Building conversational AI
We work with teams who need a structured approach to AI system development. Our mentorship covers architecture decisions, response quality, edge case handling, and deployment strategy.
See what we offer
Understanding the problem first
Before writing any code or choosing frameworks, we analyze what you're trying to build and why. Most AI projects fail because they skip this step.
User intent mapping
What are people actually trying to do when they interact with your system? We map real user needs to technical requirements.
Data assessment
Looking at what you have to work with — existing conversations, documentation, support tickets, product data. Quality matters more than volume.
Failure scenario planning
Every conversational system breaks in predictable ways. We identify the edge cases and plan for graceful degradation before they become production issues.
Remote setup that works
Time zones and locations don't matter when the infrastructure is designed for async collaboration. We use scheduled calls for complex decisions, written documentation for everything else.
Most communication happens through shared documents, recorded code reviews, and structured async updates. Real-time collaboration when needed, deep work when possible.
- Video calls scheduled around your core working hours
- Written feedback on implementations within 24 hours
- Shared documentation accessible anytime
- Code reviews with detailed explanations
How initial assessment works
Initial call
30-minute discussion about what you're building and current blockers
System review
Look at existing code, architecture docs, or detailed requirements if starting fresh
Assessment report
Written analysis covering technical gaps, architecture issues, and recommended focus areas
Decision point
Decide if ongoing mentorship makes sense based on the assessment findings
Development path structure
Three phases that build on each other. Most projects need all three, but timing varies based on team experience and project complexity.
Foundation phase
Setting up the architecture correctly so you don't have to refactor everything in two months. This includes choosing appropriate frameworks, designing the conversation flow structure, and establishing evaluation criteria.
Implementation phase
Building the core functionality with proper error handling and monitoring. This is where most projects encounter unexpected issues with response quality, latency, or edge cases.
Optimization phase
Making it faster, more reliable, and easier to maintain. This includes prompt optimization, caching strategies, and setting up proper monitoring for production issues.