AI Engineer · Phoenix, Arizona
I build retrieval systems that turn unstructured data into something production can depend on. Above is my own work, modelled as the kind of graph I build for a living.
01 Why
I finished my bachelor's in Hyderabad and moved to Arizona a few months later, in 2023.
I came for the master's at ASU, and I came to find out how somewhere else thinks — a different country, different people, a different set of defaults about how things get built and why they get built that way.
That instinct turned out to be most of the job. The useful part of retrieval work is rarely the model; it's asking what a system is actually being asked for, rather than what it was originally designed to answer.
The useful part is rarely the model.
02 Now
06/2025 – Present · Remote
I architected an enterprise information catalog unifying metadata across PostgreSQL and five other sources, and the ingestion, validation and indexing that keeps it fed.
100K+
Data assets
Discoverable and lineage-tracked through the catalog.
99%+
Pipeline uptime
Ingestion and indexing in production, behind CI/CD and monitoring.
The call that mattered
Vector retrieval is excellent at finding what a document says. Preserving how things relate to each other is a different problem: chunking optimises for semantic similarity, and a relationship spanning several chunks doesn't survive that split intact. Adding more context doesn't recover it, because the structure is gone before the model ever sees it.
Chunked for similarity
The relationship is lost in the split.
Modelled explicitly
The relationship is the data.
That's not a flaw in RAG so much as a question it was never shaped to answer. So for relational queries I stopped asking retrieval to infer connections and modelled them directly — entity and relationship extraction into Neo4j and Graphiti, running alongside the vector layer rather than replacing it.
Building a graph with correct relations costs a significant number of tokens up front: you pay once at ingestion instead of paying again at every query. It earns that back only if the system answers enough relational questions to justify it — which is exactly the bet worth checking before you make it.
~35%
Faster contextual search
After modelling relationships explicitly rather than inferring them.
03 Explore
Every node is a real entity from my work and every edge a relationship that actually exists. Drag to orbit, hover a node to read it, click to pin.
04 Built
A multi-agent system for supply chain resilience: real-time news analytics, geospatial risk scoring, and automated alerting. I designed the orchestration layer: state management, tool routing, memory, and the LLM decision loops.
~60%
Less manual monitoring
Agent orchestration replacing hand-watched supply-chain signals.
A lookup over the World Swing Dance Council's competitor registry. Type part of a name and it finds the dancer, then returns their points by division, how close they are to moving up, a chart of their competitions over time, and a comparison against other dancers on the same axis. Lookups take about a seventh of a second: the original asked the registry for a session token first, and nothing ever checked it.
10
Dependencies, down from 53
Twenty-eight Radix packages removed for components that are a few lines of markup.
A route planner that picks the pretty way instead of the quick one. Every drivable road in Arizona is scored for how close it runs to woodland, wilderness and water, then checked against the roads the state has officially designated scenic. Ask it for Phoenix to Sedona and it sends you over the Mogollon Rim rather than up the interstate, shows the climb that explains the extra two hours, and works out when to leave to arrive in golden hour.
931,043
Roads scored
Every drivable way in Arizona, rated against the state's own designated byways.
A small language that compiles to CHIP-8 machine code, written for the emulator below. Variables, sprites, routines, collisions, numbers and text: press compile and play it, with the real output shown as bytes under the screen. Meteors, a dodging game with a score, three lives and a proper ending, comes to 447 of them, down from 504 after the compiler learned to flip a skip instead of jumping, share one copy of wait, and print from a table. Every program was recorded frame by frame before each change and matched exactly after.
53
Bytes from a dozen lines
Every test compiles a program and runs it on the machine. Each example must also come out identical under the six instructions interpreters disagree about.
A virtual machine from 1977, rebuilt from its instruction set and running in a browser, with both of the extensions people later built on it: SUPER-CHIP's larger screen and scrolling, and XO-CHIP's second colour plane, sixty four kilobytes of memory and waveform sound. Every program in the community archive runs, and because none of them record how they are played, the page works out which keys each one watches by playing it. It can also read any program back as Octo source, telling code from sprites by walking every path the machine could take; all 104 listings compile back to the identical bytes through Octo's own compiler. It is also the machine Nibble compiles for.
86
Programs quietly running wrong
Six instructions have two accepted readings, and the archive records which each program expects. It had been ignoring that. It reads them now.
05 Foundations
2.5M+
Item repository
Physical and digital collection kept accurate and retrievable.
10/2023 – 05/2025
Mesa, AZ
Data & Operations Analyst
Arizona State University · Library Systems
05/2024 – 08/2024
Boston, MA
Software Engineering Intern · Interactive Media
Idori Inc.
Education
Arizona State University
M.S. Information Technology (STEM)
GPA 4.0 / 4.0 · May 2025
Neil Gogte Institute of Technology
B.E. Computer Science & Engineering
GPA 8.2 / 10 · Jun 2023
06 Contact
If you're building retrieval systems that have to hold up in production, I'd like to hear about it.
© 2026 Harianth Kalavala