Bito

Bito’s AI Architect delivers a 39.4% higher task success rate on SWE-Bench Pro 🏆 • Higher success, faster execution • Fewer tool calls, and no added cost Validated by an independent evaluation with The Context Lab. Swipe through for the key findings, or read the full evaluation report here: bito.ai/benchmarks/swe…

Bito’s AI Architect now extends into technical design and planning through Jira. AI agents accelerated coding. Technical design is still behind. Senior engineers spend 60 to 70% of their time on work before coding starts. Every new feature creates that same queue at the same desks. AI Architect now gives every engineer system level context the moment an epic is created, combining deep codebase context with operational history from past Jira tickets. AI Architect now provides: → Feasibility analysis → Technical design documents → Epic breakdowns → Proactive risk detection Same knowledge graph now powering technical design, code generation, and code reviews. Read more here: bito.ai/blog/ai-archit…

The TPUT Implementation Claude Code Got Wrong and AI Architect Got Right: bito.ai/blog/tput-impl…

On Elasticsearch's 3.85M line codebase, Claude Code skipped an algo implementation. Because it could not see how the pipeline connects. We ran the same agent on the same task with AI Architect providing system context. Without system context: ➡️ Brute force workaround, 6 files, severe memory risk With AI Architect: ➡️ Multi phase TPUT across 27 files ➡️ Proper pipeline extension actions ➡️ Multi shard test coverage Full writeup in comments.

Bito's AI Architect gives Claude Code full codebase context across all your repos through MCP. How it works: → Builds a knowledge graph of your codebase → Claude Code queries it at runtime for every prompt This matters because: on SWE Bench Pro, Claude Sonnet 4.5 with AI Architect saw 39% higher task success. Read the full integration guide and try for yourself. For FREE. → bito.ai/blog/how-to-in…

Bito's AI Architect gives Cursor full codebase context across all your repos through MCP. How it works: → Builds a knowledge graph of your codebase → Cursor queries it at runtime for every prompt This matters because: on SWE Bench Pro, agents with AI Architect saw 39% higher task success. Read the full integration guide and try for yourself. For FREE. → bito.ai/blog/how-to-in…

How @PrivadoHQ shipped enterprise SSO in 5 hours with Bito's AI Architect: (the original estimate was 7-10 days) The feature spanned 4 repositories across auth, billing, and user management, each with a different tech stack. AI Architect's knowledge graph gave the coding agent cross-repo context from the first prompt. → 4,872 lines added across → 24 files PRD generated and approved in 1 hour → Zero senior engineers pulled off their work Full case study here: bito.ai/case-studies/p…

Coding agents stall when they face a large codebase with no clear entry point. We tested this on a 710-file Python codebase from SWE-Bench Pro. Without system context: → Claude Sonnet 4.5 made 96 tool calls → Wrote zero lines of code With AI Architect: → 500 lines of production quality code delivered → 42% fewer tool calls → Complete backend feature with tests Full case study here: bito.ai/blog/blank-pag…

AI Architect fixed calendar URL validation using pattern reuse after coding agent failed: bito.ai/blog/ai-archit…

Coding agents often build new systems when the fix is one line in an existing function. We tested this on a calendar URL validation task from SWE Bench Pro. Without system context: → Claude Sonnet 4.5 created 3 new helper functions, a new enum, and 50 lines of unverified code With AI Architect: → Extended the existing getError() function by one case 15 lines total, zero new abstractions All validation scenarios passed The codebase already had the right pattern. AI Architect recognized it. The baseline agent rebuilt it from scratch. Full case study in the comments:

Full blog: bito.ai/blog/how-ai-ar…

Bito's Lead Engineer hit a production webhook failure on a Wednesday afternoon. She pasted one error log into her coding agent with AI Architect enabled. With deep codebase context across 50+ repositories, the agent traced the root cause to a completely different service from where the error surfaced. Result: → Root cause identified in under 10 minutes → 3 services traced, 50+ repos searched → $0.91 total AI cost Full writeup in comments.

The 7-Component React Fix That Coding Agents Missed and AI Architect Nailed: bito.ai/blog/the-7-com…

Coding agents fix React components one file at a time. When a feature spans 7 components, they break. We tested this on SWE Bench Pro, with AI Architect. Without system context: → Sonnet 4.5 modified 2 to 3 components → failed all 4 tests With AI Architect: → All 7 components modified → 4 out of 4 tests passed AI Architect mapped the complete component tree and handler chain before the agent started writing code. Full case study link in comments.

@tcl_ai Great work team!

@BitoHQ Congrats team @BitoHQ

Bito’s AI Architect delivers a 39.4% higher task success rate on SWE-Bench Pro 🏆 • Higher success, faster execution • Fewer tool calls, and no added cost Validated by an independent evaluation with The Context Lab. Swipe through for the key findings, or read the full evaluation report here: bito.ai/benchmarks/swe…

We're launching AI Code Reviews in CLI. Catch bugs before they reach production — right in your terminal. What it unlocks: → Pre-commit AI reviews → Works with Claude Code, Cursor, Windsurf → Universal SCM support (Git, SVN, Perforce) → Interactive fixes with automatic backups Learn more: bito.ai/blog/ai-code-r…

Vibe coding has 10x’ed code output, but every developer understands the "hallucination" problem. What becomes clear at scale is that hallucinations are usually a symptom. Developer surveys show the bigger issue is missing system context once AI tools are used on large production codebases. We broke down why better prompts and larger context windows do not close the gap - bito.ai/blog/large-and…

AI coding agents feel reliable until they hit real production systems. The code compiles and tests pass, yet downstream failures show up because... Agents lack codebase intelligence about APIs, dependencies, and past incidents. We broke down why this keeps happening: bito.ai/blog/ai-coding…