AI coding agent startup ByteAsk has raised $1 million in a pre-seed funding round led by Y Combinator and Entrepreneur First, with participation from angel investors. The startup plans to deploy the capital toward product and infrastructure development, engineering and research hiring, GPU compute, training data, and enterprise-grade security infrastructure.
Founded in 2026 by Anirudha Kulkarni and Pratyush Saini, ByteAsk is developing AI coding agents designed specifically for C and C++ codebases, with a focus on engineering environments where reliability, performance, and correctness are critical.
The founders previously built legal AI startup LawSutra AI, which was acquired by Manupatra, an online legal research platform.
Building AI Agents for Mission-Critical Engineering
ByteAsk is targeting engineers working on mission-critical applications across sectors including defence, aerospace, robotics, high-frequency trading, finance, embedded systems, automotive, and semiconductors.
The startup is positioning its platform around challenges that can arise when general-purpose AI coding tools are applied to complex C and C++ environments. According to ByteAsk, these codebases often require a deeper understanding of system architecture, dependencies, performance constraints, hardware interactions, and correctness requirements.
The company intends to initially focus on large enterprises operating in high-frequency trading, automotive, and embedded systems, before expanding into adjacent markets.
ByteAsk said it sees a significant commercial opportunity in AI-assisted software engineering, citing industry estimates that place annual engineering salary spending associated with C++ at around $400 billion. The company estimates the broader agentic coding opportunity at approximately $10 billion and expects the market to grow rapidly.
Focus on Enterprise Security and Infrastructure
A significant portion of the newly raised capital will be directed toward building infrastructure for enterprise customers. This includes security, privacy, and on-premises deployment capabilities, areas that can be particularly important for organisations working with sensitive codebases and mission-critical systems.
The company also plans to invest in GPU compute and training data as it develops its AI systems and specialised coding capabilities.
ByteAsk said engineers are currently using its platform approximately six times more intensively per day than a comparable open-source coding agent. The startup also claims that its weekly active users are doubling week over week.
ByteAsk Reports 89% Resolution Rate on Internal Benchmark
The startup has also developed an internal benchmark based on real-world firmware engineering tickets to evaluate its approach.
According to ByteAsk, its grounding environment enabled a smaller AI model to resolve 89% of the tested tickets, compared with 61% for the best frontier model tested without the same environment.
The company argues that the results demonstrate the importance of providing AI coding agents with specialised context and engineering environments rather than relying solely on increasingly large general-purpose models.
However, these figures are based on ByteAsk’s internal benchmark, and independent evaluation would be needed to establish how the system compares across broader real-world development environments.
Developing a Dedicated C++ Model
Beyond its coding agent, ByteAsk is also working on a specialised post-training approach for C++.
The company plans to release a language model specifically post-trained for C++ within the next six to eight months. The move reflects ByteAsk’s broader strategy of building AI systems tailored to the requirements of specialised engineering workflows.
With its new funding, the startup plans to expand its engineering and research team while continuing development of its coding agents and underlying AI infrastructure.
As AI coding tools increasingly move from code completion toward autonomous software engineering workflows, ByteAsk is focusing on a segment where software development is closely tied to hardware, performance, safety, and system-level reliability.
By: Arushi Agarwal
