Earley AI Podcast - Episode 95: Contract Intelligence, Context Engineering, and Building AI That Scales with Deepak Bapat

Why Making Complex Revenue Simple at Scale Requires More Than Throwing Contracts Into a Chat Interface

Guest: Deepak Bapat, Co-Founder and CTO at Tabs

Host: Seth Earley, CEO at Earley Information Science

Published on: July 30, 2026

In this episode, Seth Earley speaks with Deepak Bapat, Co-Founder and CTO at Tabs, a revenue and accounts receivable management platform built for B2B companies. They explore why dropping contracts into a general-purpose AI tool is not a strategy for enterprise scale, what generative AI unlocked that OCR and legacy machine learning could never solve, why context engineering beat fine-tuning for contract extraction, and why newer and larger models are not always better for specialized tasks. Deepak shares candid and specific insights on building atomic AI pipelines, the provability requirement that financial compliance demands, and what finance and data leaders consistently underestimate before deploying AI on their contracts.

Key Takeaways:

• Dropping contracts into a chat interface is a reasonable experiment but not an enterprise strategy - doing things at scale requires specific tooling, specific expertise, and integration across systems.
• The SaaSpocalypse framing misses the point - the more interesting question is not whether chat replaces UI, but how platforms can understand intent and preempt the actions users would otherwise have to click through manually.
• Generative AI solved the contract problem by reasoning over ambiguous natural language at document level - something OCR and rules-based systems fundamentally could not do.
• Context engineering beat fine-tuning at Tabs because merchant preferences vary so significantly that fine-tuning per merchant became cost-prohibitive - a well-prompted generalized model proved faster and more elastic.
• Newer and larger models are not always better for specialized tasks - Deepak's eval sets show that models from six months ago outperform newer versions on certain contract extraction jobs, likely due to overfitting on coding.
• Provability is the non-negotiable requirement in financial AI - it is not enough to produce correct output, you must be able to prove the output is correct and traceable back to the source contract.
• Organizations that want to deploy AI on their contracts first need to standardize internally on what outcomes they actually want - two people on the same team asking the same question about the same contract should not produce two different answers.

Insightful Quotes:

"The misconception is that difficult problems can just be solved by throwing something into ChatGPT and having the answer come out the other side. In our case, the at-scale piece is everything. Those intelligence tools are still individualized tools - to do things at scale for an entire enterprise still takes specific tooling, specific thought, and specific expertise." - Deepak Bapat

"What we're trying to do is move from a place of unstructured data to provable and correct structured data. That is what Tabs is built around - and that is what most of these other systems simply cannot handle." - Deepak Bapat

"When you think about the legacy players that were more rigid SaaS tools with manual entry and brittle connectors - what was intractable about that model is exactly what generative AI made solvable. The ability to reason over the words in a document, understand what they meant, and understand what the output should be - that changed everything." - Seth Earley

Tune in to discover what it actually takes to build AI that is accurate enough, auditable enough, and elastic enough to handle enterprise revenue data at scale - and what most organizations underestimate before they start.

Links

LinkedIn: https://www.linkedin.com/in/deepakbapat/

Website: https://www.tabs.inc

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