we found the hackathon you actually want to win this month 🏆

hey builder 👋

you’ve been asking us to drop more hackathons that are:

✅ actually winnable (not just “big tech” flex fests)
✅ teach you something real
✅ don’t require a PhD to enter
✅ have actual cash prizes

this one is all four. no cap.

deadline: May 5, 2026
format: 100% online
team size: 1–5 (solo okay)
prize pool: $600 cash + certs + TigerGraph recognition
beginner friendly: yes, for real this time

🔗 register here → unstop.com/o/97bGeFT

let’s be real - LLMs are insanely powerful. but they’re also:

• slow on complex questions

• expensive (burning thousands of tokens per query at scale = 💸)

• dumb about relationships between things (they read text, not structure)

GraphRAG fixes this.

instead of dumping a 10,000-token document into a model and hoping it figures things out, you let a graph database (TigerGraph) do the heavy lifting first - it maps entities, follows relationships, does multi-hop reasoning - and then gives the LLM only the filtered, structured context it actually needs.

result? faster answers. fewer tokens. lower cost. better accuracy.

this is the tech powering the next generation of production AI systems. knowing how to build it is a serious competitive edge.

your job is to build two parallel pipelines answering the same question, then prove one is better:

Pipeline 1 → The Baseline
plain LLM. send prompt → get answer. simple, but token-hungry and slow.

Pipeline 2 → GraphRAG
prompt → TigerGraph (entity extraction, multi-hop reasoning, relevant context) → LLM → answer. faster, cheaper, smarter.

The Comparison Dashboard
a live scoreboard tracking tokens used, response time, cost per query, and accuracy. you’re not just building it - you’re proving it works with numbers.

the architecture they want is four clean layers:

1. Graph Layer → TigerGraph does entity + relationship work

2. Inference Orchestration Layer → decides when to use graph vs raw LLM

3. LLM Layer → generates the final answer from filtered context

4. Evaluation Layer → runs the benchmarks, feeds the dashboard

clean. production-ready. the kind of architecture that gets you hired.

🥇 1st Place → $250 cash + certificate

🥈 1st Runner-Up → $150 cash + certificate

🥉 2nd Runner-Up → $100 cash + certificate

🌟 Community Leads’ Exclusive → $100 cash + certificate

🏅 Top 10 Teams → Appreciation Certificate

📝 May 5 — Registration deadline

🏗️ May 1 → May 12 — Build & Submit phase

📢 May 12 — Top 10 teams announced

🧑‍🏫 May 13–14 — 1:1 Mentoring with TigerGraph experts (Top 10 only)

🎤 May 15 — Live demo to judges

perfect. neither have most people entering this.

here’s what you actually need:

• know Python ✅

• played with LLMs or APIs before ✅

• willing to read some docs ✅

that’s it. TigerGraph will provide documentation and starter resources. Top 10 teams get 1:1 mentoring sessions with TigerGraph experts on May 13–14 before the final presentation.

this is legitimately one of the most beginner-accessible hackathons we’ve seen from a legit database company. don’t sleep on it.

we’ve been watching the GraphRAG space blow up for a while now.

every serious AI engineering team is moving toward retrieval systems that understand structure, not just text. RAG alone is becoming table stakes. GraphRAG is what separates the builders who know production AI from the ones who just prompt ChatGPT.

this hackathon forces you to actually benchmark your system. not just “it worked on my laptop” - real numbers, real comparison, real proof. that’s the kind of portfolio piece that gets you noticed.

and on top of that: fully online, globally open, free to enter, $600 prize pool, TigerGraph recognition on your resume.

the risk-reward math here is obvious.

a few things that’ll separate good submissions from great ones:

1. make your dashboard the star
the comparison dashboard is where judges will spend the most time. make it beautiful. make the numbers obvious. make it impossible to ignore how much better GraphRAG performs.

2. pick a domain that makes graph reasoning shine
graphs are insanely good at things like: knowledge bases, legal docs, medical literature, anything with interconnected entities. don’t just do generic Q&A - pick a domain where multi-hop reasoning matters.

3. the evaluation layer is often ignored
everyone builds the pipelines. the teams that win nail the evaluation - clear accuracy metrics, cost per query, latency benchmarks. do this right and you’re in Top 10.

4. clean architecture > clever hacks
they explicitly ask for the “AI Factory” model with four separate layers. follow this. judges love production-ready structure.

seriously. takes 2 minutes. deadline is May 5.

🔗 unstop.com/o/97bGeFT

solo or team of up to 5. cross-institutional teams welcome. completely free.

and if you want to team up with other Builder Base people - drop in the WhatsApp and find your squad. we’ve got builders from Delhi, Kolkata, Bhopal, Indore and everywhere in between. let’s get Builder Base teams in the Top 10. 🫡

• GraphRAG hackathon by TigerGraph

• $600 prize pool, fully online, beginner friendly

• build two parallel pipelines (plain LLM vs GraphRAG) + comparison dashboard

• deadline: May 5 to register, May 12 to submit

• Top 10 get mentoring + live finals

• register → unstop.com/o/97bGeFT

• Join Hackathon Participants Group - Whatsapp

let’s build. 🔨

- Builder Base 🏗️

Builder Base is India’s Web3 & AI builder community - 14,000+ builders, chapters across India, and growing. if someone forwarded you this, subscribe below and join the fam.