Bengaluru, India · AI engineer
Available for freelance engagements
Agents and RAG systems, the self-hosted LLM infrastructure they run on, and the observability that keeps all of it reliable. I work with founders and teams to take AI systems from first prototype to production.
Selected work
A selection of systems I've designed and shipped — agents, retrieval pipelines, LLM infrastructure, and the observability that keeps them reliable in production.
Always-on personal assistants built on OpenClaw — running on your own hardware and reachable through the chat apps you already use: WhatsApp, Telegram, Slack, and more. Pluggable models, custom skills and tools, and full data ownership by default.
An observability layer purpose-built for AI agents — agent-level traces, token-cost views, and failure-mode analysis (loops, tool errors, dead-end runs) on a ClickHouse-backed telemetry pipeline.
A production retrieval pipeline powering enterprise document Q&A — embeddings, vector search, and metadata persistence, with answers that cite their sources.
An LLM assistant that analyses live alert streams and suggests probable root causes, integrated into incident workflows. Reduced mean time to resolution by roughly 25%.
A conversational interface over operational databases — LangChain with function calling translates plain-English questions into metrics queries, no hand-written SQL required.
Open-source models on GPU infrastructure — vLLM and Ollama behind a LiteLLM gateway, reverse-proxied, containerised, and fully monitored end to end.
An autonomous research system — CrewAI agents orchestrating parallel web research, source ranking, and synthesis into concise, decision-ready briefs.
A fully offline question-answering system built for low-connectivity environments — on-device language models tuned to run within tight hardware constraints.
Services
Available for fixed-scope builds and ongoing engineering engagements. Every project ships with tests, documentation, and the dashboards to prove it works in production.
Scoped agents that do one job reliably — tool use, memory, evaluation criteria, and guardrails included. From feasibility assessment to a deployed, monitored system.
A private, always-on assistant that lives in your chat apps — built on OpenClaw, tuned to your workflows, and extended with custom skills and tools. Self-hosted on your hardware, so your messages, memory, and credentials never leave it.
Open-source models running on your GPUs — serving, routing, deployment, and CI/CD — so your data stays on your infrastructure and your costs stay predictable.
The internal tools that quietly save hours: natural-language SQL over your data, document assistants that cite their sources, and troubleshooting copilots on your alert streams.
Agent-level tracing, token-cost dashboards, and failure-mode analysis for AI systems in production — from instrumentation through to the dashboards.
Not everything I take on is a build — if you need a second opinion on an AI decision, or want your team brought up to speed, say so when you write.
Every system I ship ends the same way —
with a dashboard proving it works.
Knowledge sharing
I document what I learn building AI systems — long-form video, engineering write-ups, and short-form explainers. All of it public.
Talks and walkthroughs on agents, self-hosted LLMs, and the infrastructure underneath them — recorded as I build.
WritingLonger-form engineering notes: architecture decisions, post-mortems, and how-I-built-it breakdowns from the systems above.
InstagramClips and quick explainers — the short-form version of the same ideas, for a faster read.
About
I'm Praneeth V P, an AI engineer based in Bengaluru. I've spent my career close to production — large-scale enterprise systems, monitoring, and incident response — and I bring that discipline to every AI system I build.
I run open-source models on my own GPU infrastructure and ship product work under InsightX Labs — from agents and retrieval systems to personal assistants.
Away from the terminal, I'm usually out with a camera looking for birds.
Tell me what you're trying to build. I'll reply with an honest assessment — feasibility, the right approach, and what it would take to ship it.