We build the systems a business runs on, and the product culture that keeps them alive.
Engineering is hired into one of two rooms.
Sometimes it is a blank page, zero to one, a product that does not exist yet. Sometimes it is a running enterprise platform with a decade of decisions already baked in. The engineering changes; the method does not. We understand, we analyse, and then we install.
Before a line of code, we learn the business.
Whether it is a founder with a thesis or a platform team with a backlog, the first job is the same: understand how value actually moves, where the risk lives, and which constraints are real versus inherited. Zero-to-one and enterprise start from the same question.
We separate the product from the plumbing.
Architecture, data model, delivery pipeline, the org that ships it. We map what compounds and what decays, then decide what to build new, what to keep, and what to quietly retire. A feature decays; a mechanism compounds, the analysis is where we tell them apart.
We don't hand over a repo. We install a way of working.
Enterprise-grade product management, the rituals, the review gates, the CI, the ownership, embedded into the team that stays. When we leave, the culture is the deliverable: the platform keeps shipping without us.
The shape of the work.
Platform architecture.
The spine a product should have had, data model, service boundaries, and the decisions that decide whether it can grow without a rewrite.
→End to end delivery.
Web, API, and data, shipped end to end by a small team that owns the whole path from commit to production.
→Legacy modernisation.
Untangling a decade of enterprise decisions: what to keep, what to replace, and how to migrate without stopping the business.
→Platform & DevOps.
CI/CD, observability, and the release discipline that lets a team ship on a Thursday and sleep on a Friday.
→AI engineering.
Models, retrieval, and evaluation built as infrastructure inside the product, governed, cost-modelled, and removable.
→Product management.
Enterprise-grade PM installed into the team that stays, rituals, roadmaps, and ownership that outlast the engagement.
→The team you meet in week one is the team that ships in month nine.
Chetan Giri
Chief Technology Officer · Ex Ripple AI · New YorkOwns architecture and delivery across the toughest platform work, the person who draws the spine before the sprint.
Om Rajani
AI EngineerBuilds models, retrieval, and evaluation as infrastructure inside the product, governed, cost-modelled, and removable.
Ujala Choudhary
Backend Engineer · Ex TCSBuilds the pipelines, observability, and release discipline that turn a prototype into an enterprise-grade platform.
Aryan Singh
Product Manager · Ex InnovacerInstalls the rituals and roadmap that let the client's team keep shipping long after we leave the room.
Shivraj Roy
Frontend EngineerShips end to end, from data model to interface, and keeps zero-to-one products moving at week-one speed.
Our own AI delivery system, inside every engagement.
The Co-Pilot generates and prototypes within the client's own design language and governance constraints. It is why a direction agreed on Monday can be a working screen by Thursday.
Proof, not promises.
Three shapes of engagement.
Discovery sprint.
A short, defined first engagement to scope the actual problem. A written direction, and a recommendation on whether and how to proceed. Where ambiguous briefs become workable ones.
Project delivery.
A scoped delivery against a clear brief. A small dedicated team, milestones agreed at the start. Most of our product engagements sit here.
Embedded partner.
For platforms with a long horizon. We work alongside an in-house team on retainer, attend the meetings, hold roadmap reviews, ship in their tools.