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AI, web & app development studio · Available for new projects

We build the AI that ships.

Kalpstack designs and builds AI agents, AI chatbots, websites and mobile apps, and the data platforms underneath them. Strategy to production, one team, working with clients worldwide from Jaipur, India.

AGENT · TRACElive
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Planning2 tools selected
Retrievingorders.march · 1,284 rows
Calling toolshipping.status()
Verifyinggrounded in 3 sources
1.42s$0.00313 citationsgrounded

10+

Products shipped end to end

6

Industries, from spiritual tech to privacy infrastructure

3

AI systems running in production

1

Team owning strategy through deployment

AI, specifically

Most AI demos never reach production. Ours are built to live there.

The gap between a convincing prototype and a system a business can depend on is evaluation, retrieval quality, cost control and failure handling. That gap is the work — and it is what we do.

Your systemsCRM · Database · APIsKnowledgeDocs · Tickets · ReviewsToolsSearch · Write · NotifyGrounded answerWith citationsAction takenWritten backAudit logEvery step, replayableGUARDRAILS · EVALS · SPEND LIMITSAgentPlanRetrieveActVerifyretry on low confidence
Every hop is traced, evaluated and bounded by a cost and latency budget — which is the difference between a demo and something you can put in front of customers.

p95 < 2s

Latency budget enforced per agent step

100%

Answers carry a citation or an explicit "not found"

Every PR

Evaluation suite runs before anything merges

Grounded, not guessing

Retrieval-augmented answers cite the passage they came from. When the system does not know, it says so instead of inventing.

Measured, not vibes

Evaluation suites run on every change. We track answer quality, latency and cost per request the same way we track uptime.

Bounded, not loose

Tool permissions, spend limits and human approval on consequential actions. An agent should never be able to surprise you.

How we work

Four phases. No surprises in any of them.

01

Understand

We start with the constraint, not the technology. Questions, data access and honest scoping — including telling you which parts do not need AI at all.

02

Prototype

A working slice in front of you fast. Real data, real latency, real cost per request — so the decision to scale is made on evidence rather than a deck.

03

Engineer

Production build: typed interfaces, evaluation suites, observability, CI/CD and infrastructure as code. The boring parts are what keep it running at 3am.

04

Operate

We stay on after launch — monitoring quality drift, tuning cost, shipping iterations, and handing over documentation clear enough that you could take it in-house.

Agent orchestrationTool use & MCPRAG pipelinesVector searchFine-tuningEvals & guardrailsBigQueryGA4Power BIAirflowReactNode.jsMongoDBNext.jsPythonPyTorchAWSGCPDockerWebRTCSolidityZero-knowledge proofsAgent orchestrationTool use & MCPRAG pipelinesVector searchFine-tuningEvals & guardrailsBigQueryGA4Power BIAirflowReactNode.jsMongoDBNext.jsPythonPyTorchAWSGCPDockerWebRTCSolidityZero-knowledge proofs

Start here

Tell us what you are trying to build.

One call, no deck. We will tell you what we would build, what it would cost, and which parts you should not build at all.