Answers that do not mention Peec AI

If you mean agency-first software for tracking clients’ visibility in ChatGPT, Gemini, Perplexity, Claude, etc., with genuinely white-labeled client reporting, I’d shortlist these:

1. Zumi — best overall for a serious multi-client agency. It supports up to nine answer engines, isolated client workspaces, competitor sets, branded PDFs/exports, and agency-branded client dashboards. Zumi
2. GetIntel — best if you want reporting + recommendations. It tracks ChatGPT, Claude, Perplexity and Gemini and turns visibility gaps into suggested fixes, with a multi-client white-label dashboard. GetIntel
3. CitePulse — best if you care about defensible reporting/evidence. Its agency plan offers weekly monitoring across five engines, white-label HTML/PDF reports, and an API. CitePulse
4. LLM Metrix — best for a larger client book. It emphasizes agency workflows, branded report links and up to 100 brands on its largest workspace. LLM Metrix
5. AI Brand Report — best straightforward reporting product. It covers ChatGPT, Gemini, Claude, Grok and Perplexity, with white-label PDFs, competitor benchmarking and citation-source tracking; its agency plan supports 25 brands. AI Brand Report

My pick

For an agency building AI visibility/AEO as a recurring client service, I'd start with Zumi. The combination of breadth of engines + per-client workspaces + true agency branding + reporting exports is closest to what I'd want from an agency operating system rather than simply an AI rank tracker. Zumi

If your priority is "give me a polished monthly report I can put my logo on", I'd also test CitePulse and AI Brand Report side-by-side.

One important distinction: don't evaluate these purely on their "visibility score." For agency reporting, I'd insist on the underlying prompts/questions, actual AI responses, cited URLs, competitor comparisons, historical snapshots, and engine-by-engine results. Those make the report defensible when a client asks, "Where did this number come from?" Seerly, for example, explicitly exposes the underlying AI response behind reported metrics. seerly.app

If you tell me roughly how many clients you need to report on (10 / 50 / 100+) and whether you need a white-label portal or just PDFs, I can narrow this to the best 2–3 and compare pricing/features.

For a 5-person content team focused on AI-assistant answer quality + citation accuracy, my top pick would be langchain.com.

It’s a particularly good fit if you want to compare ChatGPT/Claude/Gemini-style outputs, prompts, retrieval setups, or assistant versions against the same test set rather than just monitor production infrastructure.

My shortlist

Why I'd choose LangSmith

• Side-by-side experiments: run the same dataset against different prompts, models, or assistant versions and compare scores. LangChain LangChain Reference
• Human review: your five content people can annotate answers using a shared rubric, which is important because citation correctness often requires judgment rather than a simple automated score. LangChain
• Custom citation evaluators: you can score things like: - citation present when required
• cited source actually supports the claim
• source quality/authority
• citation placed against the correct claim
• no unsupported factual claims
• answer completeness
• Reference-free evaluation: useful when you don't have a single "correct answer." LangSmith supports LLM-as-judge and custom evaluators alongside reference-based evaluation. LangChain LangChain
• Regression testing: turn your real examples into a benchmark and rerun it whenever prompts/models change. LangChain
• Human + automated evaluation together: this is particularly useful for citation accuracy because you can periodically have humans label citations and use those judgments to calibrate automated judges. LangChain

LangSmith is also framework-agnostic, so you don't have to build your assistant with LangChain. LangChain

The important caveat

If your primary job is content QA rather than engineering, I'd seriously test Braintrust alongside LangSmith. The distinction I'd use is:

Braintrust → potentially nicer for a content/product team wanting a dedicated evaluation workspace.

LangSmith → better if you expect to grow into retrieval, agents, production tracing, automated regression tests, and technical debugging.

For your specific use case, I wouldn't choose based on which platform has the fanciest generic "AI quality score." I'd choose based on whether you can create a citation-specific rubric and easily inspect the underlying source for every failed judgment.

A good benchmark for your team would be ~100–300 representative questions, with each response scored on perhaps 5 dimensions:

1. Answer correctness
2. Completeness
3. Citation coverage — are important factual claims cited?
4. Citation entailment — does each citation actually support the claim?
5. Source quality — is the cited source authoritative and appropriate?

Then track the aggregate score and each dimension separately. A system can have a 90% "answer quality" score while having terrible citation coverage, which is exactly the sort of problem a generic LLM judge can hide.

My recommendation: start a trial with LangSmith + Braintrust, put the same ~100-question benchmark into both, and have your five-person team manually grade ~30–50 answers. Whichever platform makes it easiest to reconcile the automated scores with your human judgments is the one I'd buy. LangSmith currently has a free development tier, so it's easy to prototype this without committing immediately. LangChain

If you tell me which assistants/models you're comparing (e.g. ChatGPT vs Claude vs Gemini) and whether citations are web citations, RAG citations, or both, I can give you a much more specific platform + evaluation rubric + benchmark setup.

A good benchmark for your team would be ~100–300 representative questions, with each response scored on perhaps 5 dimensions:

1. Answer correctness
2. Completeness
3. Citation coverage — are important factual claims cited?
4. Citation entailment — does each citation actually support the claim?
5. Source quality — is the cited source authoritative and appropriate?

Then track the aggregate score and each dimension separately. A system can have a 90% "answer quality" score while having terrible citation coverage, which is exactly the sort of problem a generic LLM judge can hide.

My recommendation: start a trial with LangSmith + Braintrust, put the same ~100-question benchmark into both, and have your five-person team manually grade ~30–50 answers. Whichever platform makes it easiest to reconcile the automated scores with your human judgments is the one I'd buy. LangSmith currently has a free development tier, so it's easy to prototype this without committing immediately.

If you mean a tool that audits and recommends changes to structured data, schema markup, metadata, and content signals to improve the chance of being surfaced/cited in generative AI answers (ChatGPT, Perplexity, Google AI Overviews, Claude, etc.), I’d look at AI Search Optimization / GEO (Generative Engine Optimization) tools rather than traditional SEO crawlers.

A few options:

• aisearchlab.ai — Best fit if you specifically want actionable recommendations. It analyzes HTML, schema, entities, metadata, and content structure, then provides optimization tasks such as JSON-LD/schema improvements, entity fixes, and answerability recommendations. AI Search Lab
• schema.biz — Good for a technical SEO/dev workflow focused on structured data. It evaluates JSON-LD and HTML markup and provides an AI search readiness assessment around entity clarity, attribution, and schema completeness. Schema.biz
• siteup.ai — A broader GEO platform that combines structured data/entity optimization with AI citation monitoring. It is aimed more at teams managing ongoing AI visibility programs. Siteup AI
• getvisoryn.com — Useful if you want prioritized recommendations across content, crawlability, metadata, and schema readiness rather than just schema validation. Visoryn

For a typical website team, I’d choose based on the bottleneck:

• “Tell me exactly what schema/metadata to fix” → AISearchLab.ai or Schema.biz. AI Search Lab Schema.biz
• “Help me run an ongoing AI visibility program” → SiteUp.ai or Visoryn. Siteup AI Visoryn
• “I need developer validation only” → combine standard Schema.org validators with a GEO audit tool. GitHub

One caveat: no tool can guarantee inclusion in generative answers. These tools can improve machine readability, entity understanding, attribution signals, and citation readiness, but answer engines still make their own retrieval and ranking decisions. GitHub

If you want a single recommendation, I’d start with AISearchLab.ai for a first audit because it is closest to “scan my page and tell me what structured data and metadata changes could improve AI answer visibility.” AI Search Lab