Building a Scalable AI Platform: 3 Key Lessons from Intercom
Discover how Intercom built a scalable AI platform with 3 key lessons—from evaluations to architecture—to lead the future of customer support.
Lesson 1: Prioritize Evaluations from Day One
Intercom learned early that rigorous evaluations are the backbone of any reliable AI system. Without a robust evaluation framework, it’s impossible to measure improvements or catch regressions as models evolve.
- Build a golden dataset of real customer interactions.
- Automate regression testing for every model update.
- Use both offline metrics and online A/B tests to validate performance.
Lesson 2: Design for Modular Architecture
A scalable AI platform requires decoupled, modular components that can be independently updated and scaled. Intercom’s architecture separates the orchestration layer from the model inference layer, enabling rapid iteration without downtime.
“We treat every AI feature as a product with its own lifecycle, not a monolithic model.” — Intercom Engineering Team
Lesson 3: Embed Human-in-the-Loop Feedback
To lead in customer support, AI must continuously learn from human feedback. Intercom built closed-loop systems where customer satisfaction ratings and agent corrections directly feed into model fine-tuning.
# Example: Feedback pipeline snippet
def collect_feedback(response_id, rating):
store_in_db(response_id, rating)
if rating < 3:
trigger_human_review(response_id)
These three lessons—evaluations, modular architecture, and human feedback—form the foundation of Intercom’s AI strategy, enabling them to scale while maintaining quality and trust.