One of the most transformative features we shipped at DailyBot was the integration of AI capabilities directly into our workflow engine. Here’s the story of how we did it.
The Vision
We wanted DailyBot to go beyond simple automation. Instead of just running predefined workflows, we wanted the platform to understand context, generate insights, and help teams make better decisions.
Architecture Decisions
When integrating LLMs into DailyBot, we faced several key architectural choices:
1. Model Selection
We evaluated multiple LLM providers and settled on a multi-model approach, routing different tasks to the most appropriate model based on complexity and latency requirements.
2. Prompt Engineering at Scale
With thousands of teams using DailyBot, we needed prompt templates that were both flexible and consistent. We built a prompt management system that allows per-workspace customization while maintaining quality baselines.
3. Cost Optimization
AI inference costs can spiral quickly. We implemented intelligent caching, response pooling, and tiered processing to keep costs manageable at scale.
Production Challenges
Rolling out AI features to production taught us several important lessons:
- Latency matters — users expect near-instant responses in chat integrations
- Fallback strategies are essential when AI services experience downtime
- User trust requires transparency about when AI is generating content
Results
The AI-powered features became one of DailyBot’s strongest differentiators, helping teams summarize discussions, generate action items, and surface insights that would otherwise be lost in async communication.
Building AI into a production SaaS product at scale is a different beast than prototyping. The engineering challenges are real, but so is the impact.