026-10-01

The customer picks the region, stated as a product property rather than a legal arrangement. The features page heads its security section with data hosted in the region you choose and names EU, US and AU data residency options alongside the ISO 27001:2022 and SOC 2 Type II attestations.

The trust center adds that each customer runs in a secure, logically separated environment with strict data boundaries maintained between clients, and that the company has completed a transfer impact assessment covering where data entered into the platform may reside, published on request through a named data protection officer. There is no self hosted or on premises option, and the three regions are the whole list rather than a starting point.

Sourceautorfp.ai/features and autorfp.ai/trustread 2026-09-04

A browsable catalog that customers can also contribute to. AutoRFP.ai publishes a Skills Library with a submission route, so packaged capabilities are selected from a collection rather than configured from scratch.

Around it sit branded document templates and generation of standard bid artifacts such as executive summaries and timeline sections, dedicated automation for security questionnaires including CAIQ, SIG and NIST frameworks, solution routes for RFPs, DDQs and security questionnaires, and named agents shipped ready to use: the Project Agent, Q&A Agent, Portal Agent and document importer.

Sourceautorfp.ai/skills and autorfp.ai/featuresread 2026-10-01

Several routes in and real channel coverage, with nothing on a clock. Work enters by importing Word, Excel or PDF with requirements extracted automatically; by the Portal Agent pulling questions directly out of RFP and security portals; by asking the Q&A Agent in Slack or Teams; and through the developer API or the MCP server from an external assistant.

Work leaves the same way: notifications and mentions to subject-matter experts in Slack and Teams, and export back into the prospect's original Excel or Word file with macros and formatting preserved. Nothing fires on its own. No schedule, recurring run, event trigger, inbound email watcher or webhook listener is documented, and the Portal Agent is invoked by a person on a portal rather than polling for new questionnaires.

Sourceautorfp.ai/featuresread 2026-09-04

Providers named, selection kept by the vendor. The FAQ answers the question directly: AutoRFP.ai uses the most performant and secure models available from trusted providers such as Microsoft Azure AI, and directs anyone wanting detail to a demo.

The security section names AWS, Google Cloud and Azure as the AI platforms it runs private inference through, each under zero-retention and no-training terms, and the product describes drafting from a private AI model with customer data never leaving the environment during inference. So more than one named provider runs internally, but the buyer is given no selection.

There is no model list, no routing policy stating which model handles which task, no per project selection and no bring your own key, and the vendor says its current approach is discussed on a demo rather than published.

Sourceautorfp.ai/featuresread 2026-09-04

Two documented external surfaces and a status endpoint for them. AutoRFP.ai publishes a Developer API and MCP page, runs an API status endpoint at api.autorfp.ai, and ships an MCP Server as a named product capability that connects with any external AI agent, naming Claude, ChatGPT and Gemini, so a customer can use their AutoRFP.ai data from the assistant they already work in.

The content management section describes the same thing from the other direction, an MCP connection to the customer's AI tools sitting alongside the connected source library. No SDK or client library is published, and the MCP is described as surfacing AutoRFP.ai data rather than as executing actions back on the platform.

Sourceautorfp.ai/developers, autorfp.ai/features/mcp and autorfp.ai/featuresread 2026-09-04

Every answer scored twice before a person sees it, and nothing compared. The platform runs two automatic assessments on each draft: a Trust Score for confidence in the source material and a Feedback Score for how fully the response answers what was asked, both readable and both applied before review, so the weakest answers surface rather than hiding in a batch.

A gap analysis report tracks non-compliant requirements across projects, ROI reporting measures AI usage and effort saved, and a reviewer can prompt the agent to regenerate or sharpen a response and see the change as a diff before accepting or declining it. Those are mechanisms the customer holds, operating on the agents' own output. What none of them does is compare. There is no controlled test of one configuration against another, no scored evaluation set, no benchmark and no regression check when the library or prompt changes. There is no outcome loop connecting submissions to won or lost deals, so scoring never calibrates against results.

Sourceautorfp.ai/featuresread 2026-09-04

An agent that works inside procurement portals it has no API to. AutoRFP.ai ships a Chrome extension carrying its Portal Agent, which pulls questions from RFP and security portals, generates answers from the customer's library, and pastes them back automatically without copy-paste. The vendor names the target explicitly elsewhere on the property, stating support for RFPs arriving through web portals and citing SAP Ariba.

That is the agent reading and writing in software the vendor does not control, through the interface a human would use. Live web research, by contrast, is search and retrieval rather than browser control. Its reach is scoped to questionnaire portals rather than arbitrary software, and no general browser control, headless session or virtual desktop is offered.

Sourceautorfp.ai/features and autorfp.ai/features/ai-rfp-chrome-extensionread 2026-09-04