ChatGPT answers, unedited

Yes. The category you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking—essentially a rank tracker for AI answers.

A few options stand out if API access for engineering is a requirement:

Honeyb is probably the closest fit if your engineering team wants to consume the data programmatically. Its API explicitly exposes the platform's AI-search visibility data over HTTP, including raw mentions and ranked recommendations. www.honeyb.aicitations.io

If what you mean by “answer placements” is specifically:

Given 500–10,000 prompts, tell me whether our company/product appeared in the answer, where it appeared, whether it was cited, which competitor appeared instead, and give me the raw answer + timestamp via API

then MentionsAPI or Honeyb look particularly relevant. MentionsAPI explicitly describes an API that maps citations back to the prompt and provider. mentionsapi.com

For a more complete marketing/GEO dashboard plus engineering integration, I'd shortlist Honeyb, SE Ranking, and Citations.io. SE Ranking is particularly interesting if you want to integrate visibility data into an existing SEO/search stack; its API is designed to expose structured AI-visibility data to your own applications. seranking.com

One important distinction: these services generally query the assistants/search engines repeatedly and measure the resulting answers. They're not giving you some hidden universal “ranking position” inside ChatGPT or Claude—AI answers are probabilistic, so the useful metric is usually visibility/citation rate + answer position + share of voice over a controlled prompt set and time period. www.honeyb.aicitations.io

If you tell me whether you're building (a) an internal monitoring system, (b) a customer-facing SaaS, or (c) an SEO/GEO analytics product, I can narrow this down to the best 2–3 APIs and compare their API coverage, pricing, rate limits, raw-answer access, prompt scheduling, and commercial/redistribution rights.

Yes. The category you’re looking for is generally called AI visibility tracking, GEO (Generative Engine Optimization) tracking, or answer-engine visibility.

If by “multi-assistant answer placements” you mean “For a fixed set of prompts, where does our company/product appear in ChatGPT, Gemini, Claude, Perplexity, etc., who appears above us, and what sources are cited?”, there are several platforms now doing this.

Strong candidates

• seranking.com — Probably the closest match if engineering/API access is a primary requirement. Its API exposes brand presence, citations, average position, share of voice, competitors, full AI answers, and scheduled tracking for custom prompts. It covers ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode. SE Ranking
• developer.meltwater.com — More enterprise-oriented. Its API is specifically designed to measure how brands are surfaced, cited and recommended across AI assistants including ChatGPT, Gemini, Perplexity and Copilot. Meltwater Developer Portal
• zumihq.com — Interesting if you want broad assistant coverage. It says it tracks up to nine engines, including ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Overviews/AI Mode and DeepSeek. Zumi
• kitbase.dev — More developer-oriented positioning, with scheduled prompts, per-engine results, presence rate, share of voice, competitor movement and cited-domain data. It supports up to ten answer engines on its Business offering. Kitbase
• genwolf.ai — Worth looking at if your engineering team prefers open-source/self-hosted infrastructure. It tracks prompts across multiple LLMs and is explicitly designed to be self-hostable. Genwolf

What I'd look for in an API

For an engineering integration, I wouldn't settle for a simple “visibility score.” Ideally the API should return something roughly like:

prompt
assistant
model
timestamp
answer
brand_mentioned
brand_position
competitors_mentioned
citations[]
  ├── domain
  ├── url
  └── position
share_of_voice

That lets you build your own warehouse/dashboard and calculate things like:

• Mention rate: % of answers mentioning you
• Recommendation rate: % recommending you
• Placement: median/average position in ranked answers
• Top-3 rate: % where you're in the first three recommendations
• Citation rate: % of answers citing your domain
• Share of voice: your mentions vs. competitors
• Assistant variance: ChatGPT vs. Gemini vs. Claude vs. Perplexity
• Citation-source map: which third-party sites are causing you to appear
• Historical movement: whether a content/SEO/PR change actually moved AI visibility

One important distinction: “track what assistants say” and “track what users actually see in the consumer assistant” aren't always identical. Some vendors query provider APIs, while others attempt to reproduce/search the consumer experiences. For serious measurement, I'd ask each vendor exactly which endpoint/product surface they're querying and whether they preserve the complete answer + citations.

If API access is the deciding criterion, I'd start with SE Ranking and Meltwater, then investigate Kitbase/Zumi depending on how many assistants you need. SE Ranking Meltwater Developer Portal Zumi Kitbase

If you tell me which assistants you need to track (e.g. ChatGPT + Claude + Gemini + Perplexity) and whether you need raw API data vs. just dashboard/export access, I can narrow this to the 3–5 best options and compare API availability, pricing, coverage, granularity, and engineering integration.

Yes. The category is usually called AI visibility / LLM visibility / GEO (Generative Engine Optimization) tracking. Several platforms now track the same prompts across multiple assistants and expose the underlying data through APIs.

