A connected, human-controlled workflow helped product managers prepare clear delivery tasks faster using the tools and approval process already in place.
Assess your highest-value workflowProduct managers were repeatedly rewriting the same product decisions across four tools. The manual handoff slowed delivery and made it harder to keep work consistent.
Liorant connected the existing tools into one controlled workflow that uses approved product knowledge, checks every draft and keeps people in control.
The product team's estimate for the requirements-to-delivery stages supported by the workflow.
Notion, Miro, Figma and the team's delivery-tracking tool.
Every draft follows the same four-level structure and passes the same quality checks.
Product managers had to turn requirements and research into clear delivery tasks, then enter the same information again across four product and design tools.
The goal was not simply to generate more text. The team needed a faster, more consistent handoff that respected its existing process, tools and approval responsibilities.
The product team estimated a 30–40% reduction in time spent on the requirements-to-delivery stages supported by the workflow.
Four existing product and design tools now operate as one connected process. Every draft follows the same four-level work structure, passes the same quality checks and remains subject to human approval.
Executive implication: product managers can redirect time from manual re-entry toward prioritization, review and delivery support without adding another interface.
Product decisions were rewritten manually for each destination tool.
Approved requirements move through one controlled workflow into delivery-ready drafts.
Client team estimate for the requirements-to-delivery stages supported by the workflow.
Claim boundary: the estimate is directional, not an independently audited company-wide productivity metric.
Three business risks made a standalone assistant the wrong fit.
Free-form answers vary from one request to the next, making reviews slower and team-wide consistency difficult.
A separate chat interface adds another handoff instead of improving the tools and process people already use.
Without shared checks, approved company knowledge and provider independence, quality and long-term control remain fragile.
The project started small, proved value and then connected the full handoff.
Liorant first tested whether approved requirements could become clear delivery tasks, proving the value before expanding the scope.
The workflow was grounded in the team's users, product decisions, working methods and design standards.
Every draft follows the same structure, passes automated checks and remains subject to human review.
Approved work moves into the team's delivery and design tools instead of creating another interface to monitor.
AI helps prepare the draft, but people remain responsible for approval before work reaches delivery.
The executive case stands on its own. Technical teams can expand the implementation pattern below.
The team's knowledge, working rules and quality checks are kept separately from the AI provider. The AI supports the process; it does not define it.
That protects the investment. The organization can change providers or connected tools without rebuilding the way good product work is defined.
The value came from improving an important handoff inside the existing business process — not from adding another AI tool.
No. The system prepares clear, structured drafts and checks them before they reach delivery tools. Product managers still review and approve the work before it enters production.
Those were the client's existing tools. The same approach can connect to a different product or delivery stack without changing the core workflow.
Because the product methodology — hierarchy, rules, validation — lives outside the AI model, switching the underlying provider doesn't require rebuilding the workflow.
The product team provided the estimate for the specific requirements-to-delivery stages supported by the workflow. It is directional evidence, not an independently audited company-wide productivity metric.
In a focused 30-minute session, we map one recurring handoff, identify where time is being lost and assess whether an AI-supported workflow is worth pursuing. You leave with a mapped bottleneck, an initial feasibility view and the evidence needed for a business case — no slides, no pitch.
A first workflow can typically launch in as little as 4 weeks, depending on scope and integrations.
