
Vivek Khandelwal
Vivek Khandelwal is the Chief Business Officer at CogniSwitch, where he leads go-to-market strategy, enterprise partnerships, and the company's thought leadership programs. He is the author of Signal, CogniSwitch's weekly newsletter that translates the complex machinery of enterprise AI infrastructure into clear, actionable intelligence for practitioners and executives in regulated industries.
With a background in chemistry (M.Sc., IIT Bombay) and a career spanning enterprise technology strategy and AI product development, Vivek brings a methodical, evidence-based approach to understanding how AI systems fail and how they can be made more reliable. His writing focuses on the structural reasons why enterprise AI projects stall — poor knowledge quality, missing evaluation frameworks, and the gap between what LLMs promise and what regulated deployments require.
At CogniSwitch, Vivek works closely with customers in healthcare, financial services, and life sciences to identify where in an AI deployment trust breaks down. This work informs his editorial focus: knowledge quality, governance frameworks, context engineering, and the evaluation methodologies that separate audit-ready AI from AI that only looks compliant on a slide deck.
He writes about neuro-symbolic AI not as an academic concept but as an operational constraint — the specific class of problems where probabilistic reasoning alone is insufficient, and where formal verification of AI outputs becomes a competitive advantage. Before CogniSwitch, Vivek worked in enterprise technology across strategy and business development roles.
Articles by Vivek Khandelwal
11 articlesThe Handover Problem
AI agents are in production. Soon they'll start talking to each other. The question is what happens to context at the handoff — and who's accountable when it drops.
Read ArticleGarbage In, Garbage Out: The AI Knowledge Quality Problem
Everyone gets the concept of Garbage In and Garbage Out. What's difficult is clearly filtering - what's garbage and what's not.
Read ArticlePhantom Human-In-The-Loop
Every enterprise AI pitch ends with "Don't worry, we have a human in the loop." But human-in-the-loops today feel like performance theater.
Read ArticleAI Guardrails: What the Status Quo Gets Wrong
Everyone claims having guardrails for their agents yet pilots get blocked. Read why AI guardrails need to focus on SOP enforcement and not just content safety.
Read ArticleEvals Are NOT Audits
Evals are being looked at as the missing piece in the AI stack. For most part, they are. But to close the feedback loop, you need a more deterministic output.
Read ArticleOntologies: What They Are, Why They Matter Now
The spotlight is on ontologies, semantic layers, and taxonomies. These are familiar to data practitioners but for the rest — overwhelming. A plain-language breakdown.
Read ArticleNeuro-Symbolic AI: A Practitioner's Taxonomy
Five architectures. Six dimensions. A simple framework that starts with your workflow — not vendor pitch decks.
Read ArticleContext Graphs: From AI Pilot to Production
Context graphs capture what people actually do. But without reconciling with SOPs and policy, you're routing around the problem — not fixing it.
Read ArticleCogniSwitch: An Ontology-Governed Approach to Enterprise AI
We are building CogniSwitch for organizations where inconsistent AI gets you fired - or fined. This is how we approached the problem of deterministic, auditable enterprise AI.
Read ArticleThe End of Deploy and Pray
After 3 years of ChatGPT, Gartner analysts, investors and most importantly, enterprises are still asking the question they should have asked earlier: when does it all get real?
Read ArticleIntelligence Migration is NOT Possible
Can you export not just your data, but the intelligence layer - all the learned relationships, orchestration logic, and reasoning patterns?
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