AI Conf 2026

Talk details

What does it take to build a production-ready AI system that processes 120,000+ complex documents at around $2 per document? This talk shares lessons from building an AI engine that digitizes and structures unstructured clinical trial data at enterprise scale.

We'll explore how the team preserved layout and document structure, detected and extracted tricky tables (nested, merged cells, domain-specific formatting), and used chain-of-thought reasoning with citation tracking to keep outputs verifiable in a regulated environment.

You'll also hear the operational realities of running high-throughput inference on AWS Bedrock, balancing RPM/TPM, accuracy vs. speed trade-offs, and choosing between custom and foundation models after spending over half a million dollars on research and infrastructure.

Talk details

An LLM call alone is just noise without a system that’s secure, observable, and built with intent. Too many products rely on bare-bone prompting and hope for magic. This talk shows how to blend creativity with engineering discipline so AI earns trust, not just attention.

Learn how to design LLM systems that think, feel, and behave responsibly by design—clear guardrails, telemetry that catches drift, and UX that sets expectations. Sayantika shares patterns for aligning model behavior with product intent while keeping humans-in-the-loop and governance intact.

Talk details

Briefly speaking, let's look at the current landscape of programming languages and imagine what a next-generation language designed for both humans and machines would look like.

The software development landscape is undergoing a seismic shift. Three years after ChatGPT's debut, AI coding assistants have moved from novelty to necessity. But here's the paradox: while AI can write code in any language, the choice of language dramatically affects the quality, cost, and reliability of AI-generated code. This talk raises a fundamental question: What characteristics define the perfect programming language for the AI era?

In this talk, we'll explore the evolution of programming languages in the age of AI: how expressiveness, safety, and performance converge to form a new paradigm of communication between humans and intelligent systems. We'll compare modern statically and dynamically typed languages such as Rust, Python, Go, and Ruby, and draw lessons from their strengths and limitations. We'll discuss the economics of "cheap tokens" versus "expensive correctness," how compilers become feedback systems for agents, and why traits, macros, and extensibility matter more than ever.