Frontier intelligenceis not the hard part.Running it inproduction is.
Every engagement delivers the same goal: a production AI system running in your own stack, owned by your team. We build it, deploy it, and train the people who run it.
How we work
From a use case to a system you own.
No open-ended retainers. Every engagement is scoped to a working system and a clean handover, measured against your own cases the whole way.
Scope
We start from your use case, not a platform. One call defines what working means and how it will be measured.
Build against evals
We build to an eval suite drawn from your real cases, so progress is a number, not a demo.
Deploy to the data
Cloud, on-prem, or air-gapped. The model runs where your data already sits.
Hand over the keys
You keep the model, the training data, and the code. No lock-in, nothing to renew.
What we do
The whole stack, one partner.
Four divisions, one engineering standard. We build the systems, deliver the hardware they run on, staff the teams that operate them, and teach you how all of it works.
Who builds it
Software we have built already runs where downtime is measured in dollars per second.
Himalayan Labs was founded by Varun Singh. The database software he built, ScaleArc, ran in production at NASDAQ, Dell, and Microsoft. We build production AI the same way that demands: to survive contact with a live queue, not just a demo.
The eval engine
Nothing ships without an eval.
Every capability is graded against your real use cases before it goes live, so accuracy is a number you can see, not a claim you have to trust.
In test mode, the actions your team takes become new evals, so the system keeps being measured after launch, not just before it. That is the difference between a demo and something you can trust with a live queue.
Measured, not asserted
Every deploy re-runs the eval suite against your real cases before it ships.
Agentic AI cost calculator
Know what it costs before you build it.
The fully-loaded cost of a production agent, a finished video, or an hour of transcription. Not the price of a token: the prompt and tools billed on every turn, the clips you render and discard, the voice on top. Priced across every model on the stack, so you can see where the money goes and choose accordingly.
- Pick a use case, then any model on the stack for each step.
- See which stage actually dominates the bill, not just the token price.
- Watch the number swing as you change models, then choose accordingly.
No sign-up. Rates verified against source.
Figures depend on the models you pick. Run yours.
Deploy
Hardware that ships with the models already loaded.
Five inference-ready boxes, from a desktop to an eight-GPU rack. An open LLM, real-time voice, and retrieval, pre-loaded and benchmarked, so it runs the moment it arrives. You own it.
gpt-oss · real-time voice · RAG — pre-loaded, guaranteed, yours
Entry ~₹6.5–8.5 L to Rack ~₹4.3–5.8 Cr, incl. GST
See all five configsThe second meaning of AI
AI answers like a person. It also builds like a team.
That second meaning is the one most people miss. The same models that let an agent understand a customer let a pair of engineers build what used to need a department. We work both sides, and the proof is our own software: three products, live in production, each built by a pair, give or take.
MoviePipe.ai
A live AI video pipeline: screenplay in, cinematic shots out. Many generative models behind one interface, real payments, real users.
ReduceWaste.ai
A material-nesting engine written in Rust, with a hand-written GPU kernel and trained placement models, shipped in 19 languages.
TestFlo.ai
One platform for end-to-end, load, API, and security testing, where teams usually stitch together five separate tools.
This is not a story about working harder. It is leverage, and it is teachable. It is the efficiency we get your team to →
The stack we build on
Vendor-neutral by design, from silicon to model.
We are not tied to one chip, one cloud, or one model. We choose the parts that fit your latency, budget, and data residency, and we know each one well enough to defend the choice.
Marks denote tools we build, deploy, and train on. They do not imply endorsement or partnership.
Ownership
You own the model, the data, and the code, so there is no vendor to renew with and no dependency to lose.
Playbooks
Take the thinking with you.
The same material we walk clients through, as PDFs. Leave an email and it downloads.
The build brief
How we scope and build a production agent, end to end.
AI training programs
The three programs, the MDAP framework, and what your team walks away able to do.
Two-day executive brief
The condensed decision guide for leaders: build versus buy, privacy, and real cost.
Insights
Notes from building production AI.
What holds up, what breaks, and the numbers behind it.
Launching RFSpace: An Interactive Learning Space for Radio Frequency Engineering
Inspired by the beautifully illustrated Soviet-era technical books that shaped a generation of Indian engineers, we're building frictionless learning tools for RF — free for everyone.
Introducing ReduceWaste.AI: Open-Source Nesting for Every Material
How our Rust-powered nesting platform cuts material waste by 15-30% across 19 languages and every major industry.
Comprehensive Testing with TestFlo.AI
E2E testing, load testing, API testing, scheduled runs, demo video recording, and site exploration — all in one platform.
Questions
What teams ask before the first call.
We already call the OpenAI and Anthropic APIs. Why do we need you?
The frontier model is the easy part. We build the system around it: the evals, the training-data pipeline, the deployment, and the ownership. You keep your choice of model. We make it survive production.
Can the model run where our data cannot leave?
Yes. We put inference hardware on-prem or fully air-gapped, sized to the workload. The model comes to the data, not the other way around.
What do we actually own at the end?
The model, the training data, and the code that runs them. There is no vendor to renew with and no dependency to lose.
How do you prove it works before real traffic?
Every capability is graded against an eval suite built from your real cases. Accuracy is a number you can see before anything ships.
How long does a first system take?
Weeks, not quarters. We scope to one use case and build against its evals, so there is a working, measured system early.
Who is behind this?
Himalayan Labs was founded by Varun Singh, whose software has run in production at NASDAQ, Dell, and Microsoft.
Book a call
Bring the use case. Leave with a plan.
Thirty minutes with an engineer who has shipped this before. Either of two of us can take it, so slots stay open.