Macroscope provides an AI‑powered code review platform that analyzes pull requests using graph‑based AST representations and large‑language models to detect high‑signal bugs, generate concise PR and commit summaries, and auto‑approve low‑risk changes. The service integrates with GitHub, Slack, and Jira/Linear for real‑time codebase queries and status updates, and includes enterprise‑grade security and SOC 2 Type II compliance.
Funding
$30M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Software engineering teams spend significant time manually reviewing pull requests, writing commit summaries, and coordinating status updates across disparate tools, which leads to delayed releases, missed bugs, and inefficient meetings. Existing code review solutions often generate noisy false positives or lack context from issue trackers, making it hard to assess the true impact of changes.
Solution
Macroscope delivers an AI‑driven code review platform that automatically analyzes pull requests using graph‑based Abstract Syntax Tree (AST) representations of the entire codebase. By combining large‑language models (e.g., OpenAI o4‑mini‑high and Anthropic Opus 4) with issue‑tracker context, it surfaces high‑signal correctness issues and suggests concrete fixes. The system generates concise, AI‑written commit and PR summaries, and can auto‑approve trivial changes, reducing manual overhead. Integrated Slack, Jira/Linear, and GitHub connectors let engineers query the codebase on demand and receive real‑time status updates without leaving their workflow. A continuous self‑learning loop incorporates developer feedback to improve detection accuracy over time. All data is encrypted at rest and in transit, with SOC 2 Type II compliance and strict isolation of customer code.
Target Audience
The primary customers are engineering teams at mid‑size to large technology companies that use GitHub (or compatible Git platforms) and issue‑tracking systems such as Jira or Linear, seeking to accelerate code reviews and improve visibility into development progress.
Features
- AST‑based graph model that captures cross‑file dependencies for precise bug detection across Python, TypeScript, Kotlin, Java, Rust, Swift, and Go.
- Dual‑model pipeline (OpenAI + Anthropic) that performs an initial scan and a consensus verification step to minimize false positives.
- Automatic PR summarization and commit‑message generation, delivered via web dashboard and Slack notifications.
- Contextual analysis that pulls ticket data from Jira, Linear, or custom issue trackers to align code changes with business intent.
- Auto‑approval workflow for low‑risk PRs, configurable via policy rules and role‑based access controls.
- Real‑time Q&A chat interface in Slack that queries the full codebase, git history, and issue metadata.
- Enterprise‑grade security: end‑to‑end encryption, SOC 2 Type II certification, isolated customer environments, and a policy of not training external models on proprietary code.
- Usage‑based billing integrated with Stripe, supporting per‑developer licensing and transparent consumption tracking.