Pulkit Verma
AI Engineer | RAG, AI Agents & Next.js AI SaaS MVPs
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sensei: AI code review that learns your team's style.
An open-source CLI (on PyPI) I built that learns how your team reviews code and posts inline, confidence-routed comments on GitLab merge requests: high-confidence issues get flagged, low-confidence ones stay quiet. Paired with mr-principle-review-agent (npm), which enforces team-specific coding principles on every MR. Together they save ~5 hours/week of manual review.
Stack: Python, LLM APIs, GitLab CI, agentic workflows.
Code: github.com/pulkit004/sensei
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WeaveAI Anchor: answers grounded in your own documents.
A document-grounded retrieval and Q&A product I built at WeaveAI. It answers only from approved documents, cites the source for every answer, and refuses when it isn't sure instead of hallucinating. Built for internal docs assistants, support knowledge copilots and product documentation search.
Stack: RAG, vector DB, retrieval evals, Next.js, LLM APIs.
Try it: weaveai.dev/products/anchor
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Enterprise RAG: support resolution from days to minutes.
I architected an end-to-end RAG pipeline over a large internal document base so solutions and merchant-facing teams get contextual answers instantly. Paired with an AI insights engine that generates automated analytics summaries, change detection and risk flags, replacing manual analyst reporting cycles.
Impact: serves teams supporting 3,000+ merchants; resolution time cut from days to minutes.
Stack: RAG, LangChain, AWS Bedrock, vector DB, React, Node.js.
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WeaveAI Cite: get cited in AI Overviews.
An autonomous content engine I built at WeaveAI for AI search visibility (GEO/AEO). It finds the questions buyers ask ChatGPT, Perplexity and Google AI Overviews, writes answer-first articles grounded in your product, and auto-publishes them to your site.
Stack: multi-step LLM agents, Next.js, scheduled pipelines.
Try it: weaveai.dev/products/cite
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