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About
Most firms selling AI have never had to keep one running. We built a multi-tenant AI product of our own, put it in front of paying customers, and have been on the wrong end of its incidents. Everything we now do for clients came out of that.
What we own
The clearest thing we can tell you about how we build is that we already run something ourselves, at scale, with money attached.
GenAI Ranker is our product. It measures how six AI engines describe a brand against its named competitors: multi-tenant, row-level tenant isolation, orchestration across six model providers, live billing, paying customers, and real incident nights.
Everything we sell came out of operating it. Eval suites exist because a model update broke something for us before it broke something for a client. Cost ceilings exist because we have watched a token bill behave badly. Refusal design exists because a confident wrong answer is worse than no answer, and we found that out on our own product rather than on yours.
It is also why we can run AI search visibility for clients at almost no marginal cost, and why the first scan is free rather than a lead magnet with a price behind it.
Most firms selling AI can show you a prototype. We can open a production system in the meeting and let you click around it.
How we build
Any competent team can produce a demonstration. These six decide whether it is still accurate, affordable and trusted eighteen months later.
Before a system speaks to a customer or writes to a financial record, it is tested against a labelled set drawn from your own data, with a pass threshold agreed in writing. You get a measured figure every month afterwards, not an assurance.
Every build ships with an eval suite. When a model changes underneath us — and providers deprecate on their own schedule — we re-run it, see exactly what moved, and migrate deliberately. Without that suite you find out from a complaint.
A system that says it does not know is worth more than one that guesses convincingly. Retrieval cites its source; anything ungrounded is refused and escalated. We test that behaviour first, because it is the one that protects you.
Input, retrieved context, model and version, guardrails applied, human approval. That record is as much the deliverable as the software, and it is what gets a system through a risk review rather than stuck in one.
We model cost per transaction before writing code — caching, routing, right-sized models, hard ceilings. Model and infrastructure costs are billed at cost against a ceiling you approve. A margin on tokens would corrupt every one of those decisions.
Approval gates and escalation paths scaled to what an error actually costs, not to how the demonstration looks. Where being wrong is expensive, a person signs off, and the sign-off is recorded.
Why us
GenAI Ranker is our product: multi-tenant, six model providers orchestrated, row-level tenant isolation, live billing and real incident nights. Most firms selling AI can show you a prototype. We can show you a production system we own, operate and are paged for — and you can watch us open it in the meeting.
Our own delivery runs on agentic tooling, which is why an agent is live in days rather than a quarter. The speed does not come out of the quality budget: eval suites, regression gates, tracing, guardrails and cost ceilings are still there, because that is what makes it survive contact with real users.
We cost the current process before proposing a system, and we can defend the build to a finance committee as well as to an architecture review. Most AI vendors are fluent in one of those conversations. The one your board will actually hold is usually the other.
Billed at cost against a ceiling you approve. A margin on tokens would corrupt every architectural decision we make on your behalf — model choice, caching, routing, context size, all of it.
A platform nobody arrives at and an agent nobody talks to are worth nothing. We run the search, content and campaigns that feed what we build, instrumented end to end — so the system and the demand for it are designed together.
Monitoring, retraining, eval regressions, model migrations and cost control after launch. Most firms hand over and vanish, right before the model underneath gets deprecated. That gap is the whole reason clients stay with us.
Due diligence
Everything below is on the GST certificate and in our terms. If you are about to send money to a firm you met in a LinkedIn message, check it — of us and of everybody else.
The boring, checkable part
Gurugram, Haryana, India
Wyoming, United States
US clients contract with the LLC and pay a US bank account domestically. Everyone else contracts with the Indian entity. One team either way — the engineers, the work and your data are in India, and the LLC is a contracting convenience rather than a second office.
And two products you can open
Full registered addresses are on our terms and on every invoice. Anything else your procurement needs — W-9, GST certificate, incorporation papers — email ravi@geeksnomads.com and it comes back the same day.
The name
It describes the two halves of the job. The geek builds the system: precise, tested, instrumented, cheap to run. The nomad goes where the work is and adapts when the ground moves — which in this field is roughly every quarter.
The mark is two open brackets rotating around a node. Violet is build, orange is run. They never close, because the system is never finished.
On every engagement, the people who write the plan are in the delivery, and are reachable when it matters. That is the model, not a favour we extend to large accounts.