FlowBridge AI — a 4-month rebuild of an enterprise migration & workflow ecosystem. From spreadsheets and brittle scripts to a guided, AI-assisted system that ships data confidently.
FlowBridge serves enterprise customers migrating from legacy spreadsheets, in-house tools and fragmented dashboards. Onboarding was the single biggest reason deals stalled, support tickets spiked, and trust eroded.
Stakeholder interviews · enterprise client interviews · workflow mapping sessions · usability testing on the existing system. The patterns were unmistakable.
“Fear of breaking data is the biggest blocker.”
Users don't understand system dependencies.
Admins want control, not just automation.
Real-world workflows are non-linear.
Don't hide the system. Reveal it in approachable layers.
Confirmations, previews, dry-runs. Speed comes second.
Chunk by intent. Progressive disclosure on every screen.
An ecosystem of interlocking parts — not a flow chart.
Each feature solves a specific failure mode in the original product. Together they form an ecosystem where users always know where they are, what just happened, and what's safe to do next.
Problem · Users struggled to import data from sprawling, inconsistent sources.
Problem · Users couldn't see how source fields aligned with destination schema.
Problem · Users discovered issues only after migration completed.
Problem · Admins lacked visibility and governance for large teams.
Problem · No visibility = anxiety, drop-off, support tickets.
Progressive disclosure, chunked workflows, and confidence-building UI states. Every interaction earns the user's next click.
One overwhelming form. No feedback. Errors surfaced after submit. Users abandoned at the mapping stage.
Step-by-step guided flow with real-time feedback and inline help. Users always know what's safe to do next.
A Grammarly-level expectation: intelligence that surfaces options inline, never overrides judgment, and is always overrideable.
Suggests destination fields with confidence scores.
Reads source structure, infers types and relationships.
Proposes fixes for invalid rows — user approves.
Recommends next steps based on similar migrations.
Measured across the first 90 days post-launch with enterprise pilot accounts.
User confidence: significantly up. Tracked via post-migration NPS and qualitative interviews — admins reported feeling “in control” for the first time.
Stakeholder + user interviews · workflow mapping · audit of current product.
Insight clustering · jobs-to-be-done · failure-mode mapping.
Ecosystem architecture · primitives · interaction patterns library.
Mid-fi Figma prototypes · narrated walk-throughs with engineers.
Moderated tests with 8 enterprise users · iterating on the mapping stage.
Specs, edge cases, motion notes · in-sprint design review with engineering.