Announcing IntakeOS: AI-Powered Process Intake for the Enterprise Automation Era

For the past two years, we've been working alongside enterprise automation teams - CoE leads, business analysts, RPA architects, and the business users who submit the ideas that power their pipelines. What we heard, consistently, was a version of the same problem: the front door of automation - the intake - is broken. It's manual, it's inconsistent, it produces variable-quality data, and it's the single biggest bottleneck in programs that want to scale.

Today we're releasing IntakeOS - an AI-powered platform that transforms how enterprise automation programs discover, qualify, and prioritize opportunities. We built it to solve the specific, concrete problems we heard from hundreds of automation practitioners. Here's what it does.

Introducing VARA: Your AI Business Process Consultant

At the center of IntakeOS is VARA - an AI business process consultant trained on the Problem-First qualification methodology. VARA conducts structured discovery interviews with business users, adapts its questions based on what it learns, probes vague answers, and produces a complete, structured qualification report automatically.

VARA isn't a chatbot with scripted responses. It's a reasoning system that understands the logic of process qualification - why you ask about root cause before pattern matching, why the IDP sub-tree applies when documents are mentioned, why developer complexity matters for backlog planning. It conducts interviews the way a skilled consultant would, but at scale, consistently, for every intake submission regardless of when it arrives or who submits it.

The Four-Gate Qualification Framework

Every intake conducted through IntakeOS follows the Problem-First four-gate framework: Problem Statement (is the problem defined and mature?), Root Cause Analysis (could fixing the underlying system solve this?), Pattern Matching (which of the 7 automation patterns fits?), and Backlog Refinement (Expert or Citizen Developer scope?). The framework is enforced consistently - not as a form, but as a conversation that adapts to the specifics of each process.

What IntakeOS Produces

• A complete, structured qualification report for every intake - problem summary, root cause finding, recommended automation pattern with rationale, ROI estimate, developer complexity, and recommended next steps.
• A prioritized pipeline view for CoE administrators - sortable and filterable by pattern, ROI, complexity, department, and qualification status.
• Mermaid.js process maps - AI-generated current-state and future-state process diagrams that provide instant visual context for every candidate.
• Executive reporting - portfolio-level automation potential and ROI summary across the full pipeline.
• Deep Dive capability - admin-triggered follow-up questions to surface specific information gaps without requiring additional workshop sessions.

Who It's For

IntakeOS is built for enterprise automation teams - CoE leads who need to scale their pipeline without scaling their team, business analysts who spend too much time in intake workshops, and business users who want a fast, professional intake experience without having to navigate a 50-question form.

It integrates with the tools CoE teams already use - exporting to UiPath, Microsoft Power Automate, and other RPA platforms, with process definition documents (PDDs) and solution design documents (SDDs) generated automatically from the intake data.

What's Available Today

The IntakeOS platform launches today with multi-tenant organization management, the VARA AI interview engine, the Problem-First four-gate qualification framework, ROI calculation, process mapping, executive reports, and the full admin suite including intake management, user management, technology catalog, scoring configuration, and audit logs.

We're offering a live demo environment where you can experience the full intake process as a business user and review the resulting qualification report as a CoE administrator. No setup required - try it at intakeos.ai/demo. We'd love to hear what you think.