ByteBell gives every AI change the one thing the model can't see: its full cross-repo impact, checked before you merge.
80% cheaper, 90% faster, more than 93% accurate. Your source never leaves your infrastructure.
The verification layer sits between your agents and prod. It grounds what they generate, proves what they ship, and answers what you ask, all against the real behavior of your codebase. Changes go out faster, and the ones that would quietly break something never make it in.
Your agent writes against the real rules and contracts already in your repos, so what it produces is more than 86% accurate at 80% lower cost, not a plausible guess that breaks something three files away.
Before a change lands, ByteBell traces its blast radius across every repo and generates the tests that catch the break, at more than 95% accuracy. You find out in review, not in prod.
Every pull request is checked against the contracts other repos depend on and the rules your code already enforces. It flags what a diff-only reviewer can't: the change that's clean but wrong.
ByteBell writes test cases from the actual behavior across your whole org, covering the cross-repo paths a single-repo suite never touches.
“Where does payment validation happen?” answered by meaning across every repo in milliseconds, with exact file references, not grep.
Follow a failing behavior back through service and repo boundaries to the change that caused it, in minutes instead of a day of spelunking.
ByteBell reads your whole org once, with any model you like, and builds one graph of how everything connects and what every part is meant to do. About $13 indexes 1,000 files, then it re-derives only what changes on each commit, and serves the same graph to every tool over MCP.
Claude, GPT, Gemini, DeepSeek, Llama, Qwen. ByteBell builds the same IR from any model and locks you to none. ~$13 indexes 1,000 files, once; it re-derives only what changes on each commit.
Intent, dependencies and business logic compiled into a single graph. Every change gets checked against intent before it lands, so every query returns precise meaning, not a dump of files that rots the window.
Your IDE, copilots, agents and review tools all read the exact same trunk through one MCP endpoint. Add it in seconds. No re-indexing per tool, no per-session warmup.
Ask in plain English and get the exact files, across every repo, in milliseconds, by meaning, not by grep. ByteBell understands purpose and relationships, not just symbols.
Symbol-level tools only read the repo the PR lives in. ByteBell reads the whole graph, direct and transitive dependencies, including the contract, schema, and build dependencies that never show up as a function call, so you know exactly what breaks across 50 repos or 500 before you merge.
Instead of dumping thousands of files into the window, agents pull a small slice of structured meaning from the graph. Context stays clean all session. 80% cheaper, 90% faster, no compaction death-spiral.
ByteBell derives a candidate spec from your real code, then checks every AI change against it before it merges. You don't rebuild code from the layer, you prove the code still does what it's supposed to, so hallucinations get caught before they ship.
ByteBell speaks the Model Context Protocol, so the same IR powers your editor, your agents and your review bots. No custom adapters, no per-tool re-indexing.
ASTs map call edges. Context files store stale prose. The questions developers actually ask are about intent, and that lives in the trunk, not the symbols.
| The question you ask | AST | LLM reads files | CLAUDE.md | ByteBell IR |
|---|---|---|---|---|
| “What calls validateCard()?” | Precise | ~ Sometimes | ~ If documented | Precise |
| “Which code handles payment?” | Blind | ~ If it fits | ~ If hand-written | By meaning |
| “What breaks across 50 repos?” | Single-repo | Too big | Doesn’t scale | Cross-repo graph |
Once the IR can check code against intent, a lot becomes possible that a parser simply can’t do.
Change the intent once, then check every change across 100 repos against it in lockstep.
Derive a candidate spec from real code, then verify every change against it.
Check every AI change against the IR to catch hallucinations before they merge.
Meaning preserved: Java→Go, REST→gRPC, monolith→services.
New engineers ask the codebase questions and get answers with file refs.
“Where’s the rate limiter?” across every repo, in milliseconds.
Needs Bun, Docker and an OpenRouter key. bytebell boot brings up the local Mongo + Neo4j + Redis stack, then any MCP client reads the same graph. Self-host the open source, or use the hosted IR.
ByteBell runs the entire indexing and serving pipeline inside your own infrastructure. You own the graph. No third-party server ever sees your source.
Deploy via Docker in your own cloud or datacenter. Admin panel on your domain, your control.
Run fully offline against local or self-hosted models. Nothing egresses your perimeter.
The graph and metadata are yours. Portable, inspectable, and never a vendor hostage.
A verifiable IR turns code into a language humans and agents share.
Open-source first. No per-seat lock-in. On-premise, hybrid, or air-gapped.
Index once. Serve exact context to every tool you already use, over MCP, for 80% less. Get started free in minutes.