A few worth evaluating:

Sellm is particularly aligned with what you're describing: its API exposes the AI-search tracking data so engineering teams can put it into their own dashboards, BI, alerts, or pipelines. Sellm

Searchable looks stronger if you need enterprise controls: it offers scoped API keys, project permissions, REST API, MCP, and coverage across ChatGPT, Gemini, Perplexity, Claude and Google AI. Searchable

DemandSphere is interesting if you want raw-ish visibility data rather than just a dashboard. Its API exposes mentions, citations, sentiment and full response data, including citation position and the prompt that generated the response. DemandSphere

And Reachd is worth a look if by "answer placements" you specifically mean "we're #2 in the recommendations, while Competitor X is #1". Its API explicitly returns recommendation order, competitors, cited sources, and the queries won/lost. Reachd.ai

What I'd look for technically

For an engineering team, I wouldn't choose based primarily on the dashboard. I'd verify that the API gives you:

• Prompt → raw assistant response
• Brand mention + exact position/order
• Competitor mentions and positions
• Citation URLs/domains
• Which assistant/model produced the answer
• Timestamp + geography/language
• Prompt-level historical results
• Share of voice / visibility calculations
• API/webhook access
• Bulk export to Snowflake/BigQuery/S3
• Ideally, the ability to define and execute your own prompt sets

That's important because "visibility = 72%" isn't nearly as useful to engineering as a dataset like:

prompt → engine → model → response → brands → positions → citations → timestamp → geography

If you tell me what you're trying to build (e.g. an internal dashboard, SaaS product, SEO/GEO monitoring system, or automated alerts), I can narrow this to 3–5 platforms with API quality, pricing, coverage, and data ownership/exportability.

Yes. What you’re describing is generally called AI visibility / LLM visibility / GEO (Generative Engine Optimization) tracking: a platform runs a controlled set of prompts across multiple assistants and tracks whether your company/product is mentioned, where it appears, competitors mentioned, citations, sentiment, and changes over time.

If API access for your engineering team is a key requirement, I’d shortlist these:

My strongest candidates

1. DemandSphere — probably the closest match if your requirement is "give engineering a clean data API." Their LLM API exposes mentions, citations, sentiment, and response data through REST/JSON endpoints, with filtering by platform, date, prompt category, etc. They also advertise webhooks. www.demandsphere.com

DemandSphere LLM Visibility API

2. Honeyb — interesting if you want to build a product/internal system around the data rather than just consume a dashboard. Their API exposes visibility, mentions, citations, share of voice, sentiment, recommendations, and crawler-access data. www.honeyb.ai

Honeyb API

3. Visiblee — worth looking at if your engineers are building AI-agent workflows. They explicitly offer API, MCP, and CLI, allowing agents or internal systems to pull visibility scores, model breakdowns, and tracked-prompt results. www.visiblee.ai

Visiblee

4. Ansvisor — compelling if you want more control. It's open source and can be self-hosted, while tracking prompts, mentions, citations, competitors, and AI traffic across a broad set of answer engines. github.com

Ansvisor on GitHub

What I'd look for in the API

Don't just buy a tool that returns a single "AI visibility score." For engineering purposes, I'd want the underlying records:

prompt
assistant / engine
model
timestamp
response
brand_mentioned
brand_position
competitors_mentioned[]
citations[]
sentiment
visibility_score
share_of_voice

That lets your team build something like:

Prompt → run across assistants → capture answer → extract placement → store evidence → calculate trends

rather than being locked into somebody else's dashboard.

One important distinction: tracking "answer placement" is not the same thing as tracking traffic from AI assistants. The former tells you "ChatGPT put us #2 in this recommendation"; the latter tells you "we received 1,240 visitors from ChatGPT." The better platforms increasingly provide both.

If you tell me whether you're looking for this for your own brand, or you're building a SaaS/API that will track placements for your customers, I can narrow this down substantially—the best choice is different for those two use cases.

Yes. What you’re describing is generally called AI visibility / answer-engine optimization (AEO) tracking: tracking whether a brand/product is mentioned, where it appears in an answer, which competitors appear, and which URLs are cited across ChatGPT, Claude, Gemini, Perplexity, etc.

A few platforms look particularly relevant if engineering needs API access, rather than just a marketing dashboard:

My shortlist for your use case would be Finseo, DemandSphere, and MentionsAPI.

If by "placements" you mean something more specific

I'd distinguish between:

1. Mention tracking — “Does ChatGPT mention Acme?”
2. Answer position — “Is Acme the #1 recommendation, #4, or merely mentioned?”
3. Citation tracking — “Which of our URLs did the assistant cite?”
4. Competitor placement — “Acme vs. Competitor X in the same answer”
5. Prompt-level history — “How did this answer change over time?”
6. Cross-assistant comparison — “ChatGPT says X, Claude says Y, Gemini says Z”
7. Raw answer access — useful if your engineering team wants to build its own scoring/analytics layer.

