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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.

Discovery plus a focused build slice — often a few weeks for a first production path when the workflow is clear.

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

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AI and automation product workflow

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.

Who this is for

A fit when the problem looks like this.

  • 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

Capabilities

What we deliver in this practice.

  • Workflow automation and agent-style orchestration as scoped
  • Model feature design inside existing products
  • Retrieval and tool-calling patterns where they actually help
  • Eval and failure-handling baselines
  • Cost and latency awareness in the architecture
  • Human review queues for high-stakes outputs
  • Instrumentation hooks for quality and spend signals

Process

How this service actually runs.

  1. 01

    Name the workflow

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

  2. 02

    Constrain the system

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

  3. 03

    Build the production path

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

  4. 04

    Measure and fallback

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

  5. 05

    Operate

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

Every serious build still follows the studio path — Discover through Improve. See the full studio process.

Technology

Stack we typically reach for here.

  • Python
  • TensorFlow
  • Node.js
  • PostgreSQL
  • React
  • Docker

Deliverables

What you leave with.

  • 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

What this is meant to change.

  • 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

Teams in Delhi NCR

Who this page is for.

  • Enterprise-adjacent product and internal platforms
  • Startups selling into regulated or process-heavy buyers
  • Operators consolidating tools after a messy first build

The practice itself is unchanged. Read the AI & Automation page for the full offering without a location overlay.

Selected work

Product work from this practice.

Related services

Practices that often sit beside this one.

Timeline

How long this usually takes.

Discovery plus a focused build slice — often a few weeks for a first production path when the workflow is clear.

Starting price

Honest commercial footing.

Quote after a written brief

Scope drives the number. You get a written proposal before build starts — not a surprise invoice after.

This practice typically sits in our Products pricing lane — still a scoped proposal, not a menu quote.

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Also serving

Same practice, other places we work with teams.

These pages are for teams in that place. They are not local office listings.

FAQ

Common questions for this service.

Ready to talk through this service?

Start a project conversation — or email us with the problem you are trying to solve.

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