Abhinav Bohra

I help early-stage founders turn AI ideas into systems that can actually work in production. Most founders do not need “more AI.” They need clarity on what to build, what not to build, and how to avoid fragile AI systems that look good in demos but fail with real users. I can help you with: 1. Reviewing your AI/LLM/RAG architecture 2. Debugging why your LLM app breaks in production 3. Designing reliable AI agents and agentic loops 4. Improving RAG retrieval quality 5. Choosing between OpenAI, Claude, Gemini, open-source models, fine-tuning, embeddings, or classic ML 6. Designing search, ranking, personalization, or recommendation systems 7. Reducing LLM latency and inference cost 8. Building model evaluation, observability, and guardrails 9. Turning vague AI ideas into a practical MVP roadmap 10. Understanding what data you need before building the model 11. Avoiding overengineering and expensive architecture mistakes Just book a free call with me and lets chat, you've got nothing to lose :)

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  Artificial intelligence

  I help founders build AI systems that work beyond the demo. I can review LLM/RAG architecture, AI agents, agentic loops, tool-calling workflows, model choice, hallucination issues, prompt reliability, evals, guardrails, latency, cost, and production failure modes. Good fit if your AI app breaks with real users, your RAG retrieves weak context, your agent loops or calls the wrong tool, or you are unsure whether to use APIs, open-source models, fine-tuning, embeddings, or simpler ML.

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  Data science

  Define metrics, build evaluation frameworks, design A/B tests, identify data gaps, separate signal from noise, and decide what should be measured before scaling. If you have user/product/model data but are unsure what it means, whether your AI product is improving, which KPI matters, or how to create a practical decision framework from incomplete data.

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  Product analytics

  For AI products, analytics should not stop at clicks or usage. You need to know whether users trust the output, where the AI fails, where users drop off, which workflows create repeat usage, and whether the model is driving real value. I can help define KPIs for activation, retention, AI answer quality, failure rates, adoption, feature ROI, and product-market fit signals.

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  Product launches

  I help founders launch AI products with the right technical and product assumptions. Before launch, I can help review MVP scope, AI reliability risks, evaluation plans, user feedback loops, launch metrics, and what should stay manual versus automated. Best fit if you are preparing to ship an AI MVP and need to know what can break, what to measure, what to cut, and how to avoid launching a fragile product that looks good only in demos.

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  AWS (Amazon Web Services)

  8 years of experience

  I use AWS for scalable data, ML, and AI infrastructure. I can help founders think through practical cloud architecture for AI products: storage, compute, batch jobs, APIs, model serving, cost tradeoffs, monitoring, and when not to overbuild. Useful if your AI product needs to move from prototype to a more stable backend.

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  Docker

  8 years of experience

  I use Docker to package Python, ML, and AI services so they can run consistently across local, staging, and production environments. I can help founders understand deployment basics, dependency isolation, reproducibility, containerized model services, and why many AI prototypes break when moved outside a notebook.

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  MySQL

  10 years of experience

  I use MySQL/SQL for analytics, data validation, feature generation, experimentation, and ML data preparation. I can help founders understand their product data, define useful metrics, debug data-quality issues, and avoid building AI/ML systems on top of messy or unreliable data.

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  Tableau

  10 years of experience

  Experience using Tableau-style dashboards for business reporting, product metrics, executive visibility, and operational tracking. I can help founders think through what should be measured, how to structure dashboards, and how to connect analytics to real decisions rather than vanity reporting.

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  Machine Learning

  I help startups design practical ML systems for search, ranking, recommendations, classification, forecasting, personalization, and model evaluation. I can help founders understand what model to build, what data is needed, how to measure quality, and how to avoid overengineering.

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  E-Commerce

  My strongest production background is in e-commerce and advertising systems. I can help founders with product search, recommendations, ranking, personalization, catalog understanding, pricing intelligence, relevance quality, and AI systems that improve product discovery.

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  Artificial Intelligence

  I help startups build AI products that work beyond demos. My focus is LLMs, RAG, AI agents, hallucination reduction, model evaluation, production failure modes, cost/latency tradeoffs, and deciding when AI is actually needed versus when a simpler system is better.

• https://hackernoon.com/why-modern-search-systems-use-both-dual-encoders-and-cross-encoders
• https://hackernoon.com/evaluating-rag-systems-why-answer-quality-is-not-enough
• https://www.preprints.org/manuscript/202511.0069
• https://arxiv.org/pdf/2302.03297
• https://arxiv.org/pdf/2601.10119