The prefrontal cortex
for generative AI.
Agentic systems that reason causally, not just respond. Built at the intersection of frontier research and production-scale engineering.
An intelligence OS.
Not an AI wrapper.
Three functions, one operating layer, built for decisions that cannot afford to be wrong.
Prototype lab
From research hypothesis to working system. Vaish.ai incubates research prototypes engineered to the reliability and scale demanded by production infrastructure.
Research studio
Active research into causal reasoning architectures, reinforcement learning for agentic decision loops, and model reliability, backed by peer-reviewed work spanning three continents.
Intelligence OS
Vaish.ai is the umbrella runtime. ARCH is its reasoning engine, CORTEX its reliability suite, CART its adversarial layer. Together, an intelligence stack for high-stakes environments.
Wrappers pipe.
An operating system governs.
A wrapper is a passive conduit: zero friction, zero validation, the enterprise fully exposed to the model's baseline hallucinations and security flaws. An intelligence OS manages, regulates, and secures the interaction — necessary friction before, during, and after generation.
Direct pipes to a model. Whatever the model hallucinates, the enterprise ships; whatever the prompt smuggles in, the model executes. The wrapper adds nothing but branding.
An intermediary layer that manages cognitive resources. Reasoning, validation, and security checks run before, during, and after generation — friction where friction protects.
ARCH
Autonomous Reasoning & Causal Hierarchy
A metacognitive executive layer for frozen models. A learned controller plans over an explicit structural causal model, and no conclusion commits without passing a counterfactual consistency gate. The base model is never retrained.
Climbing the ladder of causation.
Standard LLMs are trapped on the first rung — advanced statistics engines, pattern-matching over what they have seen. ARCH is built to operate on the third, where a conclusion must survive the question: what if we had acted differently?
Seeing patterns.
P(y | x)
Simple pattern matching over historical data. This is where standard LLMs operate — sophisticated correlation, mistaken for understanding.
"What do past loan defaults look like when interest rates are 5%?"
Predicting actions.
P(y | do(x))
Mapping direct action-to-reaction pathways: what changes in the world if we act, not just what co-occurred in the record.
"What happens to our default rate if we change the interest rate to 4.5% tomorrow?"
Rewinding the world.
P(yx' | x, y)
Retrospective simulation — isolating one variable against historical constants. This is the domain of ARCH's counterfactual gate.
"Given that defaults were 3% at a 5% rate, what would have happened if we had lowered rates last quarter?"
The counterfactual gate · eliminating statistical guesswork
Parse and flag.
The incoming query is parsed and flagged as a Level-3 counterfactual — "what if we had acted differently?" — not a lookup.
Halt the raw prompt.
The gate stops the prompt before it reaches the model, preventing a standard semantic guess dressed up as analysis.
Enforce the graph.
The prompt is rewritten against a strict causal graph — rates → affordability → default risk — holding history constant while modifying only the isolated variable.
Answer on rails.
The model processes the query exclusively through the constrained causal pathway, returning mechanically logical output — or the gate abstains.
CORTEX
Model Reliability Evaluation Suite
An open evaluation harness that measures reliability, not capability: the same tasks re-run under drift, tool degradation, and adversarial content, scored as a degradation profile instead of a single number. ARCH is the first test subject.
CART
Continuous Adversarial Red Teaming
A standing adversarial agent for regulated AI. One engine attacks a deployed system continuously and writes every finding into an audit-ready record — built for the Three Lines of Defense structure that governs AI in financial services.
Point-in-time audits vs a standing immune system
Point-in-time.
Traditional penetration testing builds static walls — a security posture dated the day the test ran.
Continuous.
A standing adversarial agent — an active immune system that never signs off and never stops probing.
Vulnerable on discovery.
Exposed the moment a new jailbreak is published anywhere in the world; the report doesn't know it exists.
Tracks the threat landscape.
Constantly runs automated background attacks, folding in new edge cases as the threat landscape evolves.
A static sign-off.
The deliverable is a PDF that starts aging the moment it is stamped.
A living record.
Real-time updates to a dynamic reliability record — evidence that the system's logic holds up under pressure, on any given day.
One query through the stack.
What the modules do together: an enterprise counterfactual, validated at the boundary, reasoned causally, and stress-tested before the answer is allowed to commit.
"If we had lowered interest rates by 0.5% last quarter, would our loan default rate have stayed under 2%?"
VALIDATE
Verifies the user's permissions for financial data and sanitises the prompt for injection patterns — before anything touches a model.
REASON
Detects a Level-3 counterfactual. Structures the rates → affordability → default-risk causal graph, isolates the 0.5% variable against held-constant history, and runs the conclusion through the consistency gate.
VERIFY
In the background, standing adversarial attacks (CART) confirm the applied causal logic matches a stress-tested reliability profile (CORTEX) for financial modelling.
A mechanically logical, policy-adherent, structurally sound resolution — with the reasoning on the record.
Vaishnavi Yeruva.
Published researcher. Production engineer. Eight-plus years building AI systems at the scale of hundreds of millions of users.
The Architect · Vaish.ai
Research lineage
- 01
M.S. Artificial Intelligence
Northwestern University · Evanston, IL, USA
- 02
Research · Speech & Signal Processing
Indian Institute of Science (IISc), Bangalore. Collaboration with GE Healthcare: 66% reduction in system processing time.
- 03
6 Peer-Reviewed Publications & Talks
INTERSPEECH · Speaker Odyssey · ICANN · IEEE INDICON · NCC
- 04
Speaker · Grace Hopper Celebration India
Associative Memory Frameworks for Speech Recognition, 2016
Engineering pedigree
- 01
Senior AI Engineering Consultant
Architecting agentic LLM systems (multi-agent orchestration, MCP-integrated workflows, evaluation, guardrails, and observability) for Fortune 500 clients within the applied AI practice of a global Big Four consultancy.
- 02
Agentic SDLC Transformation
Re-engineered enterprise software delivery for a Fortune 500 financial services firm around agentic development patterns, cutting feature cycle time from one week to one day and tripling team capacity.
- 03
Autonomous Diagnostics at Internet Scale
Designed agentic root-cause-analysis systems for one of the world's largest consumer internet platforms, embedding expert investigative reasoning into semi-autonomous workflows spanning hundreds of millions of users.
- 04
Explainable AI for Healthcare
Predictive risk pipelines and XAI for clinical decision support: patient outcomes, health equity, and personalized preventive care.
Publications & talks.
Working on something that demands real AI reasoning?
Whether you're a researcher, founder, or hiring for frontier AI roles, let's talk. Research conversations and collaborations are always welcome.