AP

    AKHIL PAVULURI

    Applied AI systems engineering

    Akhil
    Pavuluri

    I build systems that have to be correct under real conditions — Printish is the production proof.

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    Focus Areas

    Reasoning · execution · monitoring

    Deterministic reasoning

    Structure decides, language only expresses intent

    Decision graphs and semantic layers turn requests into explicit paths instead of soft guesses.

    Execution systems

    Execution is validated before anything moves

    Typed plans, validators, and replayable runs replace model-led side effects.

    Failure and drift intelligence

    Failures stay visible before they spread

    Causal attribution and drift monitoring make degradation inspectable while it is still actionable.

    Philosophy

    Good AI systems should be inspectable

    The systems I trust are constrained on purpose: explicit contracts, visible failure paths, and enough structure to argue with.

    I care less about model theater and more about whether the stack stays legible when production stops being polite.

    Selected Work

    Featured systems, pulled forward

    01 / 03Causal inference · Execution graphs · Root cause
    Causal AI System

    TraceAI

    Walks pipelines backward to find what actually caused a failure

    Walks pipelines backward to find what broke. Initial ranking failed because undamped merge aggregation let the wrong node win — fixed with edge weighting and fan-in damping.

    Causal graphsLineageFastAPIPostgreSQL

    Why it stands out

    Failure pressure

    Incomplete instrumentation makes the graph sparse; attribution degrades to coarse buckets until edges exist.

    Trade-off

    Chose explainable graph math over deep learning attribution—better auditability, less expressive on unstructured text.

    Open case study
    02 / 03Shipped product · Non-AI · Full-stack
    Production system (non-AI)

    Printish

    Print ordering platform — customer checkout, order tracking, and an admin dispatch view. Built and launched solo.

    Customers upload artwork, pay, and track delivery in one clear flow. Admins manage dispatch, catch stuck jobs early, and keep updates honest when vendor or courier signals go stale. Designed, built, and launched solo.

    TypeScriptNext.jsFirebaseRazorpay

    Why it stands out

    Failure pressure

    Payment provider callbacks and retries can duplicate delivery—dedupe on provider ids and idempotent handlers.

    Trade-off

    Favored explicit states and boring CRUD over clever automation early—easier to debug under real traffic.

    Open case study
    03 / 03Deterministic analytics · Semantic layer · SQL
    Deterministic Analytics

    DecisionGraph

    Schema-adaptive analytics without hallucinated SQL

    Replaces brittle BI glue with a governed semantic graph: questions map to validated SQL, not model improvisation.

    Semantic layerSQL generationGovernancePostgreSQL

    Why it stands out

    Failure pressure

    Under-specified business terms map to the wrong metric ID—mitigated with explicit confirmation for ambiguous matches.

    Trade-off

    More upfront modeling work than a vanilla text-to-SQL toy; pays off in correctness and trust.

    Open case study
    Let's Connect

    Open to roles where system reliability matters more than demo polish.

    Available for remote and relocatable positions in systems and AI engineering.