caveman

Product Introduction

1. Definition: Caveman is an AI efficiency operating stack and cost optimization platform. Technically, it is a multi-layered system that sits between AI applications (like agents using Claude, ChatGPT, or Gemini) and their respective LLM providers to analyze, compress, cache, and route traffic.
2. Core Value Proposition: It exists to automatically reduce AI operational costs by up to 65% without sacrificing output quality. Its primary value is providing verified, provider-causal savings through a stack that applies optimizations like context compression and intelligent routing, then proves the reduction in token usage and spend with signed receipts.

Main Features

1. The Caveman Stack (Five Layers): This is the core architecture. It offers the same optimization engine at different integration depths: the Caveman Skill (open-source prompt compression), the Caveman Proxy (local traffic wrapping), the Caveman Agent SDK (programmatic context planning), the Caveman Cloud (managed gateway), and Caveman Enterprise (on-premises deployment). Each layer provides increasing control and verification.
2. Byte-Safe Compression Engine: The platform uses multiple, eval-gated compressors targeting specific data structures agents commonly send. It recognizes and optimally compresses JSON, logs, code (via AST), tables, and bulk context. A key innovation is byte-safe recovery; the original content is stored and can be exactly reconstructed, ensuring no loss of critical information like code syntax or error messages.
3. Verified Savings & Proof Plane: Caveman implements a rigorous, multi-stage verification ladder. Savings progress from inferred (modeled headroom), to replayed (measured delta on real requests), to verified (signed, booked savings from live traffic). This creates an audit trail with Ed25519-signed receipts, making the cost reduction claims CFO-auditable.
4. Eval-Gated Autopilot & Routing: Optimizations are not applied blindly. Each potential change (compression, model routing) must pass declarative evaluation gates checking for exactness (byte-for-byte accuracy for code/errors), structural validity, and task success rates. The system can automatically route requests to the cheapest model in a configured pool that passes these evals.
5. Granular AI Spend Visibility: The platform provides a unified workspace that attributes every dollar of AI spend to a specific cause, member, API key, workflow, and model. It calculates cost using provider-reported usage multiplied by public catalog list prices, offering a truthful, non-averaged view of expenditure and highlighting waste like uncached prefixes or oversized tool schemas.

Problems Solved

1. Pain Point: Unpredictable and spiraling costs from LLM API usage, especially for high-volume applications and AI agents. Traditional observability tools only show the bill but do not actively reduce it.
2. Target Audience: Developers and Engineering Teams building with AI agents (e.g., using Claude Code, Cursor, Vercel AI SDK); DevOps and Platform Engineers managing AI infrastructure costs; Enterprises and Startups with significant, production-scale AI traffic seeking to optimize operational expenses.
3. Use Cases: Optimizing token usage for AI coding assistants to reduce per-query cost; managing spend for production AI agents that handle customer support or data processing; providing cost governance and showback for internal AI tooling within large organizations; research teams needing to maximize experiment iterations within a fixed compute budget.

Unique Advantages

1. Differentiation: Unlike simple token counters or caching proxies, Caveman combines deep technical optimizations (context-aware compression) with a rigorous, proof-based business model. It doesn't just estimate savings; it aims to verify and sign them. Competitors often project monthly savings, while Caveman's dashboard starts at $0.00 verified savings until evidence is locked.
2. Key Innovation: The "eval-gated, fail-closed" automation philosophy. The system automatically finds optimization opportunities (via the Cave Plan autopilot) and ships them as pull requests with attached evidence, but never auto-merges or books savings until the change is live and continuously verified. This removes human error from the optimization loop while maintaining safety.

Frequently Asked Questions (FAQ)

1. How does Caveman reduce AI costs without hurting performance? Caveman uses context-specific compression (for logs, JSON, code) that is lossless from the model's perspective and byte-recoverable for the user. All optimizations are guarded by evaluation gates that check for output exactness and task success, ensuring quality is maintained before any cost-saving change is applied to live traffic.
2. Is Caveman secure? Does it see my prompts? In the Caveman Proxy (local) and Enterprise (on-prem) layers, prompt data never leaves your infrastructure. The control plane receives metadata but not the raw prompt bytes. The Cloud gateway processes traffic, but enforces zero data retention at write time. The system is designed with data privacy as a first principle.
3. What AI models does Caveman support? The platform explicitly supports and optimizes traffic for OpenAI's ChatGPT (GPT models), Anthropic's Claude, and Google's Gemini. Its architecture is provider-agnostic, allowing it to work with any LLM API, including private/on-premises model endpoints.
4. How are the "65% fewer tokens" savings measured? The claimed 65% average reduction in output tokens is based on Caveman's internal CaveBench methodology, which measures "savings at held quality." It uses paired trials comparing original and Caveman-optimized interactions across a diverse set of prompts, ensuring the task success rate and output fidelity (especially for code) remain equivalent.
5. What is the difference between Caveman Skill, Proxy, and Cloud? The Skill is an open-source, installable prompt style for agents like Claude Code. The Proxy is a local wrapper you run to compress traffic from any agent. The Cloud is a fully managed SaaS gateway that adds caching, routing, and the verification dashboard. They represent increasing levels of integration and managed service.