Are you in Gurugram or Noida?

No local office listing. We work with Delhi NCR teams remotely from our studio, on IST, with a written proposal.

Do you work with larger organisations?

Yes, when the brief is specific. We will not staff a theatre of roles to look like a body shop.

What happens after launch?

Handoff notes, environments, and optional maintenance. Support is scoped — not implied by the city page.

Will you guarantee model accuracy?

No. We design for measurement, fallbacks, and human review where stakes are high — not magical guarantees.

Can this sit on our existing stack?

Usually yes. We prefer integrating into your product and data boundaries over inventing a parallel system.

Do you only use one model vendor?

No. We pick for the job, your data boundaries, and cost. Vendor lock-in is a design choice we will name, not a default.

What if we just need a chatbot?

We will ask what the chatbot is for. If it is a FAQ wrapper, we may recommend something simpler. If it is a workflow with tools and retrieval, we will build that — scoped.

Quote after a written brief. We write a scoped proposal before build starts — no invented fixed quotes for open-ended work.

AI & Automation · Delhi NCR, India

AI & Automation for teams in Delhi NCR

Delhi NCR mixes enterprise process with startup speed, often in the same week. We take briefs that name the workflow, the risk, and the deadline — then we build a slice that survives a real review. Kodees Labs is not a Gurugram captive centre. We are an India-based studio working with NCR teams on shared hours. Model-backed features and workflows that sit inside real products — with evals, failure modes, and ownership considered.

Delhi NCR is IST. Working-day overlap with Gurugram, Noida, and Delhi teams is the default.

Automation that earns its keep on a defined workflow — measured, owned, and reversible.

Scoped project or pilot · typically 6–12 weeksQuote after a written brief

Start a Project See how it works

OverviewScoped project or pilot · typically 6–12 weeks

Automation that earns its keep on a defined workflow — measured, owned, and reversible.

Problem

Demos are cheap. Production AI is not a prompt file.

Teams ship chat UIs without retrieval strategy, cost controls, or a path when the model is wrong. A weekend prototype is not an operating system for untrusted output.

Without evals and fallbacks, “AI features” become a support queue with extra latency. Operators stop trusting the product — and they should.

Solution

Automate the workflow — constrain the model.

We embed AI where it earns its keep: clear inputs, observable outputs, human fallbacks, and product surfaces operators trust. The model is a component, not the architecture.

Cost, latency, and failure modes are design inputs. If a workflow does not need a model, we will say so and automate it without the theatre.

Fit

Know if this is the right practice.

Honest filters save both sides weeks. If the problem does not look like this, we will say so before you buy a track that will not land.

Ideal for

Product teams who need a model feature inside an existing app — not a standalone chatbot landing page
Operators drowning in repetitive workflows that have clear inputs and a human fallback
Founders who want retrieval, tools, and evals — not a prompt pasted into production
Teams who will measure quality instead of demanding a magical accuracy guarantee

Not a fit when

Undirected “add AI somewhere” without a workflow to change
Guaranteed model accuracy or revenue outcomes
Replacing judgment where humans must stay accountable
Black-box vendors you cannot observe or roll back

How it works

The path from brief to handoff.

5 stages for this practice — still anchored in the studio path from Discover through Improve.

Name the workflow

Inputs, outputs, stakes, and whether a model belongs here at all. Some automations should stay deterministic.

Constrain the system

Retrieval, tools, permissions, and fallbacks. The model does not get unconstrained access to your data or your users.

Build the production path

Integrated into your product and data boundaries — not a parallel demo environment that never ships.

Measure and fallback

Evals, logging, and a human path when the model is wrong or expensive.

Operate

Notes for prompts, tools, and cost so the feature does not rot the week after launch.

What you get

Handoff you can own. Change you can feel.

Deliverables

Integrated automation or model feature for the agreed workflow
Operating notes for prompts, tools, and failure paths
Instrumentation hooks for quality and cost signals
Human fallback or review path where stakes require it
Eval baseline so “it feels smarter” is not the only test

Outcomes

A model-backed path operators can trust because failure is designed, not hoped away
Automation that sits in the product you already run — not a notebook on someone’s laptop
Visibility into quality and cost so the feature can be governed after launch