AI · MVP → Retainer
Product intelligence for teams that ship.
Started as a fixed-scope MVP: the AI layer that surfaces sprint health and delivery risk.
What we built
- AI / Signals
- Sprint health + risk detection
- Analytics
- Real-time delivery metrics pipeline
- Enterprise
- SOC 2 aligned data handling
Engagement
The challenge
Engineering teams generate enormous amounts of signal — commits, PRs, sprint velocity, deployment frequency — but most of it sits in disconnected tools. Performalise wanted to build the AI layer that synthesizes these signals into actionable intelligence: is this sprint healthy? Where is delivery risk building up? Which teams are overloaded? The founder needed a technical team that could build the data pipeline and the AI models from scratch.
How we approached it
We started as a fixed-scope MVP engagement — build the core data ingestion pipeline and the first set of AI-powered sprint health indicators. The scope was deliberately narrow: prove that the AI could surface meaningful signals from noisy engineering data. Once the MVP validated the approach with early customers, the engagement expanded into a retainer, and we continued building out the analytics platform.
The technical work
The ingestion layer connects to project management tools, version control, and CI/CD pipelines — normalizing data from different sources into a unified event stream. The AI models run on top of this stream, detecting patterns that correlate with delivery risk: sudden drops in commit frequency, PRs sitting in review too long, sprint scope creeping mid-cycle. All data handling is SOC 2 aligned, since enterprise customers need assurance that their engineering metrics are handled securely.
Where it is today
Performalise delivers real-time delivery signals with 84% sprint accuracy — meaning the AI correctly predicts sprint outcomes four out of five times. The platform is used by engineering leaders who want data-driven visibility into their teams without adding process overhead. Our team continues to refine the models and expand the signal coverage.
The people behind this project
Client
Founded Performalise and defined the product thesis — that engineering teams generate enough signal to predict delivery outcomes. Validated the AI approach with early enterprise customers and shaped the roadmap around their feedback.
Zeroic
Shankar Prasad Built the data ingestion pipeline that normalizes signals from project management tools, version control, and CI/CD systems into a unified event stream. Designed the AI models for sprint health prediction.
Jatin Mukheja Owned the analytics frontend and the real-time delivery metrics dashboard. Built the SOC 2 aligned data handling layer and the enterprise-grade access controls.
Building an AI analytics product?
We built Performalise's signal-to-insight pipeline — from raw engineering data to 84% sprint prediction accuracy. If you're turning data into intelligence, let's talk.
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