Finseo explicitly exposes metrics, competitor rankings, source rankings, attribution, and bulk exports through its API. www.finseo.ai

DemandSphere is particularly interesting if you need citation position and surrounding context, as its API exposes mentions, citations, and full response data, plus webhooks. www.finseo.aiwww.demandsphere.commentionsapi.comwww.tryprofound.com

MentionsAPI takes the more developer-centric approach: a single API can query multiple answer engines and return normalized brand position, sentiment, context, and cited URLs. mentionsapi.com

If you want self-hosting, Elmo is worth investigating: it's open source and tracks multiple answer engines, competitors, and citations on your own infrastructure. www.elmohq.com

What I'd build around the API

For an engineering team, I'd want the underlying record to look roughly like:

prompt
  ├── assistant/provider
  ├── model/version
  ├── timestamp
  ├── raw_answer
  ├── brand_mentioned
  ├── brand_position
  ├── competitors[]
  ├── citations[]
  │     ├── url
  │     ├── position
  │     └── context
  └── visibility_score

Then you can put your own dashboard/warehouse on top and calculate things like share of answer, citation share, competitor displacement, and visibility by prompt/category instead of being locked into somebody's UI.

If you're evaluating vendors specifically for an engineering integration, I'd start with Finseo + MentionsAPI + DemandSphere and compare their API schemas, rate limits, historical retention, pricing, and whether their "ChatGPT" data represents the actual consumer product experience versus an API/model simulation. That last distinction can materially affect how useful the placement data is. Profound, for example, explicitly says it captures answers directly from the browser rather than simply pulling model API responses. www.tryprofound.com

If you tell me what you mean by “placements” (e.g. product recommendations, brand mentions, citations, rankings, or all of them) and roughly how many prompts/queries you need to run per day, I can narrow this down to the best 3 APIs and compare pricing + coverage + API capabilities.

Yes. What you’re describing is usually called AI visibility tracking, Answer Engine Optimization (AEO), or Generative Engine Optimization (GEO): tracking where your brand/product appears in answers from ChatGPT, Claude, Gemini, Perplexity, etc.

A few platforms look particularly relevant:

• BotScope — specifically interesting for an engineering team. It tracks AI visibility across ChatGPT, Claude, Gemini, Google AI Overviews, Copilot and Perplexity, and now offers a read-only REST API and MCP server for pulling visibility data into your own stack. www.botscope.ai
• Finseo API — probably the most directly aligned if API access is a primary requirement. It advertises a public REST API with OpenAPI 3.0, metrics/competitor/source endpoints, and bulk exports for warehouses such as BigQuery, Snowflake, Redshift and Databricks. It covers ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews. www.finseo.aiwww.botscope.ai
• SearchFit — tracks AI visibility across assistants and explicitly exposes a REST API plus MCP server. Its API example includes share-of-voice, citations and assistant-level data. www.finseo.aisearchfit.aiwww.botscope.ai
• Storyzee — useful if you care about the actual answer evidence, rather than just a visibility score. It says it probes the engines through native APIs and tracks competitors, sources, sentiment and the exact answer evidence behind rankings. www.storyzee.com
• OpenLens — tracks seven platforms and keeps the underlying responses, with per-platform visibility, competitor and citation analysis. openlens.com

What I'd evaluate for an engineering integration

Don't just look for "AI visibility score." I'd want the platform to expose:

1. Prompt-level results — exact prompt → exact assistant → exact response.
2. Placement/ranking — e.g. "recommended #1," "#3 alternative," or not mentioned.
3. Citation/source data — which domains/pages caused the answer.
4. Competitor positions — ideally normalized across assistants.
5. Historical snapshots — so you can detect when an answer changes.
6. Raw API access — REST/OpenAPI rather than CSV-only exports.
7. Webhooks or scheduled exports — useful for triggering internal alerts.
8. Engine separation — don't collapse ChatGPT, Claude, Gemini, etc. into one opaque score.
9. Repeat sampling — LLM answers are nondeterministic, so one query shouldn't be treated as definitive placement.
10. Warehouse integration — BigQuery/Snowflake/etc. if you're building internal dashboards.

If you're building this into your own product/engineering stack, I'd start by comparing BotScope vs. Finseo. Finseo looks particularly API/warehouse-oriented, while BotScope looks particularly strong if you want an observability-style product with API + MCP. www.finseo.aiwww.botscope.ai

If by "multi-assistant answer placements" you mean something more specific—tracking where a product is ranked/recommended inside answers to thousands of commercial prompts—I can also map out the current platforms, their APIs, pricing, assistant coverage, and whether they provide raw answer-level data.

Yes. The category you’re looking for is usually called AI visibility tracking, LLM visibility monitoring, Generative Engine Optimization (GEO) analytics, or answer engine optimization (AEO) platforms. They track whether and where a brand/product appears in answers from multiple assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) and some provide APIs for engineering teams. www.storyzee.comapi-docs.lumar.io

Some options with API access:

For an engineering team, I’d evaluate them on these API capabilities:

• Prompt-level tracking: store the exact questions being tested.
• Raw answer capture: keep the assistant response, not just a score.
• Placement extraction:
  • mentioned / not mentioned
  • rank position (“#1 recommendation”, “listed alternative”)
  • sentiment or framing
  • citations/sources used
• Historical snapshots: compare changes over time.
• Webhook/export support: push into Snowflake, BigQuery, Looker, Datadog, Slack, etc.
• Multi-brand/project support: useful if you manage many products or customers.

