Co-founder and CEO at Bito. 

We build codebase intelligence for AI coding and reviews, so AI tools can reason about real systems instead of guessing. 

Before Bito, I built and scaled companies across multiple markets, including PubMatic (NASDAQ: PUBM). 

Right now, I spend most of my time thinking about AI, software systems, and why understanding codebases matters more than writing code faster.

Amar Goel

Super excited to share our first independent evaluation of Bito’s AI Architect on SWE-Bench Pro.

Working with The Context Lab, we evaluated AI Architect using the same Claude Sonnet 4.5 class agent, the same tools, and the same execution limits. The only difference was system context. 

With AI Architect enabled, the results were clear: 

• Task success rate increased by 39%
• Tasks completed 19.6% faster 
• Tool calls dropped by 25.4% 

All without any additional token or compute overhead.  

We intentionally focused this evaluation on large, real-world codebases with millions of lines of code and hundreds of files, because that’s where today’s coding agents struggle the most without system-level understanding. 

This reinforces our core belief: better outcomes don’t come from bigger models alone, but from giving AI real understanding of the system it’s working in. 

The full SWE-Bench Pro evaluation report is available below. 

Link in the comments.

Bito | Top score on SWE-Bench Pro - Deep codebase intelligence for AI coding and reviews

In 2025, AI-assisted pull requests showed around 1.7× more issues than human-written PRs, even though tests passed and reviews looked clean. This will only go up if we don’t understand the root: 

The reason is fairly straightforward once you look closely.  

AI code gen works with partial views of the codebase. 

It sees files and snippets, but it does not see how state flows across services, where invariants were added after past incidents, or which assumptions are baked into downstream consumers.  

When those relationships are missing, the generated code follows patterns that look reasonable locally and break behavior globally. 

The study pointed out that most of the issues appeared in places reviewers and tests are weakest. 

Conditional paths, retries, rollbacks, and failure handling are rarely exercised fully in unit tests, and PR reviews are scoped to diffs.  

If the system context is incomplete, both humans and tools end up approving code that only works under ideal conditions. 

This is why scaling AI coding tools inside large systems has been harder than demos suggest. Throughput goes up, but system risk quietly accumulates. 

This is the gap Bito’s AI Architect is designed to address.

If you want to learn more, I'd be happy to chat. DMs always open.

AI coding tools feel super cool on side projects, then break as soon as they're inside large, existing, enterprise codebases.  

Custom frameworks, internal conventions, fragmented repos, and years of accumulated constraints change the whole narrative. 

A LinkedIn post by Gergely Orosz from earlier last year captured it well, including a case where a top company rolled out an AI coding tool to thousands of developers and roughly half stopped using it a few months later. 

~50% ! 

The fact that it still feels true today matters, because it points to a structural gap (not a temporary tooling gap).

AI code gen breaks down when the tool cannot model the system, including dependencies, call paths, and the implicit contracts that make production software behave.  

That is where codebase intelligence becomes the difference between a demo and something you can trust in an enterprise. 

Exactly what Bito is building: The AI Architect.

Veracode’s 2025 GenAI code security report states that about 45% of AI-generated code still contains vulnerabilities.

This is one big reason AI-generated code keeps adding security debt. And that number has barely changed even as models have improved.

The underlying reason is straightforward. These models learn from public code, and public code includes a lot of insecure patterns.

When AI code gen is asked to implement common tasks, it often reproduces what it has seen most:

Raw SQL queries instead of parameterized ones.
Hardcoded secrets. Missing validation. And more..

The code compiles, looks reasonable in review, and passes basic tests, while carrying flaws that only surface later.

Security rules live across schemas, data flows, and service boundaries. When AI works without seeing those relationships, it fills gaps by pattern matching. That behavior scales insecure code faster than teams can realistically review it.

We built Bito’s AI Architect. It addresses this by building system intelligence through a knowledge graph of the full codebase, so AI code gen reflects how security actually works across the system.

🎤 This is what a customer has to say about the AI Architect:

"We’ve spent too much time fixing agent-generated code. Now most changes work in one shot because Bito’s AI Architect actually understands our services and APIs. It’s been a huge boost to Cursor."

