Your AI tools are guessing.We make them know.
Every session they rediscover your codebase from scratch — guessing at structure they can't remember. The FORGE Engine gives them persistent, queryable memory of how your code actually works, so they retrieve real context instead of guessing.
Drops into the agents you already use
AI made writing code fast. Everything else got slower.
Amnesia has a price. When agents guess instead of remember, the cost compounds — rework, review drag, and production incidents. Hover each figure for the detail.
accuracy on codebase-specific tasks
Frontier models answer repo-specific questions correctly only ~70% of the time — the rest is confident guessing.
ARXiV 2026
longer PR waits for AI code
Pull requests containing AI-generated code sit in review 4.6× longer before they merge.
LinearB 2025
more review time on AI-heavy teams
Teams shipping mostly AI code spend 91% more human time reviewing it than before.
LinearB 2025
more production incidents
Change-failure rate has climbed 23.5% since AI-assisted development became the norm.
ARXiV 2026
Sources: ARXiV 2026 · LinearB 2025
The bottom line
Hidden cost per 100 developers, every year
// Code is logic.You can't reliably guess at logic.
01000011 01101111 01100100 01100101 01010110 01100001 01101100 01100101 01110100LLMs are powerful statistical engines. FORGE brings deterministic logic back into the equation.
We give your agents a memory
FORGE reads your repository and builds the Code-Logic Universe — a persistent, deterministic model of every symbol, dependency, and execution path. Always current, built without running your code, and made for agents to query instead of guess.
Discover
Pulls in every file and artifact across your repo, wherever it lives.
Understand
Parses structure and semantics — symbols, signatures, inheritance, intent.
Master
Resolves cross-file relationships, dependencies, and execution paths into one graph.
Your agents get exactly the context they need — the moment they ask.
The whole platform, in one view
Two paths meet at one engine — how your code becomes a queryable model, and how your agents query it. Hover or tap any box to see what it does.
Integration layer
FFA carries FORGE Reviewer's full capability — incl. automated refactors
FFA carries FORGE Reviewer's full capability — incl. automated refactors
The whole FORGE platform, in one view
Two paths meet at one engine: your code becomes a deterministic, queryable model — and your agents (and GitHub) query it. Hover or tap any box.
Runs where your code lives
Your code never leaves your perimeter.
Faster, cheaper, exact.
The number everyone feels is inference cost — and FORGE cuts it to the bone. Benchmarked against Claude Sonnet 4.6 on a ~30k LOC production Python repo. Multiple runs, reproducible.
Faster answers
Fewer tool calls
Accurate. Zero hallucinations.
Lower inference cost
Same agent. Same repo. One difference.
We ran identical codebase questions with and without FORGE. Without it, the agent burns tool calls hunting for context. With it, one query to the Code-Logic Universe returns the exact answer — so latency and cost collapse together.
- Fewer tool calls → lower token spend
- Exact retrieval → zero hallucinated context
- Reproducible across multiple runs
See the numbers — all three questions from the walkthrough
Complexity Analysis
Find the most complex method + its metrics
Inheritance Tracing
Chain the methods’ inheritance hierarchy
Impact Analysis
If get_file_system changes, which files are affected?
Live zero-shot run on a ~30k LOC production Python repo, Claude Sonnet 4.6 — the same three questions shown in the walkthrough. We confirmed these results were representative, not anomalous. The headline “55× faster · 31× fewer tool calls” is Question #1; across all three the overall is 15.7× faster and 16.2× fewer tool calls.
The video walks through three questions. Our internal benchmark spans many more, across every category — request the complete dataset.
The cheap-inference era is ending
Frontier models were sold below cost to win adoption. Now that teams depend on them, prices are climbing.
Blended (input + output) ÷ 2, per million tokens.
Blended (input + output) ÷ 2, per million tokens.
The cost of inference, per model
Monthly spend for one full-time developer, by release date
Each release pushes the meter up. codeValet cuts your call volume ~96% — the shaded gap under each line is what you stop paying.
Monthly $ = blended (input + output) ÷ 2 price × output tokens per task (DeepSWE, highest reasoning setting) × a full-time developer's task volume. Prices & release dates from vendor pricing pages.
Give your agents a memory
We're rolling out FORGE access in real development environments. Request access and we'll get you set up.
Prefer to talk to someone? sales@codevalet.com