AI engineer
Hi, I'm Raj. I build AI systems that people actually use.
For five years I've built AI for enterprises, for startups, and for myself. Some of it runs today inside organisations with thousands of employees, across hundreds of thousands of documents. Some of it is open source, and I stream the building on YouTube.
- 5 years
- building AI systems
- 2M+
- reads on my write-ups
- 94 hrs
- of building on YouTube
- 10
- projects shipped
Work
Systems that went into production.
Click any of these for the story behind it: what the problem was, and what it changed.
Deep Field
liveSeventy years of NASA research, searchable by just asking. You get an answer with the exact page it came from, and the original diagrams alongside it. I built it partly because it's genuinely useful, and partly because it's the honest way to prove a private system works. Point it at documents everyone can already see.
Read moreEnterpriseIntraplex
in productionLaw firms, hospitals, defence suppliers and offshore operators mostly can't use AI at all, because their files aren't allowed to leave their network, and that rules out every hosted assistant. Intraplex runs on open models inside their own perimeter: ask across everything they own, get cited answers and real documents back, with the internet physically unplugged if that's the requirement. About an eighth of what the cloud equivalents cost.
Read moreCurrent projectJaro
in buildJaro is lifecycle governance for HR Tech digital transformation, built for the professional services organisations that run SAP, Workday and Oracle HCM programmes. It comes out of WhiteSpot, an agentic AI engineering company in Melbourne whose argument is that in consequential work, faster isn’t the win. Governable is. I’m consulting with the team on it.
Read moreProduct · betaIntraParse
shippedThe hosted way to use Meridian. Drop in a PDF or call the API and you get back tidy, structured data. No GPU, no infrastructure, no queue. Built for developers who want the parsing without running the pipeline. On a 69-page scanned NASA report it finished in 59 seconds against LlamaParse’s 15+ minutes, and returned all 69 pages where LlamaParse managed 8.
Read moreOpen sourceMeridian
open sourceScans, photocopies, tables that fall apart, diagrams and equations that get skipped. Meridian reads them anyway. Layout detection, then a vision model writes out every table, figure and formula it finds, then it all becomes searchable. Measured at up to 118 pages a minute on a single H200, and it runs the whole stack on that one card. Free and open source.
Read moreYC S25Perfectly
shippedSmall startups have the hardest recruiting problem and nobody spare to work on it. Perfectly puts agents on the sourcing and outreach so a recruiter only handles the conversations. I built the first working version back when it was called Hire, and the team raised on it and got into Y Combinator.
Read moreAlso worth a look
Archival voice cloning
Building a usable voice from 1,240 hours of 1970s cassette tape.
Private research system
Advised G42 on a private AI research system, with their risk leadership in the room.
Airstrip AI
A proactive legal agent that researches case law and drafts contracts to human standard.
HyperTribe
An AI that helps musicians work out their next move.
WhooCrew matching
Matching 7,000+ event crew to jobs automatically.
Approach
How I build systems.
Before I build anything I go and learn the world the work lives in. How the people who do it actually spend a day, how they talk about it, who they check with, what they reach for first, which tools they trust and which ones they quietly work around. Rocket scientists and legal teams have almost nothing in common except that both have a way of thinking you cannot get from a requirements document. You have to go and watch.
That's the difference between an agent that answers and one that behaves like the expert would. I'm after the second kind: systems with some judgement in them, that carry the instincts of someone who has done the work for years.
The way I get there is usually by building less into the system, not more. The models already hold a great deal of domain knowledge. The job is to draw that out rather than bury it under rules I invented. So rather than scripting every step, I keep the system lightly opinionated and tune it so the model's own instinct for the domain comes forward, shaping it as the work runs.
Enterprises and startups want opposite things from that. Enterprise work is about scale, reliability and cost: systems that hold up across a whole organisation and don't get more expensive every month they run. Startups are the other kind of interesting: doing something that genuinely hasn't been done, cracking a problem that used to need conventional software, and changing what the product itself can be. That's why I keep taking both.
On the record
Emergent Ventures grantee
Mercatus Center, George Mason University
Tyler Cowen's grant programme for early, ambitious ideas.
Built the first version of a YC S25 startup
Perfectly
It was called Hire then. The team raised on that build and got into Y Combinator.
Advised G42 on a private AI research system
G42 · national-scale AI group, UAE
Worked with their enterprise risk and assurance leadership, so the governance requirements shaped the architecture from the first week rather than turning up as a review at the end.
Writing
Across 28 posts and 6 communities. I write up the builds in full. What worked, what cost more than expected, and what broke. It's been the most reliable way I've found of meeting people worth working with.
r/AI_Agents
Building RAG systems at enterprise scale (20K+ docs): lessons from 10+ enterprise implementations
The one that travelled. Posted to six communities and read about 794,000 times in total. Ten enterprise builds' worth of what actually matters, on documents that had been sitting in SharePoint since 2005.
r/LLMDevs
I Built RAG Systems for Enterprises (20K+ Docs). Here’s the learning path I wish I had (complete guide)
My most-read post. The route from toy demos to systems people depend on: what to learn, in what order, and which skills actually carry weight on a real deployment.
r/LLMDevs
I built RAG for a rocket research company: 125K docs (1970s-present), vision models for rocket diagrams. Lessons from the technical challenges
Fifty years of propulsion research, air-gapped on the client's own hardware, where half the critical knowledge lived in schematics and equations that ordinary search ignores completely.
r/Rag
I made 60K+ building RAG projects in 3 months. Here's exactly how I did it (technical + business breakdown)
The business half, written openly: pricing, how the clients came in, and why project values moved from $3–5K to $15K once the work was genuinely production-ready.
r/AI_Agents
I Built 10+ Multi-Agent Systems at Enterprise Scale (20k docs). Here's What Everyone Gets Wrong.
The hard part isn't the agents, it's the orchestration. Keeping them from tripping over each other, capping cost when any one of them can call the API forever, and recovering when one dies mid-run.
r/LLMDevs
I built RAG for 10K+ NASA docs (1950s–present) in 2 weeks: VLMs for complex tables, diagrams & formulas, 657K+ pages on a single H100, live-streamed full build.
The build behind Deep Field. 657,000 pages processed on one graphics card, an agent that investigates rather than searches, and the whole thing streamed live.
Build log
I built the NASA search engine live on YouTube.
10,000 NASA documents, from an empty folder to a working system. 94 hours across 15 parts, unedited, including the days it didn't work.
Part 1: Rocket schematics and more: starting the build
7 h 08 m · the first session, from nothing
Formula extraction and visual anchoring
Part 3 · 11 h 05 m
Agents that think like engineers
Part 5 · 11 h 55 m
The rewrite: 28 days down to 2.5
Part 9 · 5 h 38 m
About
A bit about me.
I'm fascinated by space, and by how little any of us really understand about the universe we're sitting in. Given the chance I'll talk to rocket scientists, researchers and philosophers for hours, which is probably how I ended up building a search engine over seventy years of NASA research.
Music is the other constant. I sing, I play a few instruments, and I travel whenever there's time.
I love Interstellar and still haven't made peace with what it says about time. Happy to keep hanging in.
What I'm looking for
Consulting on systems that have to work at scale, and on agents that take real work off a business rather than shaving minutes off it. The ones I want most are the ones nobody has built yet.
And someone to partner with on the market side. I'll build it. If you have the access and the relationships, that's the half I don't have.
Say hello