Governed AI that actually works inside the business.
Client Service Ops
Governed workspace
Quarterly reporting pack
Draft the Q3 client commentary from holdings_snapshot using the approved template, then route for review before anything leaves the workspace.
Activity
jori connected Flowchestra to FC-2418
Checking the request against workspace policy…
Worked for 4s
- zero_data_retention enforced for this provider
- holdings_snapshot masked 2 client identifiers
approved provider · governed workspace · activity logged
Illustrative view of the Flowchestra workspace.
Flowchestra connects your workflows, data and policies to the AI tools and models your organization uses, so AI-powered work can run with governance, visibility and measurement built in. That's the whole idea. This page shows how it works.
30 minutes on your workflows, not a slideshow.
Security review first? Read the Trust page →
One platform, three layers.
Control and governance.
Set organization-wide AI policies, then apply more granular guardrails by workspace or user. Control model and provider access, privacy, sensitive information, and usage without managing every interaction manually.
Execution and orchestration.
Where the work happens. Governed workspaces and workflows where your teams and AI work together, with human review built into the operating process. Configurable human approval gates are coming soon.
Visibility and intelligence.
Usage, spend and activity are visible across teams and workflows, with audit logs that support review and accountability.
From business context to measurable outcomes.
Five things the platform does, in the order they matter.
Understand the business.
Flowchestra maps your workflows, teams, approvals, systems and policies. AI starts from how your business actually works.
Connect business context.
Tools, data, documents and workflows come together in one governed layer, so AI has the context real work requires.
Orchestrate AI execution.
Work moves through governed, repeatable AI workflows, with automation where it's safe and human oversight where it matters.
Govern and observe.
Policy enforcement, security and auditability sit inside execution, not around it. Every workflow can be controlled, traced and reviewed.
Measure and optimize.
Track ROI, usage, cost, workflow performance and adoption over time, building the evidence needed to measure AI's business impact.
What this looks like on a real workflow.
Take client onboarding: one of the workflows teams typically start with.
The workflow gets mapped.
The steps, the documents, the approvals and the people involved go into the platform, so the AI works from your actual process.
The rules attach.
Who can run it, which models and providers are allowed, and what guardrails apply. Those controls stay connected to the workflow as it runs.
AI does the heavy drafting.
Intake documents get read, summarized and checked for what's missing. Draft welcome packs and checklists get produced inside the governed workspace.
Human review stays in the process.
Teams can review AI-generated work before it moves into client-facing, sensitive or higher-risk use.
Everything is on the record.
Every run is logged and measurable: what ran, what it cost, where the time went. The workflow improves because you can finally see it.
AI can only scale when it is governed by default.
Governance in Flowchestra isn't a settings page you visit after something goes wrong. It's the layer everything runs through, built around three questions:
Who can use what?
Access governance: which models, tools, data and workflows each person can use, under what controls.
How does data move?
Data governance: how data flows, is protected, trusted and retained, with sensitive data flagged before it goes anywhere.
Can we trust the result?
Deeper validation, review, explainability, and policy alignment for AI-generated outputs before they move into higher-risk or client-facing work.
Security detail, certifications and data handling live on the Trust page. Read how we handle security and data →
Use the AI tools, models, and private models you already trust.
Flowchestra gives you control over which models and providers your organization can use, and under what conditions. Apply organization-wide policies, then refine access and usage at the workspace or user level.
Deployment, hosting and residency questions are answered directly, and in writing. Read the Trust page →
What evaluators ask about the platform.
A Secure AI Operations platform is the operational layer between a business and the AI tools and models it uses. It maps how the business works, runs AI inside governed workflows with human oversight, and makes usage, cost and outcomes visible. Flowchestra is a Secure AI Operations platform.
No. For exploration and non-sensitive workflows, teams may still use approved AI tools directly. Flowchestra can also work alongside and integrate with approved tools where supported. Complex workflows and sensitive work are different. When AI touches client data, approvals and real processes, you need to know who's using what, with which data, under which rules. That work runs in Flowchestra: governed while it happens, not just audited after.
Flowchestra supports a broad range of AI models and providers, including private models. You decide which models and providers are available, to whom, with what data, and the platform enforces it.
It depends on how many workflows you start with, which is why we scope it live rather than quote a number here. We start by mapping one high value workflow together, and rollout grows from there. Book a demo and we'll scope it against your actual work.
See your own workflow run governed.
Bring one real workflow. We'll show you what it looks like inside the platform.
Security review first? Read the Trust page →