LLM outputs
look right.
Until they don't.
Quelm catches silent failures the moment they're generated — before your users, your clients, or your regulators do.
financial-parser
claude-opus-4 · 12ms
clinical-notes-ehr
gpt-4o · 34ms
trading-signal-gen
claude-3-5-sonnet · 8ms
legal-contract-ext
gpt-4o-mini · 21ms
12,400+
responses certified
in private beta
99.3%
accuracy on drift
vs LLM-as-judge
<80ms
added latency
p99 at production
01 — The problem
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.
Silent provider updates
LLM providers update hosted models continuously — 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 tool that measures how fragile a given prompt is — until it breaks in production.
No safety net
Software engineering has decades of CI/CD tooling. None of it translates to LLM outputs, which are probabilistic and semantically evaluated. The industry has no equivalent of a test suite for prompt behaviour.
02 — How it works
Three layers. One platform.
Each layer targets a different failure mode. Each can be deployed independently or combined.
Model A
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, Quelm re-runs those prompts and compares new outputs against your approved baseline.
- 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 chars)
- 4.LLM-as-judge — secondary model scores output quality on a 1–5 scale
Model A runs entirely independently of production traffic. Zero impact on live operations.
03 — See it in action
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) 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.
04 — Why Quelm
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.
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.
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.
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. GDPR, HIPAA, SOC 2 — 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. We're not ready to publish the full technical architecture yet, but we are ready to show it to the right teams.
05 — Who it's for
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. 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. Quelm gives you the monitoring layer that turns "we think it's fine" into "we can prove it."
Regulated industries
Finance, legal, healthcare
For you, LLM output consistency is a compliance requirement, not a quality preference. Quelm's certification layer is designed specifically for structured outputs where silent errors have real consequences.
Be among the first teams to use it
Onboarding a small cohort. No commitment required to join the list.
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.
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 — we'd welcome a conversation.