Podcast

ContextOps

How to build the context layer that gets enterprise AI to production.

Most enterprise AI teams have cycled through the same fixes. Better prompts. Bigger context windows. Newer models. The agents fail in the same ways.

If none of that has moved the needle, that's your cue to look further upstream — at the knowledge the agent is actually running on. Who assembled it? Is it current? Does anyone own it?

The operators who have taken enterprise AI to production — not just demos, actual production — are the ones who asked that question early. ContextOps is a podcast about how they did it.

First episode: May 2026
01 — The Show

The Show

A fortnightly conversation between Vivek Khandelwal and Dilip Ittyera. Occasionally joined by practitioners and researchers doing real work in this space. We're trying to think through what it actually takes to make enterprise AI reliable at a knowledge level — not model benchmarks, not prompt tips.

02 — Why Now

Every generation gets its ops function.

DevOps
~2008
DataOps
~2015
MLOps
~2018
ContextOps
202?
We are here now

DevOps, DataOps, MLOps. Each promoted a function from afterthought to infrastructure. ContextOps is next. The gap is named. The discipline is forming.

03 — The Problem

Why AI keeps failing the same way

One person wrote the prompts. Someone else sourced the documents. A third team loaded them into the pipeline. The developer building the agent assumed the context was clean. Nobody checked. When the agent failed, everyone blamed the model.

Prompt Engineer
Content Team
Data Team
Developer
Agent
Wrong answer
Blame →Model

Assembled by many hands. Owned by none.

04 — The Discipline

What ContextOps actually is

ContextOps operationalizes knowledge for AI. It's the loop that turns documents, SOPs, and policies into something an agent can reason over: reliably, traceably, and without contradiction. Ingest. Validate. Structure. Serve. Audit. Refine. Knowledge flows in. Feedback flows back.

ContextOps Loop
Ingest
Validate
Structure
Serve
Audit
Refine

ContextOps operationalizes knowledge for AI.

05 — The Hosts

Dilip Ittyera

Co-Founder & CEO, CogniSwitch

Started building AI systems in the 1980s — expert systems, before the AI winter. Lived through two cycles of hype and reset, each time with a clearer read on what the technology actually requires. Founded three companies, served as CTO four times, including at Zensar where his work became a Harvard Business School case study. Founded CogniSwitch in 2022 because the current wave was repeating the same structural mistakes he had seen before.

Vivek Khandelwal

Co-Founder & Chief Business Officer, CogniSwitch

IIT Bombay grad. Previously built iZooto from scratch into a market-leading marketing automation platform. Spent two years in enterprise AI sales watching teams blame the model for failures that were actually upstream — in how their knowledge was assembled, governed, and served. Co-founded CogniSwitch to build the infrastructure layer that was missing.

No one has written this playbook yet.

We're working through it one episode at a time. If you're figuring it out too, come build it with us.