Cresance

Cresance builds an AI-driven cloud cost optimization product designed to identify waste and reduce spend. The core challenge is not a dashboard—it’s ingesting messy billing/usage data, detecting waste patterns, and surfacing recommendations that engineering and finance teams will actually act on.

Problem: Cloud spend optimization is difficult because:

• Billing and usage data is noisy, large, and provider-specific
• Recommendations must be explainable enough to earn trust
• Savings need to be actionable (not generic “turn things off” advice)
• Early-stage teams can stall on execution due to data complexity and integration churn

Solution: Horizon Labs supported engineering execution with a focus on shipping usable product workflows:

• Implemented/extended pipelines to transform cost/usage inputs into savings insights
• Built product workflows that surface findings in a usable, decision-oriented way
• Focused on reliability and edge-case handling so outputs remain credible
• Worked closely with the founder to prioritize deliverables that matter for early customers

Impact:

• Improved delivery velocity by reducing founder bottlenecks
• Shipped product milestones suitable for real customer evaluation
• Strengthened product credibility through reliable data-to-insight workflows
• Established a foundation for expanding integrations and deepening optimization logic

Implemented Product Capabilities:

• Cost/usage data ingestion and normalization
• Waste detection logic and recommendation surfacing
• Reporting views and savings opportunity summaries
• Admin/config workflows for connecting accounts and baselines