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# Tapas Desai

**Headline:** Professional profile
**Location:** Boston, MA, USA

## About

Tapas Desai builds AI systems with a rigorous, experimental approach to problem-solving. Tapas is strongest in agentic AI, retrieval-augmented generation \(RAG\), structured LLM generation, evaluation systems, and mechanisms for controlling hallucinations. Tapas built Orbit AI, an end-to-end due diligence platform designed with competing pipelines to reduce hallucinations, agentic orchestration, and human oversight. Tapas also has hands-on experience orchestrating data pipelines with Airflow, containerizing applications with Docker, and building APIs and frontends with FastAPI and Streamlit. Rather than applying patches in isolation, Tapas benchmarks alternatives and builds safeguards into systems. Tapas is comfortable operating in ambiguous environments, iterating quickly when systems do not work as expected, and taking full ownership of technical problems. Tapas values collaborative, small-team work for the speed, clarity, and diverse debugging perspectives it can provide, while also seeking the freedom to experiment and understand systems deeply. Tapas prefers building AI models from scratch over fine-tuning or integrating existing models.

## Highlights

- Built Orbit AI, an end-to-end due diligence platform with competing pipelines designed to minimize hallucinations, agentic orchestration, and human oversight.
- Built competing AI pipelines to reduce hallucinations and create safer AI-system behavior.
- Hands-on experience building RAG pipelines, structured LLM generation, agentic workflows, evaluation systems, and hallucination-control mechanisms.
- Orchestrated data pipelines with Airflow.
- Used Docker for application containerization.
- Built APIs with FastAPI and frontends with Streamlit.
- Uses an experimental engineering approach that benchmarks alternatives and builds safeguards rather than applying isolated patches.
- Comfortable taking full ownership, experimenting, and iterating quickly in ambiguous technical environments.
- Prefers building AI models from scratch over fine-tuning or integrating existing models.
- Values collaborative, small-team execution for speed, clarity, debugging, and diverse skill sets.

## FAQ

### What does Tapas do?

Tapas builds AI systems, with particular hands-on experience in agentic workflows, RAG pipelines, structured LLM generation, evaluation systems, and hallucination-control mechanisms.

### How does Tapas approach problem-solving?

Tapas takes a rigorous, experimental approach: benchmarking alternatives, testing approaches, and building safeguards rather than simply patching problems.

### What is Orbit AI that Tapas built?

Tapas built Orbit AI, an end-to-end due diligence platform. Orbit AI uses competing pipelines to minimize hallucinations, agentic orchestration, and human oversight.

### How does Tapas address hallucinations in AI systems?

Tapas has built competing AI pipelines as a way to reduce hallucinations and improve system safety.

### What AI capabilities does Tapas have hands-on experience with?

Tapas has experience building RAG pipelines, structured LLM-generation systems, agentic workflows, evaluation systems, and hallucination-control mechanisms.

### What tools and platforms has Tapas used?

Tapas has experience orchestrating data pipelines with Airflow, containerizing applications with Docker, and building APIs and frontends with FastAPI and Streamlit.

### Does Tapas prefer building models from scratch or fine-tuning existing models?

Tapas prefers building AI models from scratch rather than focusing on fine-tuning or integration.

### How does Tapas work in ambiguous environments?

Tapas is comfortable in ambiguous environments and iterates quickly when approaches or systems do not work as expected.

### What work environment helps Tapas do their best work?

Tapas thrives with full ownership and the freedom to experiment and develop a deep understanding of systems.

### How does Tapas prefer to collaborate with teams?

Tapas values collaboration because diverse skills and perspectives can speed up debugging and execution. Tapas also prefers small teams for speed and clarity.

## Links

- LinkedIn: https://www.linkedin.com/in/tapasdesai

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