FlowAI Product

Engineered for trustworthy, reliable software delivery.

Turn a goal, ticket, or backlog item into a coordinated software delivery workflow. FlowAI connects specialist agents, separate reviewers, validation gates, durable artifacts, and bounded remediation in one native delivery runtime.

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Native delivery controls

Trust you can inspect. Reliability you can follow.

FlowAI makes review, evidence, and recovery part of the software delivery workflow, from the first plan to the final delivery decision.

Trustworthy software delivery

Make delivery decisions with reviewable evidence and clear accountability.

  • A different engineer reviews the implementation; architecture review uses a different architect when available.
  • Structural, engineering-quality, and grounded-truth validation inspect implementation, review outputs, and upstream context.
  • Durable artifacts and validation findings make decisions inspectable. Unresolved findings can escalate to human review.

Reliable software delivery

Keep work coordinated, preserve progress, and respond to failures within explicit limits.

  • Dependency-aware orchestration coordinates specialist tasks through a defined delivery path and final gate.
  • Persistent memory and artifacts preserve evidence across retries and remediation cycles.
  • Eligible design defects return to the architect within a bounded rewind budget. Requirements issues and exhausted remediation escalate to a person.
Inspect the workflow in a demo

Trust and reliability are runtime capabilities.

The team manager tracks dependencies. The execution harness preserves work and evidence. Validation and reflection determine whether to proceed, remediate, or escalate to a person. These controls are built into how FlowAI runs software delivery.

Agentic team management

A coordinating manager decomposes work, assigns specialist roles, tracks dependencies, and keeps each run moving through explicit quality gates.

  • Goal-to-plan decomposition
  • Role-based specialist assignment
  • Run status, retry, and review visibility

Controlled execution runtime

Each task runs inside bounded workspace, network, and toolchain controls so generated work can be inspected before it reaches your systems.

  • Sandboxed file and process execution
  • Provider routing and runtime policy controls
  • Durable artifacts and replayable run history

Production governance

Human approval, tenant isolation, usage metering, and audit trails are first-class product behavior rather than after-the-fact reporting.

  • Human review gates
  • Tenant and billing boundaries
  • Audit-ready workflow records
Operating Model

A delivery loop with checks and recovery built in.

01

Capture task context

02

Plan and staff agents

03

Execute in sandbox

04

Validate and reflect

05

Route human review

06

Publish artifacts

Where It Fits

Built for teams that need repeatable AI delivery operations.

Turn backlog items into implementation-ready workflow runs.
Run governed code changes, validation, and artifact capture in one place.
Connect agent work to Git, Jira, Slack, billing, and tenant controls.
Scale from anonymous demos to team workspaces without changing the operating model.

See the agentic team run before you commit.

Start with the public demo, then create a workspace when you are ready to run real tasks with team controls.

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