LLM providers update models silently. Prompts break on inputs you never tested. Structured outputs corrupt without raising a single error. By the time you know, users already have.
Quelm is the reliability layer your AI stack is missing. Catch regressions before they reach production. Monitor quality across every live call. And validate the internal integrity of each output the moment it's generated — no baseline, no history required.
The silent regression problem
Building on LLM APIs is not like building on any other API. A REST endpoint either works or it doesn't. An LLM endpoint returns something plausible-looking every time — even when the answer has drifted from what you need. There is no 500 error. There is no stack trace. There is just a slightly different output, silently corrupting downstream systems until someone notices.
Silent provider updates
LLM providers update hosted models continuously — safety tuning, inference changes, system prompt adjustments — without incrementing the public version string. The model you called last month is not the model you call today, even when the version number is identical.
Prompt fragility
Production prompts are tested on the inputs you anticipated. The long tail of real user inputs is different, and there is no existing tool that measures how fragile a given prompt actually is — until it breaks in production in ways you did not foresee.
No safety net
Software engineering has decades of CI/CD tooling for catching regressions. None of it translates to LLM outputs, which are probabilistic, semantically evaluated, and context-dependent. The industry has no equivalent of a test suite for prompt behaviour.
Three layers. One platform.
Quelm addresses LLM reliability at three distinct layers. Each targets a different failure mode. Each can be deployed independently or combined.
Regression testing
Quelm stores a curated set of approved prompt-output pairs — your golden set. On a defined schedule, or triggered by a CI/CD deploy or detected model version change, Quelm independently re-runs those prompts and compares the new outputs against your approved baseline.
Four signal types
- 1.Semantic similarity — embedding-based cosine similarity (alert threshold: < 0.85)
- 2.Structural diff — for JSON or markdown, schema validation and key-by-key comparison
- 3.Assertion checks — user-defined rules (output must contain X, length > 50 characters)
- 4.LLM-as-judge — secondary model scores output quality on a 1–5 scale with reasoning
Model A runs entirely independently of production traffic. Zero impact on live operations. No changes required to application code.
Live traffic monitoring
A lightweight agent runs inside your infrastructure, observing every LLM call. It evaluates outputs asynchronously against baseline expectations, routes unusual calls for deeper inspection, and — uniquely — automatically promotes statistically unusual outputs into your regression test suite. Your test library grows itself from real usage.
$ npm install @quelm/sdkimport { quelmWrap } from '@quelm/sdk'
const client = quelmWrap(
new Anthropic({ apiKey: process.env.ANTHROPIC_KEY })
)Cross-provider intelligence
Because Quelm observes traffic across its customer base simultaneously, it can detect a provider-side drift event in aggregate within hours — before any individual team notices. When a provider quietly pushes a change, Quelm sees it first.
No data leaves your infrastructure. Your API keys stay with you. BYOK (Bring Your Own Key) architecture by design.
Output certification
Quelm wraps structured prompts with a novel certification mechanism. At the moment of generation, the output is required to carry a verifiable integrity signal. Quelm independently recomputes that signal from the output fields themselves. A discrepancy is an immediate red flag.
Three certification levels
- 1.Logical consistency — line items sum to total; dates are in sequence; referenced entities are named consistently throughout
- 2.Cross-field dependency validation — encodes the dependency graph between fields as verifiable rules (total_value = duration_months × monthly_fee)
- 3.Deterministic fingerprint — a canonical checksum derived from the output's key fields in a defined order
Model C requires no golden baseline and no historical data. It validates each output in isolation, in real time. It is the only mechanism in the Quelm stack that is both baseline-free and deterministic — pass/fail, not probabilistic.
See it catch what everything else misses
Five industries. Five silent failures. Every output passes schema validation. Only Quelm catches the real errors.
The arithmetic error (€1,200 + €3,800 = €5,000, not €4,800) would have passed every standard validation check. Quelm’s certification layer caught it at the moment of generation. The downstream accounting system received an alert instead of corrupted data.
The only platform that validates meaning, not just structure
Every observability tool logs what happened. Format validators confirm the JSON schema was followed. Neither one tells you whether the content is actually correct. Quelm operates at a different layer entirely.
Integrity at the moment of generation
Most reliability tools are retrospective — they compare against what you expected. Quelm's certification layer validates each output independently, at the instant it's generated, with no prior history required.
Cross-provider drift detection
Because Quelm observes traffic across its entire customer base simultaneously, it sees provider-side behavioural changes in aggregate — hours before any individual team's alerts trigger. Silent updates become visible events.
A test suite that builds itself
Existing tools require you to define every test case manually. Quelm's live monitoring layer identifies statistically unusual production outputs and promotes them into your regression suite automatically. Coverage grows with usage.
No proxy, no data exposure
Quelm runs as a lightweight SDK agent inside your own infrastructure. Your API keys and prompt data never transit a third-party server. Enterprise compliance requirements — GDPR, HIPAA, SOC 2 — are met by architecture, not policy.
The certification mechanism is novel. The verification approach is inspired by how financial payment systems embed mathematical proof of validity directly into a reference — meaning the validity signal is inseparable from the data itself. We're not ready to publish the full technical architecture yet, but we are ready to show it to the right teams.
Built for teams shipping LLMs to production
Engineering teams
LLMs in customer-facing workflows
You've shipped an LLM feature and it worked at launch. Now you're not sure it still works the same way. You've probably discovered a regression through a user complaint, not a monitoring alert. Quelm gives you the safety net that should have been there from day one.
Agencies & studios
Delivering under quality SLAs
Your clients don't want to hear that a model provider pushed an update. They want to know their product works. Quelm gives you the monitoring layer that turns "we think it's fine" into "we can prove it."
Regulated industries
Finance, legal, healthcare, compliance
For you, LLM output consistency is a compliance requirement, not a quality preference. Financial extractions. Contract summaries. Clinical data processing. Quelm's certification layer is designed specifically for structured outputs where silent errors have real consequences.
Be among the first teams to use it
Join the waitlist
We're onboarding a small cohort of early teams starting in Q2 2026. Priority access for teams with structured output use cases — data extraction, document processing, financial automation, contract analysis.
Reach out
We're speaking with a select group of investors who are building conviction in LLMOps infrastructure. If you understand why the reliability layer matters — and why the cross-provider position is structurally unbeatable by native provider tooling — we'd welcome a conversation.