B2B mentoring and learning platform
FutureLab AI Workflows
AI-assisted transcription, summarization, and workflow automation for mentoring operations and B2B delivery.
Outcome
Helped product and operations teams turn session data into summaries, follow-ups, and delivery visibility.
Problem
Mentoring sessions produced valuable context, but too much of it disappeared into manual notes and follow-up work.
Approach
Built service objects around transcription and summarization jobs, added tests around edge cases, and treated AI output as reviewable product data rather than magic text.
Architecture
Rails workers process recordings and session metadata, call model services, persist generated summaries, and expose review states inside existing admin workflows.
Result
Introduced practical AI workflows into production while keeping the platform maintainable for a small engineering team.
Lessons learned
The best AI features often look like boring workflow software: queues, retries, review states, and clear ownership.
Constraints
- Integrate AI into an existing Rails product without destabilizing core flows.
- Keep outputs reviewable for operations teams.
- Control cloud and model costs as usage grows.
Technical decisions
- • Wrapped model calls in tested service boundaries.
- • Used asynchronous jobs for long-running transcription and summarization.
- • Optimized AWS resources and database access patterns alongside feature work.
Key features
- • AI-generated session summaries.
- • Operational review flow for generated content.
- • Cost-conscious background processing.