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OpenLM — SaaS Platform Transformation

Sept 2022 – Present  ·  Tribe Lead

3 → 85+
Team scaled
$2K → $35K+
Avg. customer ARR
70%
Infra cost reduction
Daily
Release cadence (was 1–2/yr)
50%
Downtime reduction
Near real-time
Data (was twice-daily batch)

I was brought in with a clear mandate: transform OpenLM from a legacy, monolithic license management tool into a scalable, AI-first SaaS platform capable of serving global enterprise customers. Over two years I owned the full scope — product architecture, engineering team scaling, real-time data platform, AI integration, commercial strategy, and global expansion — working directly with the founders and leadership team.

Strategic Mandate

OpenLM had a hard 1,000-user ceiling, a twice-daily batch reporting architecture, and a customer base of SMB accounts with average renewals of $2K–$5K. The business needed to move upmarket to win enterprise deals across US, Europe, and Japan — but the platform couldn't support it.

  • Legacy monolith — no multi-tenancy, no horizontal scale, no real-time data.
  • 1–2 software releases per year — too slow for enterprise feature demands.
  • No AI capabilities, no NLP interface, no consumption-based pricing.
  • 3-person engineering team — insufficient for the scope of transformation needed.

What I Built

SaaS Platform Re-architecture

  • Re-architected the legacy monolith into a microservices-based, cloud-native SaaS platform.
  • Enabled multi-tenant architecture with distributed data using Apache Spark and Kafka.
  • Removed the 1,000-user ceiling — platform now supports unlimited enterprise-scale deployments.
  • Aligned product architecture with a consumption-based SaaS monetization strategy.

Real-Time Data & Analytics Platform

  • Built a streaming analytics platform replacing the batch-based reporting system.
  • Transitioned from twice-daily reporting to real-time license and usage insights.
  • Scaled data capacity from GB to TB-scale processing.

AI Engineering & DevOps

  • Introduced AI-assisted development, testing, and CI/CD automation.
  • Implemented predictive alerting and anomaly detection across microservices.
  • Release frequency: 1–2 per year → daily deployments.

NLP-Based Product Innovation

  • Developed an NLP-based MCP reporting interface — converting complex BI into conversational queries.
  • Simplified product UX and improved enterprise customer adoption.

Multi-Platform BI Ecosystem

  • Delivered a multi-platform BI architecture (Power BI, Superset, QuickSight) for flexibility.
  • Enabled upsell opportunities through analytics-driven value creation for customers.

Tech Stack

Apache Spark, Kafka, Airflow  ·  AWS / Azure  ·  Kubernetes, Docker, Swarm  ·  Power BI, Superset, QuickSight  ·  NLP / LLM integration  ·  AI-based monitoring & anomaly detection  ·  CI/CD (GitHub Actions)  ·  Postgres / ClickHouse

Business Outcomes

  • Average customer renewal: $2K–$5K → $35K+ — enabled entry into enterprise segment across US, Europe, and Japan.
  • Team: 3 → 85+ people — scaled engineering and support across 4 product lines with 24×7 global coverage.
  • Infrastructure cost: –70% — consumption-based architecture and right-sizing eliminated Apache overhead.
  • Releases: 1–2/year → daily — AI-assisted CI/CD cut development effort by 30–50%.
  • Downtime: –50%, support tickets: –20% — via AI-based monitoring and anomaly detection.
  • Customer onboarding: 1 week → near real-time — automated provisioning removed manual bottlenecks entirely.
  • GEM certified — unlocked government contract segment, demonstrating enterprise compliance standards.

Selected Visuals

Click any image to enlarge.

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