A typical engineering architecture would look like:

AI visibility platform API
          |
          v
 ingestion service
          |
          +--> warehouse (Snowflake/BigQuery)
          |
          +--> internal dashboard
          |
          +--> alerts ("competitor replaced us in ChatGPT answers")

If you’re building an internal product rather than just buying a dashboard, I’d prioritize platforms that expose raw prompt runs + raw answers + citations, not only aggregate visibility scores. www.finseo.aiapi-docs.lumar.io

I can also help compare these specifically for enterprise API usage, pricing, rate limits, and building a custom AI answer-placement tracker.

Yes. This category is usually called AI visibility tracking, AEO/GEO, or answer-engine monitoring. If by “answer placements” you mean where your brand/product appears in answers from ChatGPT, Claude, Gemini, Perplexity, etc., including citations and competitors, there are several options.

Best fits for an engineering team

Meltwater explicitly exposes an AI Visibility API for measuring brand surfacing, citations and recommendations across AI assistants. developer.meltwater.comkeyword.com

Foglift is probably the closest match to your wording: it offers a public REST API, CLI and MCP server, and says the API exposes the same AI-search visibility data as its dashboard. foglift.io

Keyword.com is another particularly relevant option if you want to pipe the measurements into your own data warehouse/dashboard. Its API exposes visibility scores, brand presence, sentiment, competitor benchmarks and cross-engine performance. keyword.com

If you're willing to self-host, Ansvisor and Elmo are worth investigating. Ansvisor describes itself as cloud-ready/open-source with APIs and MCP, while Elmo is explicitly self-hostable and records mentions, competitors and cited sources across multiple answer engines. www.ansvisor.com

What I'd evaluate before choosing

For an engineering integration, I'd specifically test whether the platform gives you raw answer-level data, rather than only a proprietary “visibility score”:

• Prompt/query
• Assistant/model
• Timestamp
• Full answer or answer snapshot
• Brand mention + position/order
• Competitor mentions + positions
• Citation URLs
• Citation position/order
• Sentiment / recommendation
• Model/version where available
• Geographic/language context
• Historical runs
• API/webhooks
• Rate limits and retention
• Ability to submit your own prompts programmatically

That's important because “we appear in 42% of answers” is much less useful to engineering than something like:

prompt → model → answer → placement → cited URL → competitor placements → timestamp

My shortlist for your use case would be Foglift, Keyword.com, Meltwater, and Ansvisor/Elmo depending on whether you prioritize API simplicity, enterprise data infrastructure, or owning the stack.

If you tell me roughly how many prompts/brands you need to track per day and whether you want raw answers vs. aggregated metrics, I can narrow this down to 2–3 platforms and compare their APIs/pricing.

Yes. What you’re describing is generally called AI visibility / AEO / GEO tracking: a platform repeatedly runs a set of prompts against multiple AI assistants, then records mention rate, answer position/rank, citations, competitors, sentiment, and changes over time.

For an engineering team that specifically needs API access, I’d shortlist these:

My top picks

1. Finseo — probably the closest match to what you're asking for.

Its API exposes projects, prompts, metrics, competitors, sources, attribution and bulk exports across ChatGPT, Claude, Perplexity, Gemini and Google AI Mode. It specifically supports feeding the data into BigQuery/Snowflake/Redshift, which is useful if your engineering team wants to build its own internal reporting layer. www.finseo.ai

Finseo API documentation

2. DemandSphere — strongest if you want a mature visibility API.

Its LLM API exposes mentions, citations, full responses, sentiment, competitors and webhooks, with filters by platform, prompt category and date. That's particularly useful if you want your own application to consume events rather than just periodically export reports. www.demandsphere.com

DemandSphere LLM API

3. cloro — worth looking at if API-first is the priority.

It positions itself as raw AI/LLM visibility data rather than primarily a dashboard. It provides a common API across six engines and returns the underlying visibility signals, which could make it attractive if you're building your own measurement infrastructure. cloro.dev

cloro AI visibility API

4. CitationBench — interesting if you want an engineering-oriented integration.

It has a REST API, hosted MCP server and official TypeScript SDK, plus multi-workspace support. Its citation endpoint records brand citation, rank position and share of voice, with historical tracking. www.citationbench.com

CitationBench

If you want to own the infrastructure

Ansvisor and Elmo are interesting because they're open source. Ansvisor tracks prompts, citations, competitors and visibility across a fairly broad set of assistants; Elmo can be self-hosted so your team controls the measurement pipeline and infrastructure. github.com

Ansvisor on GitHub

What I'd evaluate before choosing

The important distinction isn't just "does it track ChatGPT?" I'd ask each vendor whether their API gives you the raw observation, not merely an aggregated score:

prompt
  ↓
assistant/model
  ↓
raw answer
  ↓
brand mentioned?
  ↓
brand position/rank
  ↓
cited URL(s)
  ↓
competitors mentioned
  ↓
sentiment / recommendation
  ↓
timestamp + run ID

You also want repeatable historical runs, because LLM answers are stochastic. For example, Profound explicitly says it runs tracked prompts daily and captures answers directly from the browser rather than simply relying on model APIs. www.tryprofound.com

If your goal is to build an internal engineering product around this data, I'd start by evaluating Finseo vs DemandSphere vs cloro. If you tell me roughly how many prompts × assistants × runs/day you need, I can compare their APIs, pricing, coverage, data model, and likely architecture for your use case.

