At a glance
- AI Coach
- LOTTE - contextual coaching from live work signals
- Analytics
- Company analytics, value intelligence, delivery predictability
- Team ops
- Kaizen engine, agile events, team voice, kudos
- Enterprise
- SOC 2 aligned data handling
Engagement
What it does
Performalise turns the work signals engineering teams already generate - commits, PRs, deploys, sprint velocity - into coaching, analytics, and predictability. LOTTE AI Coach gives contextual guidance to individuals and leaders. Company Analytics, Value Intelligence, and Predictability modules give senior leaders and agile coaches a view of delivery health without asking teams to file status updates. Positioned against Jira dashboards, Azure DevOps, Linear, and Parabol. SOC 2 aligned.
What we built
Started as a fixed-scope MVP: prove the AI could surface meaningful signals from noisy engineering data. Scope was deliberately narrow - core data pipeline and the first sprint health indicators. Once the MVP validated with early customers, it converted into a retainer and we kept building the analytics platform as it grew into the LOTTE + Value Intelligence + Kaizen suite it is today.
The technical work
The ingestion layer connects to project management tools, version control, and CI/CD - normalizing data from different sources into a unified event stream. AI models run on top, detecting patterns that correlate with delivery risk: sudden drops in commit frequency, PRs stuck in review, sprint scope creeping mid-cycle. All data handling is SOC 2 aligned for the enterprise buyer.
Where it is today
Performalise runs the LOTTE AI Coach, Company Analytics, Value Intelligence, Kaizen engine, and Team Voice / Kudos across 5+ enterprises. Zeroic engineers still on the retainer, 2.5+ years in.
The people behind this project
Client
Josef Bacher 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.
I hired Zeroic for a fixed-scope MVP of Performalise two-and-a-half years ago. They built the data pipeline, the AI sprint health models, the SOC 2 data layer - and they're still on retainer. Says most of what I need to say.