name : Akhil Pavuluri
role : AI Engineer
focus : Deterministic AI Systems
status : Building things that hold up in prod
motto : "Why did it fail?" > "Did it fail?"I build AI systems where:
- outputs are grounded in real data
- behavior is observable and testable
- failures are explainable β not hidden
Language models are one component inside
a controlled system β not the system itself.
The goal is reliable behavior under real-world
conditions, not just a passing demo.
π TraceAICausal Execution Intelligence Deterministic causal tracing over execution graphs. Know why it failed β not just that it did. Root-cause paths from symptom to origin, built for production forensics. |
π DecisionGraphSchema-Adaptive Analytics Transforms raw relational data into deterministic KPIs via semantic modeling. No guessing β just structured derivation you can verify. |
βοΈ NormaGraphPolicy Intelligence via Decision Graphs Structured reasoning over regulatory and policy documents. Explainable retrieval β every answer has a traceable path through the policy graph. |
Local AI Execution Engine Β Natural language β validated execution pipelines over real tools. Not simulation β actual |
Design principles I actually follow
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β STRUCTURED REASONING β
β Explicit graphs, schemas, constraints, and tests β
β > opaque stacks β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£
β LANGUAGE AS INTERFACE β
β NLP as a control surface for complex systems β
β > open-ended chat endpoints β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£
β DETERMINISM WHERE IT MATTERS β
β Pipelines, evals, failure modes you can inspect β
β > probabilistic black boxes in prod β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£
β DEPLOYMENT-FIRST β
β Optimize for load, drift, partial observability β
β > happy-path demos that collapse in the wild β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Open to real-world problems where constraints are clear, iterations are measurable,
and the system has to hold up outside the lab.