AI Architect is available via MCP (on-prem coming soon).
We are rolling out demos on a huge scale.
DMs open if you want one.

EmReach

Senior Director

Hyperface

Associate Director - Customer Success

Orbo.ai

Growth Marketing Manager

Stealth Mode Startup - Data and Analytics

Identified a gap in the market and built a service offering around it. It essentially catered around offering a data analysis as a service using an existing SaaS platform of Datorama. It helped bring all digital advertising data on a one-view dashboard to quickly generate insights. While we got interest from a few potential customers abut the offering couldn't fly as most companies were not ready to incur extra cost at the time of pandemic.

The service offering was based on a single third party product (Datorama by Salesforce) while customers needed a lot of customisations. These customisations required Data Scientists that were hard to come at a justified cost. We realised that the business model would be dependent on a lot of people which wasn't scalable especially during the pandemic when clients weren't so keen to pay and were asking for longer free trial of the service.

Startup

Bonzai – The Programmatic Creative Management Platform

Bonzai is a leading creative management platform to design personalized and data-driven brand experiences that drive engagement across devices, in real time.

Key performance areas:
•	Grow business in Southeast Asia and ANZ region
•	Develop data-driven creative solutions for brands
•	Ensure smooth customer onboarding on the platform
•	Ensure customers derive maximum value from the platform
•	Define and optimize the customer lifecycle, map the customer journey and develop standardized interventions for each point in the journey.
•	Manage end-to-end sales cycle from pitches to onboarding
•	Contribute to product development with user insights

Director - Customer Success & Business Development

GroupM

Key performance areas:
•	Designing digital strategy and defining digital media KPIs
•	Collaborate across teams to strategize digital campaigns as part of overall business and brand objectives
•	Create strategies across all digital channels like Display, Programmatic, Search, Social, Mobile, Content and integrated solutions

Business Director

Komli Media

Led customer success and sales strategy for mobile ad solutions across the APAC region
Supported sales teams with marketing collaterals, creative solutions and operational support
Collaborated with internal teams to continuously deliver innovative solutions for brands like Unilever, Tata Motors, McD, KFC, Garuda Air, Maybank, etc.

Sr. Manager - Sales Strategy (Mobile)

Managed the Audience Measurement System from A to Z i.e. from panel management to client engagement with the support of an excellent team.
Seamlessly carried previous research experience into online media and adapted it to bring out key insights into consumer behavior by studying their online habits. Banking, travel, e-commerce, automotive, social media are some of the key verticals I worked upon during my stint in Online Audience Research.

Sr. Manager - ViziSense Online Audience Measurement

DecisionCraft Analytics ; Audience Measurement Ltd,

Sr. Client Engagement Exec.

IMRB International

Recommend, design, develop, and apply market research techniques to assess the marketing and communication activities of clients.

Research Manager

The Times of India

-Liaising with top corporate clients & their advertising agencies for marketing of ad space based on circulation and readership analysis

Senior Officer

With over 15 years of experience in the SaaS domain, I have acquired a deep knowledge of customer success strategies, online marketing, digital analytics, and CRM systems, as well as experience in data insights.

As a seasoned SaaS enthusiast, I thrive on transforming ideas into scalable and customer-centric solutions that drive tangible results for businesses.

At the heart of every successful SaaS venture lies a relentless focus on the customer. With a keen understanding of their pain points and aspirations, I have consistently delivered intuitive and feature-rich solutions that enhance user experiences and exceed expectations. My ability to listen to customers, analyze their feedback, and translate it into actionable insights has led to unparalleled customer satisfaction and loyalty.

Innovation is the cornerstone of the SaaS realm. Throughout my career, I have successfully collaborated with cross-functional teams to conceptualize and develop cutting-edge solutions.

My relentless pursuit of growth opportunities and strategic partnerships has enabled me to expand market reach and drive growth.

If you're passionate about SaaS, innovation, and driving transformative growth, I invite you to connect with me. Let's collaborate and shape the future of technology together!

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