Yes. What you’re describing is generally called AI visibility / GEO / AEO tracking: tracking where your brand/product appears in answers from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, etc., including mention position, citations, competitors, prompts, and answer text.

A few platforms look particularly relevant if your engineering team needs API access, rather than just a marketing dashboard:

DemandSphere is probably the closest match to your description. Its LLM API exposes mention, citation, sentiment, and full-response data through REST endpoints, including the prompt that triggered a citation and citation position. www.demandsphere.com DemandSphere LLM API

cloro is interesting if you want to build your own internal analytics layer. It explicitly positions itself as the raw API layer rather than just a hosted visibility dashboard, with a common API across ChatGPT, Perplexity, Gemini, Copilot, AI Mode/Overviews, and Grok. cloro.dev cloro API

Elmo is worth investigating if your engineering team wants control of the data pipeline. It's open source and self-hostable, and records mentions, competitors, and cited sources across several AI answer engines. www.elmohq.com Elmo

And Visiblee is unusually relevant if you want the visibility data to be consumed by your own AI agents: it offers API, CLI, and MCP access in addition to its dashboard. www.visiblee.ai Visiblee

What I'd look for in your case

If by "answer placements" you mean something like:

Prompt: "What's the best platform for X?"
ChatGPT → Your company #2, cited from URL A
Claude → Your company #1, cited from URL B
Gemini → Not mentioned, competitor C #1
Perplexity → Your company #3, cited from URL D

…and you want your engineering team to pull that into your own warehouse/dashboard, I'd prioritize a platform that provides:

1. Raw answer/response data
2. Prompt-level history
3. Brand/competitor detection
4. Position/rank within answer
5. Citation URLs + citation position
6. Engine/model metadata
7. Scheduled re-runs
8. REST API and/or webhooks
9. Regional/language variation
10. Stable IDs so you can diff answers over time

That is more useful than a single proprietary "AI visibility score."

If you're building an internal product around this, I'd shortlist DemandSphere vs. cloro vs. Elmo first. The key architectural question is whether you want to buy the measurement layer or build the measurement layer yourself.

Yes. What you’re describing is generally called AI visibility / Answer Engine Optimization (AEO) tracking: a platform that runs a controlled set of prompts across multiple assistants, records whether/how your company appears, its position, competitors, and the sources/citations used.

A few options worth looking at:

Meltwater is particularly relevant if your requirement is "our engineering team needs programmatic access to the measurements" because it explicitly exposes an AI Visibility API, rather than only offering a dashboard. Its API is designed around measuring how brands are surfaced, recommended, and cited in AI answers. developer.meltwater.com

Meltwater AI Visibility API documentation

If you want something your engineers can actually own and run internally, Elmo is interesting. It's open source and self-hostable and tracks multiple answer engines, including ChatGPT, Gemini, Perplexity, Copilot, Grok, and Google AI Overviews. www.elmohq.com

Elmo — open-source AI visibility platform

What I'd evaluate for your use case

If you're building this into an engineering/data stack, I would insist on these capabilities:

• Prompt-level API — submit/maintain your own prompt corpus.
• Raw answer retrieval — not just a proprietary "visibility score."
• Position tracking — e.g. mentioned #1, #2, #3, etc.
• Mention + recommendation detection — being mentioned isn't necessarily being recommended.
• Citation/source extraction — which URLs/domains caused the answer.
• Competitor tracking — share of answer / share of voice.
• Historical runs — model/provider/prompt/timestamp/version.
• Multiple sampling runs — LLM answers are stochastic, so one query isn't a reliable measurement. Current tools are increasingly using repeated/daily runs for this reason. www.rank.ai
• Raw API access or webhooks — so your own warehouse can become the source of truth.
• Provider/surface distinction — ChatGPT API results aren't necessarily equivalent to what users see in the ChatGPT consumer product. NYFTY explicitly notes this distinction. nyftylabs.com

One important distinction: "track what the assistants say" and "track which AI crawlers visit our site" are different products. You want the former if the goal is answer placement/visibility.

If you tell me whether you're tracking your own brand, clients/competitors, or building this as a product, I can narrow this down to the 3–5 platforms with the strongest API/data-access story, including pricing and API documentation.

Yes. What you’re describing is generally called AI visibility / Answer Engine Optimization (AEO) tracking: a platform runs a defined prompt set across multiple assistants, captures the answers, and measures things like mention rate, answer position, citations, competitors, and share of voice.

A few options worth evaluating:

• Elmo — particularly interesting for an engineering team. It’s open source/self-hostable, tracks ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, etc., and has a developer/API path. You control the infrastructure and data. www.elmohq.comwww.elmohq.com
• Menra — more enterprise-oriented; tracks multiple engines and explicitly captures which position your brand appeared in and which sources were cited. www.menra.ai
• EvidentlyAEO — tracks ChatGPT, Gemini, Perplexity, Claude, Copilot and others, with share-of-answer and citation analytics. evidentlyaeo.com
• Genwolf — focuses on scheduled multi-LLM prompt tracking, answer history, mentions, citations, sentiment, and source domains. www.genwolf.ai

If API access is the important part

I'd narrow the shortlist to Elmo vs. a commercial enterprise platform.

