We build the intelligence a business runs on, and the guardrails that keep it trustworthy.
AI as mechanism, not feature.
A feature decays. A mechanism compounds.
Most enterprise AI ships as a static generator, the same kind of output on day three hundred as on day one. We build the opposite: a closed learning loop underneath the work, monitoring what’s converting, what’s being approved, what’s being thrown away, and adjusting what gets generated next.
AI earns its place in a system or it doesn’t get shipped.
Our work tends to get hired into one of two rooms: a marketing team that needs to scale output without scaling headcount, or a product and compliance team that needs an analyst, an RM, or an investigator to move faster without losing the audit trail.
Removable, auditable, and cost-modelled.
Before the first prompt ships, not after. The client can pull the system out without rebuilding their stack, an auditor can read what it did and why, and the GPU bill is a design constraint from day one, not an afterthought.
The shape of the work.
Creative automation.
Generation systems that take segment, channel, and brand inputs and produce on-brand creative, copy, and layout at volume, trained on the client's own visual language.
→Agentic workflows.
Agents built into the product, not alongside it. They read context, surface decisions worth making, and stop at the line where a human approves.
→Co-pilots for operating teams.
For relationship managers, analysts, and investigators whose day is spent pulling the same numbers from the same places. The co-pilot pulls; the human judges.
→RAG & intelligence layers.
Retrieval, embedding, and reasoning over a client's own corpus, usually the foundation underneath a co-pilot or an agent.
→Closed learning loops.
Approval signal, conversion signal, override signal, all flowing back into what gets generated next. The system gets sharper the longer it runs.
→Evals & cost engineering.
The cost model and the eval harness built before a system ships, not after, because most enterprise AI fails when usage scales, not at the demo.
→Model selection & orchestration.
Picking the right model for the right step, and routing between them. Frontier where it matters, smaller and cheaper where it doesn't.
→The team you meet in week one is the team that ships in month nine.
Om Rajani
AI EngineerBuilds models, retrieval, and evaluation as infrastructure inside the product, governed, cost-modelled, and removable.
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.
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.
Notes on the practice.
AI as a mechanism, not a feature.
Most enterprise AI is still being built as a widget on a page. The harder, more useful question is what happens when AI stops being a feature and becomes the thing that lets a team scale without scaling cost.
Read the piece →The cost curve most pilots ignore.
Why most enterprise AI doesn't fail at the demo, and what changes when GPU spend, model selection, and the eval harness become first-class design constraints rather than after-the-fact engineering.
Read the piece →Closed loops, not static generators.
The structural difference between an AI feature that decays and an AI mechanism that compounds. What it takes to wire production signal back into generation, and why most systems skip the step.
Read the piece →Three shapes of engagement.
AI discovery.
A short, defined first engagement to scope where AI is worth building and where it isn't. A surface-area map, a highest-leverage intervention, and a sequenced roadmap rather than a vendor pitch.
Pilot to production.
A scoped delivery against a clear use case, a creative automation system, an agentic workflow, a co-pilot for an operating team. Built narrow, with the cost model and eval harness built in from the start.
Embedded partner.
For programmes with a long horizon. We work alongside an in-house team on retainer, hold the architecture reviews, and sequence the build as the proof comes in.