AI.
Applied AI infrastructure for regulated platforms.
AI as mechanism, not feature.
Most enterprise AI ships as astatic generator. It produces the same kind of output on day three hundred that it did on day one. We build the opposite: a closed learning loop sits underneath the work, monitoring what's converting, what's being approved, what's being thrown away, and adjusting what gets generated next.
Our work tends to be hired in one of two rooms. The marketing or operating team that needs to scale output without scaling cost. The product or compliance team that needs an analyst, an RM, or an investigator to be faster without losing the audit trail. AI earns its place in a system when the system is better with it than without — and removable, auditable, and cost-modelled before the first prompt is shipped.
A feature decays. A mechanism compounds.
Two Words Co-Pilot.
Our proprietary AI delivery system, used inside every engagement. It generates and prototypes within the client's own design language and governance constraints.
A handful of real rooms
to ship into.
The shape of the work.
Creative automation
Generation systems that take segment, channel, and brand inputs and produce on-brand creative 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. Designed so an auditor can read what the agent did, and why, after the fact.
Co-pilots for operating teams
For relationship managers, analysts, investigators, and the people whose day is spent pulling the same numbers from the same places. The co-pilot does the pulling. The human does the judgement.
RAG & intelligence layers
Retrieval, embedding, and reasoning over a client's own corpus. Usually the foundation underneath a co-pilot or an agent. Most of the work is in the retrieval, not the model.
Closed learning loops
The thing that turns a generator into a mechanism. Approval signal, conversion signal, override signal — all flow back into what gets generated next.
Evals & cost engineering
Most enterprise AI doesn't fail at the demo. It fails when usage scales and the GPU bill goes vertical. We build the cost model and the eval harness before the system ships, not after.
Integration contracts
The data contract that lets our systems talk to a client's stack without our team taking custody of customer data. JSON schema in, generated artefacts out.
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.
AI strategy & discovery
Where the question isn't can we build it but should we, and where. A short engagement that maps the surface area, names the highest-leverage point of intervention, and comes back with a sequenced roadmap rather than a vendor pitch.
Prototype to production
The bit most studios stop short of. Taking an AI prototype — ours or someone else's — through the work it takes to clear a real environment. Auth, observability, fallback behaviour, the boring scaffolding that decides whether a system can be relied on.
The shape of a system worth shipping.
From one campaign template
to a thousand.
One of India's largest fashion marketplaces sends millions of CRM communications every day. Until recently, almost every one of them was built from a single creative template— one tone, one offer, one composition pushed across a base segmented only by the broadest cuts. The internal teams understood the limitation. The bandwidth to fix it didn't exist.
A creative automation layer that sits alongside the client's CRM stack and produces personalised campaign variants on demand. The system takes segment, gender, affluence band, channel, and brand-guideline inputs, and generates the creative, the copy, and the layout to match — trained on the client's own visual language.
We don't take custody of customer data. The client defines the contract, our system reads from it, generates the variants, and writes back to their pipeline. Underneath, a closed learning loop monitors what's converting and adjusts what gets generated next. The output isn't static; the system gets sharper with every cycle.
The engagement is live and at proof-of-concept stage. The integration contract is defined, a working pipeline is in place, and the path to scale — from creative automation into segmentation, predictive intelligence, and orchestration — is mapped against the client's internal roadmap. The case for further detail will be made when there's shipped data to point at.
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. Surface-area map, highest-leverage intervention, integration shape, cost model. A sequenced roadmap rather than a vendor pitch.
Pilot to production.
Scoped delivery against a clear use case. A creative automation system, an agentic workflow, a co-pilot for an operating team. Built narrow, shipped to a defined surface, with the cost model and the 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, sequence the build from creative automation into segmentation, prediction, and orchestration as the proof comes in.