Dhruv Shah
AI Specialist Automating Workflows & Building AI Solutions
New to Contra
Dhruv is ready for their next project!
Hybrid Semantic Search Engine
Keyword search misses results that mean the same thing in different words. Pure vector search misses exact matches.
I built a hybrid search engine that combines both. Text is embedded with the all-MiniLM-L6-v2 sentence model and indexed in FAISS, and each query blends vector similarity with fuzzy text matching (weighted 70/30), so results match on meaning and on exact terms.
Built as a FastAPI backend with a React frontend, packaged with Docker so it runs with one command.
What this shows: I understand how retrieval works under the hood, the same retrieval that powers RAG chatbots, and where it fails.
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AI Coding Agent & Voice Model Evaluation
Ongoing paid work evaluating frontier AI models for an AI training platform.
For coding agents, I work through the same real-world bug in a codebase with two different agents, then score each on six quality dimensions plus a head-to-head comparison: correctness, reasoning, tool use, and where each one went wrong.
For voice models, I rate AI narration quality against detailed rubrics and review other evaluators' work for accuracy and consistency.
What this shows: I know how leading AI agents actually fail on real tasks, and I can evaluate them rigorously and consistently, which is exactly what teams need before shipping an AI feature.
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OpenTruth: Independent Verification Protocol
Claims made by AI systems and organisations are hard to check, and the evidence behind them can be quietly changed after the fact.
I built OpenTruth, an independent verification protocol that links each claim to its supporting evidence in a hash-protected evidence graph. Because every piece of evidence is hashed, any later tampering becomes detectable, and anyone can trace a claim back to exactly what supports it.
What this shows: I can design trust and verification systems end to end, from the data model to the integrity guarantees.
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Sentinel: MCP Security Guard Benchmark
Problem: MCP guard tools claim to block attacks on AI agents, but there was no consistent way to hold them accountable.
What I built: A benchmark with a sandboxed testing harness that runs guard tools against 6 classes of attacks in a controlled environment, so their protection can be measured and compared fairly.
Skills: AI security, MCP, adversarial testing, Python, sandboxing, benchmarking
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