Every board wants to know how AI is changing the operation. A roadmap on paper isn't the same as AI doing real work inside a real process today — reading documents, drafting responses, and routing decisions, with every action logged and costed.
In Opzaro, AI is called directly from a workflow step — reading a document, assessing a case, drafting a response, and handing off to a person only where judgement is genuinely required. That's a materially different answer to "what's our AI plan" than a pilot project or a generic assistant.
From the process designer, a business user can describe what a step should do in plain English. Opzaro reads the entire process — every upstream field, every available helper function — and generates ready-to-use step logic, constrained to your real data model rather than hallucinated against it.
AI extracts document data, assesses risk against your criteria, and routes to the right underwriter.
AI validates figures against the purchase order and auto-approves within your parameters.
An AI story the board can sign off on needs answers to the same questions as any other operational change. Opzaro's answers are grounded in what the platform actually does — not a compliance claim we can't stand behind.
AI actions sit in the same audit trail as human actions — who, what, when, and on what basis — for every task, automatically.
Token cost from AI calls is tracked and accumulated against each task, so you can see and report AI spend by process or by case.
AI calls return strictly-typed, schema-validated JSON — reliable enough to drive further workflow logic, not text someone has to interpret.
Each step's permissions, SLAs and exit conditions apply to AI exactly as they do to people — AI works inside the guardrails you set.
Every Opzaro customer gets a dedicated, single-tenant instance and database — provisioned as its own isolated deployment, not a shared multi-tenant backend. That matters for a board conversation about data ownership and blast radius: your data sits in your instance, not a shared table with other customers.
Every deployment is automatically recorded — server, domain, exact code commit, and timestamp — and the running application's own footer links directly to the exact commit currently live. A customer, or their auditor, can always verify precisely what code is running, and when it was deployed.
Grounded in your process's actual fields and functions — a plain-English instruction becomes working automation, reviewable before it's saved.
Optional KYC identity verification before a signer can sign — a native option for higher-stakes documents, not a costly add-on.
A built-in, IP-protected marketplace with Shopify checkout integration — a possible new revenue line for packaging what you've already built.
We'll show AI acting inside a real process — not a demo chatbot — using an example close to your operation.