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GroundLens and other tools

Most teams that ship LLM answers end up with three kinds of tool. They answer different questions, and GroundLens is only one of them.

Question	Kind of tool	Examples
How good is my system, on a test set, before release?	Evaluation framework	Ragas, DeepEval, promptfoo
What happened in production, and what did it cost?	Observability platform	Langfuse, Arize Phoenix, Grafana
May this answer be used, and can I prove why?	Runtime check with a record	GroundLens

When GroundLens is the right tool

The answers carry figures (amounts, rates, dates, units) taken from documents, and a wrong figure is expensive.
A real figure from the wrong company, contract or year is the error you fear most.
You need a decision per answer (PASS, REVIEW or FAIL), not a score you still have to interpret.
Someone else will audit the decisions: a risk team, a regulator, a customer. They need a signed record they can check without your software.
You already run an LLM judge and want fewer calls to it, and a trail that does not rest on its opinion.

Answers without figures, where the risk is tone, safety or style: use a judge or a guardrails library with those validators.
Measuring a system before release across many metrics: use an evaluation framework. GroundLens's measure answers one question only, the share of wrong answers among those that passed.
Checking facts against the world rather than against the passages you retrieved: GroundLens never looks beyond the passages.

	Evaluation frameworks (Ragas, DeepEval)	Guardrails libraries (Guardrails AI, NeMo Guardrails)	GroundLens
When it runs	Mostly offline, on a test set	At runtime	At runtime, and offline to replay
How grounding is judged	Mostly with an LLM as judge	Validators or rails; grounding checks mostly use a model	Exact checks on figures, scope and intent; a judge only if you add one
What it returns	Scores per metric	Pass, fix or block per validator or rail	PASS, REVIEW or FAIL under a versioned policy, with reasons
What it keeps	Results of the run	Logs, if you keep them	A signed, hash-chained record, stored before the decision returns
Same input, same result	Depends on the judge	Depends on the validator	Yes for the exact checks, on every OS

This table describes typical use. Each of these projects moves fast; check their documentation for what they do today.

With your evaluation framework: run GroundLens on the same test set, and its exact checks give a deterministic baseline next to the judged scores.
With Langfuse or Grafana: GroundLens writes each check into the trace that produced the answer, so the decision sits next to the model call.
With your judge: GroundLens runs first; a FAIL never reaches the judge, and the judge can escalate an answer, never pass one.

Install: pip install groundlens. Source: github.com/groundlens-dev/groundlens