Elmo is compelling if your engineers want to integrate the underlying data into your own warehouse/dashboard. Its documentation specifically describes hitting its API and self-hosting the stack. www.elmohq.comwww.elmohq.com

The architecture I'd look for is roughly:

Your prompt library
       ↓
Multi-assistant execution
       ↓
┌──────────────────────────────┐
│ ChatGPT │ Claude │ Gemini    │
│ Perplexity │ Copilot │ Grok  │
└──────────────────────────────┘
       ↓
Raw answer + citations
       ↓
Structured extraction
       ↓
Your API / warehouse
       ↓
Placement + visibility metrics

And I'd make sure the API exposes raw answer/response data, not just a proprietary "visibility score." For engineering purposes, useful fields would be:

prompt → engine → model → timestamp → answer → mentioned_entities → position → cited_urls → competitors → sentiment → run_id

That's important because results can differ substantially between assistants—even for the exact same prompt—so a single aggregate score can hide useful information. www.reddit.com

If you tell me whether you're tracking your own company's placement, your customers' brands, or building this capability into your product, I can narrow this to the 3–5 platforms with the best API/data-access story, including pricing and API documentation.

Yes. What you’re describing is generally called AI visibility / Answer Engine Optimization (AEO/GEO) tracking: monitoring whether your company/product is mentioned, recommended, ranked, or cited inside answers from multiple AI assistants.

A few platforms worth evaluating:

• Profound — enterprise-oriented AI search/answer visibility monitoring.
• Peec AI — tracks brand mentions, citations, competitors, and visibility across AI search engines.
• Otterly.AI — more lightweight monitoring of brand presence across AI answers.
• Semrush AI Visibility Toolkit — useful if you already use Semrush; combines AI visibility with traditional SEO data.
• Genwolf — interesting if engineering ownership matters: it says it runs daily prompts across ChatGPT, Gemini and Perplexity and has an open-source/self-hostable core. www.genwolf.ai
• Storyzee — particularly interesting for your use case because it explicitly probes ChatGPT, Perplexity, Gemini, Claude and Grok through their APIs and preserves the underlying answers/evidence. www.storyzee.com

The important distinction for engineering

If by “provide API access” you mean we want to pull the raw placement data into our own data warehouse/application, I would make API access a hard selection criterion.

Ideally the platform should expose something like:

GET /projects/{id}/prompts
GET /runs?engine=chatgpt
GET /answers/{run_id}
GET /mentions?brand=Acme
GET /citations
GET /competitors

with raw answer-level data such as:

{
  "engine": "chatgpt",
  "prompt": "What are the best CRM platforms for startups?",
  "timestamp": "2026-08-11T...",
  "brand_mentioned": true,
  "position": 2,
  "sentiment": "positive",
  "citation": true,
  "competitors": ["HubSpot", "Salesforce"],
  "sources": ["example.com", "reddit.com"]
}

That matters because AI answers aren't a stable SERP ranking. The same prompt can produce different answers at different times, and research is finding substantial cross-engine and run-to-run variability. arxiv.org

So I wouldn't buy a platform solely because it gives you a single “AI visibility score.” For an engineering integration, I'd prioritize:

1. Raw answer/API access
2. Multiple runs per prompt rather than one snapshot
3. ChatGPT + Gemini + Claude + Perplexity + AI search/overviews
4. Exact citations/sources behind each answer
5. Competitor placement
6. Prompt-level historical data
7. Webhooks or scheduled exports
8. API rate limits and retention
9. Ability to create your own prompts/programmatically manage them
10. Engine-specific metrics, rather than averaging everything into one score

If you tell me whether you're looking to buy an existing platform or build this capability into your own product, I can narrow this down to the 3–5 strongest options and specifically compare their APIs, raw-data access, supported assistants, pricing, and engineering integration model.

Yes. What you’re describing is generally called AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) tracking: monitoring how often your brand/product appears in answers from ChatGPT, Perplexity, Gemini, Claude, Copilot, etc., including mention position, citations, competitors, and the underlying URLs.

A few platforms worth evaluating:

DemandSphere looks particularly aligned with your requirement. Its LLM Visibility API exposes mention, citation, sentiment, and response data through REST endpoints, including the raw response, cited URL, citation position/context, prompt, platform, competitors, and metadata. www.demandsphere.com

urlDemandSphere LLM APIturn0search6

If your engineering team wants to build this into an internal dashboard/data warehouse, I'd also look closely at Elmo because it's open source and self-hostable, so you aren't necessarily locked into a SaaS reporting layer. www.elmohq.com

urlElmo — open-source AI visibility platformturn0search2

One important caveat: “placement” isn't quite equivalent to Google rank. AI answers are nondeterministic; the same prompt can produce different citations on different runs. Recent research specifically recommends treating visibility as a distribution and reporting uncertainty rather than treating one answer as a fixed rank. arxiv.org

