Enterprise AI Control Plane
Capture multi-tool developer workflows with zero code changes, Monitor org-wide spend with a governed LLM Gateway, and actively Optimize token costs by up to 40% with RCLM Signals.
$claude "search sessions with auth issues"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Found 12 sessions matching query
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
The problem
Every debugging session, every architecture decision, and every script generated across Gemini CLI, Antigravity, Claude Code, and Cursor disappears into ephemeral terminal windows. You can't search it, can't attribute token spend by team, and can't audit unmasked credentials leaking to third-party model APIs. Learn how engineering leaders address this with our Engineering Leads Solution and Security & Compliance Solution.
The Enterprise Blindspot
The ReclaimLLM Platform
Why now
Frontier labs price below cost to win developer mindshare. As compute costs mount, pricing will normalize toward real cost. Engineering orgs that establish spend attribution and context compression today are insulated when pricing corrects. See how in our Token Capacity Case Study.
01
Aggressive pricing and free tiers drive developer adoption. AI feels cheap because compute is heavily subsidized.
02
Compute infrastructure costs catch up as market growth slows and investors expect operational margins.
03
Token pricing normalizes toward true compute cost — organizations without FinOps controls feel the margin shock first.
The cheapest AI you will ever run is the AI you are running today.
FinOps & Cost Optimization
Range-aware read caching and test suite output compaction strip redundant prompt tokens locally before requests leave developer machines, cutting token spend by up to 40%.
Automated pattern detection flags over-exploration, session bloat, idle gaps, and runaway agent loops with direct links to underlying sessions, files, and repositories.
Attribute token consumption by team, developer, and codebase repository. Replay captured session cohorts across candidate models to verify quality and savings before migrating.
How it works
01
Capture multi-tool developer workflows across Gemini CLI, Antigravity, Claude Code, Cursor, Codex, and LiteLLM proxies. Endpoint DLP automatically scans and redacts .env credentials before data touches the network.
Native CLI hooks (rclm-hooks), local proxy, browser extension, and rclm-sync historical backfill.
02
Centralize provider credentials in the Enterprise LLM Gateway. Issue team-scoped keys with model whitelist policies and monitor spend across teams, developers, and codebases via 15-minute materialized views.
Multi-dimensional cost attribution, 3-tier RBAC, and tamper-evident compliance audit logs.
03
Automatically detect developer workflow waste (over-exploration, session bloat, repeated restarts) with RCLM Signals. Cut token spend by up to 40% with range-aware read caching and test output compaction.
Deterministic waste detection, model replay evaluation cohorts, and Docker/Helm private VPC self-hosting.
Zero proxy friction, zero config files, zero code modifications. Two terminal commands and your AI control plane is live. See full instructions in our Installation Guide.
$ pip install rclm
$ rclm-hooks-install
✓ AI workflows connected to your enterprise control plane
→ reclaimllm.com/dashboard
Enterprise Capabilities
Zero-code native hooks for Gemini CLI, Antigravity, Claude Code, Cursor, Codex, and LiteLLM proxies with pre-execution endpoint DLP secret masking.
◈Full session timelines capture paired tool executions, terminal stdout/stderr, and structured git file diffs for PR code reviews.
⊞Centralized credential vault for OpenAI, Anthropic, Gemini, and Azure OpenAI with team-scoped gateway keys and model policy rules.
⊟PostgreSQL materialized views refreshed every 15 minutes break down token spend and consumption by team, developer, repo, and model.
⌁Automated pattern matching flags developer workflow friction (over-exploration, session bloat, repeated restarts) with direct evidence links.
⬡AES-256 raw transcript encryption with customer recovery keys, US/EU cloud regions, and Docker/Helm private VPC self-hosting options.
Model freedom
The AI landscape moves fast, and no single model is best at everything. RCLM is a neutral layer that decouples your session history from any single provider — your reasoning, code, and history stay with you regardless of which assistant you use this month or next.
Explore Model Switching Feature →Take a debugging session started in Claude Code and continue it in Gemini CLI, Codex, or a local model — no manual re-priming.
Move routine work to cheaper models and keep frontier models for hard problems, with a single unified record across all of them.
Export your full history in standard formats or delete it entirely. No lock-in mechanics — if you leave, your data leaves with you.
For individuals
Every debugging session you close, every architecture you design, every workflow you build with AI — that's your work. RCLM captures it permanently. Search it, debug with it, export it, and reuse it as working context when the next task starts. Read our Developers Solution Guide.
For enterprises
Which models are your developers using? What proprietary code is flowing to third-party APIs? Where is the AI spend going? RCLM gives engineering leaders one unified platform to capture, monitor, and optimize AI workflows.
Open Ecosystem
Four capture surfaces, each usable standalone. The proxy works without hooks. The browser extension works without either. Start with what fits, add the rest as you scale.
Capture · Monitor · Optimize · Govern