About NeevSemi

The Problem

The AI Rush That's Breaking Chip Design

The semiconductor industry is in the grip of an agentic AI gold rush. Every chip design team — from RTL generation to verification to DRC analysis — is under board-level pressure to "show AI wins," and fast. But speed without architecture is producing a generation of fragile, economically unsustainable workflows that are quietly breaking in production. The numbers are sobering: RAND Corporation's 2025 analysis of over 2,400 enterprise AI initiatives found that 80% fail to deliver intended business value, while S&P Global reports that 42% of companies scrapped most of their AI initiatives in 2025 — up from just 17% the year before.

The economics are particularly brutal for agentic systems. Gartner has documented that agentic AI workflows consume 5 to 30 times more tokens per task than standard interactions, turning a workflow that costs pennies in a demo into thousands of dollars per month at production scale. Yet most teams hardcode to a single LLM provider without benchmarking, cost guardrails, or any mechanism to adapt when better or cheaper models emerge — and in this market, they emerge daily.

The result is quick-fix prototypes shipped as production, with no closed-loop monitoring for model drift, API changes, or runaway costs. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027. In semiconductors, where IP sensitivity, time-to-tape-out, and design correctness carry existential stakes, getting agentic AI wrong isn't just expensive — it sets entire programs back.

Our Approach

Agents for the Agents

NeevSemi — "Neev" (नींव), Sanskrit for "foundation" — exists to solve precisely this gap. We don't build chip design agents. We build foundational infrastructure agents that make agentic AI workflows enterprise-ready: optimized, observable, and economically sustainable.

Our platform, NeevDisha, starts where most solutions skip — by systematically analyzing your use cases, selecting domain-specific benchmarks for each, and building validated interfaces to the agents driving those workflows. At its core sits an optimization engine that continuously matches LLM configurations to user-defined criteria across both performance and cost, ensuring you never pay frontier prices for commodity tasks. And because the LLM landscape shifts faster than any team can manually track, a closed-loop agent automatically re-evaluates, re-benchmarks, and reconfigures your agentic tool stack as models improve, costs change, or requirements evolve.

No manual re-tuning, no vendor lock-in, no silent failures.

With NeevSemi, semiconductor teams can operationalize agentic AI for true enterprise usage — scaling confidently from proof-of-concept to production without the brittle architectures and runaway costs that have derailed the industry's first wave of AI adoption.