If you're evaluating vendors, I'd require these API capabilities

1. Submit/store your own prompt set
2. Run prompts across multiple assistants/models
3. Store the complete raw answer
4. Extract brand/entity mentions
5. Record mention position / share of answer
6. Extract every citation and cited URL
7. Track competitor mentions
8. Track results historically by prompt + engine + model
9. REST API and/or webhooks
10. Export raw data to your warehouse
11. Ideally, expose the exact model/configuration and timestamp used for each observation

If by “answer placements” you mean something more specific—e.g. “we want to know whether our product is recommended in response to 10,000 commercial prompts across ChatGPT, Claude, Gemini, Perplexity, etc., and pull all those results into our own API/database”—I can narrow this down to the 3–5 best platforms and compare their API coverage, pricing, and architecture.

Yes. The category is usually called AI visibility tracking, LLM answer monitoring, AI search optimization (AISO/AEO), or generative engine optimization (GEO). These platforms typically track whether your company/product appears in answers from multiple assistants (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.), which sources are cited, competitor mentions, and share of voice. corank.ai

Some options with engineering/API capabilities:

foglift.io

For an engineering team, I would evaluate them on:

1. Raw answer capture
   • Can you retrieve the full assistant response?
   • Are citations/sources stored?
   • Can you replay historical prompts?

2. Prompt management
   • API for creating prompt sets
   • Scheduling/rerunning tests
   • Regional/language variants

3. Metrics
   • Mention rate
   • Ranking/placement in answers
   • Citation share
   • Competitor comparisons
   • Sentiment or recommendation strength

4. Data export
   • REST API
   • Webhooks
   • CSV/JSON export
   • Warehouse integrations (Snowflake, BigQuery, etc.)

One caveat: there is no universal “rank tracker” equivalent to Google Search Console for all AI assistants. Most vendors measure visibility by repeatedly running controlled prompts and analyzing the resulting answers, citations, and mentions. arxiv.org

If your goal is building an internal engineering dashboard, I’d lean toward an API-first provider (Finseo, Foglift, Vizara, or similar). If your goal is marketing/SEO reporting, a dashboard-first tool like Corank or Prism-style products may be faster. www.finseo.ai

I can also help compare these against building your own stack using OpenAI/Anthropic/Google/Perplexity APIs if that is what your engineering team is considering.

Yes. The category you're looking for is usually called AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO).

If your engineering team specifically needs programmatic access, I'd shortlist these:

What I'd look for in your case

If by "answer placements" you mean something like:

For 500 target prompts, tell me whether our company appeared in ChatGPT, Claude, Gemini, Perplexity, etc., where we appeared in the answer, who appeared ahead of us, what sources were cited, and give our engineers the raw responses via API

then DemandSphere and Vizara are the most interesting starting points.

DemandSphere explicitly exposes endpoints for mentions, citations, and full AI responses, including citation position, prompt, platform, competitor mentions, and metadata. www.demandsphere.com

Vizara is particularly interesting if you're building an engineering workflow around the data because it advertises a REST API, TypeScript SDK, and HMAC-signed webhooks. vizara.ai

One important caveat: there isn't really a universal "Google Search Console for all AI assistants." Vendors generally have to re-run controlled prompts against the different AI/search systems and analyze the resulting answers. Because answers are nondeterministic, a single query isn't a reliable ranking measurement; repeated sampling matters. arxiv.org

If you tell me what you mean by "placements" (e.g. ranked #1/#3 in recommendations, citation position, mention/no mention, share of voice, etc.), I can give you a more rigorous comparison of 5–10 platforms, including API endpoints, supported assistants, pricing, webhooks/SDKs, and whether you can ingest the raw answer data into your own stack.

Yes. This is becoming its own category of software, although it's still relatively early. If your goal is to monitor how your company appears across ChatGPT, Claude, Gemini, Perplexity, Copilot, and other AI assistants—and give your engineering team API access—there are several approaches.

If you're looking for something your engineering team can build into internal tooling, the key features to evaluate are:

• Programmatic querying
  • REST API
  • Bulk prompt execution
  • Scheduled runs
  • Authentication (API keys or OAuth)

• Multi-model support
  • ChatGPT
  • Claude
  • Gemini
  • Perplexity
  • Microsoft Copilot
  • Grok (if relevant)

• Response tracking
  • Whether your brand appears
  • Citation sources
  • Rank/order within responses
  • Competitor mentions
  • Changes over time

• Version history
  • Daily snapshots
  • Diffing between responses
  • Alerting when answers change

• Engineering integrations
  • Webhooks
  • Data export (JSON/CSV)
  • Warehouse connectors
  • BI integrations

A typical architecture many teams adopt looks like this:

Scheduler
      │
      ▼
Prompt Library
      │
      ▼
Multi-model Runner
(ChatGPT, Claude, Gemini...)
      │
      ▼
Normalizer
      │
      ▼
Storage
      │
      ├── Dashboard
      ├── Alerts
      └── Internal API

If you need full control, another option is to build your own system using each provider's APIs. This gives you:

• complete ownership of prompts and results,
• custom scoring logic (brand visibility, sentiment, citation quality),
• historical tracking,
• and an internal API tailored to your needs.

