Author Profile
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 regulated industries - 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.
14 articles
In 2019 we shipped a bot that read every Slack and Teams conversation and answered from it. People agreed that the problem it solved was real, and almost nobody paid for it. We realized later that the reason was trust, not retrieval quality.
Read ArticleModel bias hides in the model's reasoning, where cleaning your data and bolting on retrieval can't reach it. Locating it means taking the model out of the stages where it decides.
Read ArticleFour AI terms used interchangeably. Four different questions. At scale, citation stops being a verification tool and becomes a confidence signal. Here is what actually works.
Read ArticleAI 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 ArticleEveryone understands the concept of Garbage In and Garbage Out. What's difficult is clearly filtering - what's garbage and what's not.
Read ArticleEvery 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 ArticleEveryone 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 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 ArticleThe spotlight is on ontologies, semantic layers, and taxonomies. These are familiar to data practitioners but for the rest — overwhelming. A plain-language breakdown.
Read ArticleFive architectures. Six dimensions. A simple framework that starts with your workflow.
Read ArticleContext graphs capture what people actually do. But without reconciling with SOPs and policy, you're routing around the problem — not fixing it.
Read ArticleWe 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 ArticleAfter 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 ArticleCan you export not just your data, but the intelligence layer - all the learned relationships, orchestration logic, and reasoning patterns?
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