An AI-native agency for the companies building AI.
We help enterprises navigate AI change management, AI transformation, and their data & content needs — built and run by operators who ship.
Four practices.
One operating philosophy.
AI Transformation Consulting
We work with enterprises and traditional businesses on the messy middle: workflow audits, build-vs-buy calls, agent rollouts, and the org changes that actually make AI stick.
Data Procurement & Licensing
Indian and multilingual datasets, RLHF and preference data, evaluation sets, and bespoke collection. Sourced ethically, structured for training, delivered with full provenance.
AI-Native Content & Media
Developer marketing, product launches, technical content, and social for AI companies. We run the same content engine on ourselves — you’re looking at proof of work.
Data Labeling & Annotation
Curated annotation pods for vision, speech, multilingual NLP, and domain-specific tasks across fintech, healthcare, and legal. Quality over headcount.
Three reasons
partners keep coming back.
AI-first,
top to bottom.
Our own systems, content engines, and internal workflows run on AI. We don’t sell what we don’t use. Every recommendation comes battle-tested on our own ops.
A network
of networks.
We plug you into the AI ecosystem — frontier labs, infra companies, founders, annotators, regulators, and the operators actually shipping. The right introduction is often more valuable than the work itself.
Operators,
not consultants.
We’re a team of operators who’ve shipped high-velocity products — across fintech, consumer, and AI infrastructure. We’ve been the buyer, the builder, and the one debugging at 2am. Now we’re the partner.
Sharp AI insights,
delivered daily.
Our public-facing content engine. The same stack we build for clients runs on us first — curating, summarising, and shipping a daily signal from the AI frontier. Free to read. Use it as proof of work.
OpenHive is solving the wrong problem — and the right problem is much harder
Shared solution stores for agents sound great until you realize the hard part isn't storage, it's solution quality decay. A fix for numpy 1.24 breaks on 1.26. OpenHive has recency scoring, which helps, but the fundamental issue is that agent-generated solutions have no ground truth validation. You're building a shared hallucination database as fast as a shared knowledge base.
The real value of tiny-vLLM isn't the engine — it's what it proves about vLLM's overhead
Everyone will look at tiny-vLLM as a deployment option. That's the wrong take. Its value is as a existence proof: a functional inference engine in ~1000 lines of C++/CUDA. The delta between its performance and vLLM's is a direct measure of what Python, Pydantic, and abstraction layers cost you. Run the benchmark.
Docker in Vercel Sandbox isn't about containers — it's about agents that can validate their own deployments
The obvious use case is 'run Redis in a sandbox.' The non-obvious use case: an agent that builds a container, validates it runs correctly, and only then triggers deployment — all without human-in-the-loop infrastructure access. FUSE and VPN support extends this further. Vercel is quietly building the substrate for autonomous deployment agents.
A small, senior team. By design.
Deepdrop began as an AI-native content platform — proof, on the public internet, that small operators can run modern media stacks. The Daily Drop is still live, still shipping. Today we partner with the companies building the future of AI on the work most agencies can’t do: data, technical content, and transformation that actually ships.