Compliance & security baseline
EU data residency
Servers in Germany under GDPR. Diffs processed; only learning signals persisted. Per-account purge anytime.
Confidential compute (TEE)
AMD SEV-SNP option: patch text decrypted only inside encrypted guest memory. Operator cannot read your diffs even with host root. Intel TDX / AWS Nitro on request.
Rust end-to-end
CLI and server. Memory-safety, data races, use-after-free CVE classes do not exist by construction.
SSH-key identity
No emails, no passwords, no third-party trackers. Same key you push commits with. SSO on enterprise tier.
Where it sits in your SDLC
Pre-commit gate on the engineer's laptop. The full diff never leaves the machine; only sanitised tokens cross the wire (and even those run inside an encrypted enclave on the Enterprise tier).
Why this pays for itself
AI-assisted teams ship more code per dev — and more residue per commit. The residue compounds: comments that restate the next line bloat every diff your reviewers read, defensive crud breaks intent for new hires, untested branches become Monday's outage. Each of those has a measurable dollar cost. Sloppoke catches them before commit lands.
Plug in your numbers
Drag the sliders. Total updates live. Math anchored on IBM SSI (prod bug = 100× design-time fix) and Capers Jones ($1k–$10k per prod defect). All three levers compose.
- These are illustrative ranges, not a guarantee — each org's numbers move with industry, tooling, and AI-assist intensity.
- Token-reduction savings assume an integrated agent workflow (Cursor / Claude Code actively reading the codebase). Lower if you only use chat-window LLMs.
- Defect-cost varies wildly by industry. Fintech / health / regulated workloads pay far more per prod defect than internal tooling does.
- Slop density grows with AI-assist usage — orgs that have been on Cursor / Claude Code for more than a few months tend to see higher density and bigger savings.
Where it lands hardest
-
Heavy AI-assist orgs
If your engineers ship multiple Cursor / Claude Code drops a day, your codebase carries the most slop residue. Most to gain on both shift-left and token bills.
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Regulated industries
Fintech / health / defense. Prod defects cost $10k–$100k+ each. One prevented incident per quarter pays for sloppoke many times over.
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Monorepo teams
Cross-file detectors (untested branches, hallucinated imports later) shine in repos where signals span multiple files inside one diff.
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OSS / vendor-facing repos
Anything customers, auditors, or contributors read. Half-finished branches and AI-authorship trailers in checked-in source erode trust — sloppoke gates them at the commit boundary.
How sloppoke compares
What sloppoke is. A statistical detector for the correlation between known LLM-coding patterns and codebase failures. Two surfaces: public scanner for VC / PM / HR due-diligence on code quality, and a developer-side CLI for catch + clean at the commit boundary.
What sloppoke is not. Not a perf tool, not a safety guarantee. Slop density is a correlation signal, not a proof of correctness. Runtime: profilers, load tests. Correctness: types, tests, formal verification.
| sloppoke | CodeRabbit | OSS slop | Linters | |
|---|---|---|---|---|
| Pre-commit gate | ✓ | ✗ | ✓ | ✓ |
| Verdict latency | <10 ms | 15 min+ | ~ms | ~ms |
| Action on a hit | strip / TODO(slop) |
review comment | flag only | --fix rewrites body |
| Deterministic verdict | ✓ | ✗ | ✓ | ✓ |
| Learns from your feedback | ✓ | ✗ | ✗ | ✗ |
| Multi-model RL loop | ✓ (NSED) | ✗ | ✗ | ✗ |
| Vendor sees source | diff only | full repo + PR | — | — |
| Pricing | flat sub | seat + LLM tokens | free | free |
- CodeRabbit lives in the PR by design. Post-push, vendor logo on every diff, adds LLM prose to version control. Sloppoke gates pre-commit; residue gone before it lands.
- The RL loop is ours, and it's SOTA. Catalog tuning runs on NSED — N-way Self-Evaluating Deliberation, peeramid labs — where ensembles of consumer-grade <20B models match or exceed proprietary 100B+ SOTA on AIME 2025 + LiveCodeBench, with peer-mediated correction pushing DarkBench sycophancy below any single agent. OSS + linters have no equivalent.
Independently measurable
Every claim above is verified against a public, peer-published academic dataset of 304K AI-authored commits across 6,299 production OSS repos. The measurement script, the catalog → linter mapping, and the canonical output artefacts all live in github.com/peeramid-labs/sloppoke-bench. Re-run the script against the same archive, get the same numbers.
Headline: sloppoke's pre-commit gate would have flagged 39.9% of the static-analysis findings that AI commits shipped past human review and reached HEAD across those 5,173 repos. Broken down by severity:
warning class: 53.0% caught
refactor smells: 84.7% caught
convention class: 25.7% caught
error class: 3.9% (CI already catches most
before HEAD — sloppoke does not compete here)
The CI on the bench repo continuously verifies these numbers against the live commercial endpoint. If sloppoke.me ever stops returning the published values, the green badge on the bench README goes red.
What ships in the Enterprise tier
Everything in Starter, plus: custom monthly volume, team accounts, private-corpus calibration, SLA, audit trail, SSO, on-prem or confidential-compute (TEE) deployment, and a named engineer on Slack / email for integration questions.
Pricing is per engagement — depends on volume, TEE flavour, and integration depth. Most deals land in the $15k–$60k / yr range. At a 200-engineer org's $1.8M illustrative savings, that's a 30–120× ROI.
Pick a 30-minute slot
We walk your codebase numbers, show the calibration plan, and scope a pilot. No slides, no upsell. Bring a redacted sample diff if you have one.
Calendar full? Email engineering@peeramid.xyz with team size and we'll work something out.