Product development
Build, Operate, Transfer.
End-to-end product engineering with a structured handover model. We build, operate, and then transfer full ownership to your team.
Learn moreProduct Engineering for Enterprise-Grade AI Systems
Build AI that ships.
We help you operationalize AI—connecting your data, building intelligence layers, and shipping LLM-powered features that work at scale.
The production gap
AI is easy to demo. Hard to ship.
Most teams have already built an AI prototype. What they don't have is a system that runs reliably at scale that handles real data, real users, real cost constraints, and real latency targets.
That’s where we come in. We work alongside your team to architect and build the system—delivering production-grade software that’s ready to run from day one.
We focus on outcomes your team can maintain after handoff.
What we deliver
What we build
Eight core practices. One engineering standard.
AI-Native Products
LLMs, agents, RAG - in production.
Beyond ChatGPT wrappers. Retrieval, evaluation, fine-tuning, and guardrails built to survive real usage.
Learn moreData Engineering
Pipelines that don't break at 3am.
ETL/ELT, streaming, CDC, data quality. Kafka, Airflow, dbt, ClickHouse built for scale and observability.
Learn moreData Platform
Your data, unified and queryable.
Data lakes, warehouses, and lakehouses. Governed, documented, and ready for analytics + ML.
Learn moreInfrastructure Optimization
Faster systems. Lower bills.
Profile, tune, and rearchitect cloud + data infra. Typical outcomes: 40-70% cost reduction, 3-10x throughput gains.
Learn moreProduct Analytics
Know what users actually do.
Event tracking, funnel analysis, cohort metrics, experimentation platforms. From instrumentation to insight.
Learn moreSDK Development
APIs your developers will love.
Multi-language SDKs, type-safe clients, clean docs. Android, iOS, web, and backend libraries.
Learn moreEdge Computing
Inference where your users are.
On-device ML, edge runtimes, CDN workers. Low latency, offline capable, privacy-preserving.
Learn moreCase studies
Selected work
Enterprise AI / SaaS
Scalable Data Infrastructure for Enterprise AI
Unified data sources · ↓ latency · Real-time analytics
Read case studyTax Technology / Financial Services
Full Business Audit & Technology Roadmap for a US Tax Filing Platform
2.5x draft capacity target · 37% to ~10% rejection target
Read case study
Hearing Aid Technology / Healthcare
Full Platform Re-Engineering for India's Leading Hearing Care Platform
Zero downtime migration · Full feature parity · ↑ velocity
Read case studyTech stack
The stack we actually ship with
Not driven by buzzwords—these are the tools we rely on in every engagement, selected for what consistently works in production.
Frontend
React ecosystems for complex apps, Next.js for production web, vanilla HTML/CSS/JS when simpler wins.
Backend
Services, APIs, and the logic that powers everything else. Chosen per problem, not per fashion.
Databases
Analytical and transactional stores. We match the database to the workload.
Infrastructure
The plumbing that keeps data moving and systems running.
Mobile
Native where performance matters, hybrid where ship-speed matters.
Don't see your stack? We've picked things up in a week before. Start a conversation.
Engagement flow
How an engagement runs
01
Assess
1 week
We audit your current state, talk to your team, identify the highest-leverage intervention. You get a written plan, scoped and priced.
02
Build
6-12 weeks
We embed with your engineers. Code goes into your repo. Architecture decisions get documented. You see progress weekly.
03
Hand-off
2 weeks
Full knowledge transfer. Runbooks, observability dashboards, and a trained internal team. We stay available for follow-up as needed.
Client perspective
What our teams say
"We had AI experiments that never made it to users. They helped us turn it into a real product—stable, fast, and fully integrated into our workflows."
— Rajaneesh, CEO, ANTAR IOT
Ready to build
Have an AI problem worth solving?
Tell us what you're building. We'll tell you honestly whether we can help — and if we can't, who can.