The trade-off is that you'll need to maintain integrations as providers update their APIs and models.

If you're evaluating vendors, the questions I'd ask are:

1. Do they execute prompts against the live assistants or simulate results?
2. How often are rankings refreshed?
3. Can you retrieve raw responses via API?
4. Is there an API for historical data and diffs?
5. How are citations and sources normalized across assistants?
6. Can you upload thousands of prompts and automate recurring checks?

For an engineering-focused use case, platforms like Profound and Scrunch AI are generally the closest fit because they emphasize APIs and enterprise integrations rather than just marketing dashboards.

Yes. The category you’re describing is usually called LLM visibility tracking, AI answer monitoring, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO). These platforms track how often a brand/product/content appears in answers from multiple AI assistants (for example ChatGPT, Claude, Gemini, Perplexity, Copilot), capture citations and competitors, and in some cases expose APIs for engineering integration. www.demandsphere.comwww.elmohq.com

Some options:

For an engineering team, I’d evaluate them based on whether they provide:

• Prompt registry — store a canonical set of user queries you care about.
• Multi-model execution — run the same prompt against multiple assistants.
• Raw answer capture — save the full response, not just a score.
• Entity extraction — detect your brand, competitors, products, people, URLs.
• Citation tracking — which sources were referenced and where.
• Historical diffing — “we appeared 40% less often after this date.”
• API/webhooks — push data into your warehouse, BI tools, or internal dashboards.
• Model/version metadata — important because answers change over time.

A common internal architecture is:

Prompt catalog
      ↓
AI assistant runners
(ChatGPT / Claude / Gemini / Perplexity APIs)
      ↓
Response archive
      ↓
Entity + citation extraction
      ↓
Visibility scoring
      ↓
Data warehouse / dashboards

If you are building this internally, you can also combine model APIs directly with an observability layer such as OpenAI API Platform, Anthropic API, and other model providers, then create your own scoring system. The advantage is control over metrics; the disadvantage is maintaining crawlers, prompt runs, normalization, and reporting.

For a large engineering team, I would shortlist:

1. API-first vendor (DemandSphere-style) if you want to ingest into existing systems quickly. www.demandsphere.com
2. Open-source/self-hosted approach (Elmo-style) if data ownership and customization matter. www.demandsphere.comwww.elmohq.com
3. Build your own layer if this is a core strategic capability.

A useful clarification: are you trying to track your company/product appearing in AI answers (GEO/AEO), or your own AI assistants’ answer quality/placement inside an application (LLM observability/evaluation)? The platforms are different.

Yes. If by “multi-assistant answer placements” you mean tracking where your brand/product/content appears inside answers from ChatGPT, Claude, Gemini, Perplexity, Copilot, etc. (often called AI visibility, LLM visibility, AEO/GEO tracking), there are platforms that do this and some provide APIs for engineering teams. www.searchinsight.aiwww.demandsphere.com

A few options:

For an engineering team, I would evaluate based on:

• Raw response capture: Can you retrieve the actual assistant answer, not just a score?
• Prompt management API: Can you define your own test suite of customer questions?
• Provider coverage: ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, AI Overviews, etc.
• Historical snapshots: Can you diff answer changes over time?
• Webhooks/data export: Can you push events into your warehouse (Snowflake, BigQuery, Datadog, etc.)?
• Authentication and rate limits: Important if you plan to build internal dashboards.

A common architecture is:

AI visibility platform
        ↓ API
Prompt/result collector
        ↓
Data warehouse
        ↓
Internal dashboard + alerts

If you mean tracking placement of answers generated by your own multi-assistant product (for example, routing the same query through OpenAI/Anthropic/Google models and measuring ranking/quality), that is a different category—more like LLM evaluation/observability platforms. I can list those as well.

Yes. There are platforms emerging for AI answer visibility tracking (often called AI search optimization, AEO — Answer Engine Optimization, or LLM visibility monitoring) that track where and how brands appear across multiple AI assistants and provide APIs or data exports for engineering teams.

A few categories to look at:

Examples:

• Profound — focuses on measuring brand visibility in AI-generated answers and tracking how brands appear across AI search experiences. Offers enterprise-oriented analytics and integrations. Profound

• Peec AI — tracks brand presence across AI assistants and answer engines, including prompt monitoring and competitive comparisons. Peec AI

• Semrush — has expanded into AI visibility tracking alongside its SEO tooling. It provides APIs for many enterprise workflows. Semrush

• BrightEdge — enterprise search intelligence platform with AI search visibility capabilities and integrations. BrightEdge

For an engineering team, I’d evaluate whether the platform exposes:

• Prompt-level tracking API
  • Example: “For prompt X, what position/citation did our company receive?”
• Assistant coverage
  • ChatGPT, Claude, Gemini, Perplexity, Copilot, AI Overviews, etc.
• Historical snapshots
  • Track movement over time
• Citation extraction
  • Which URLs/sources were referenced
• Webhook/event support
  • Trigger internal alerts when visibility changes
• Raw response storage
  • Useful for building internal dashboards
• Rate limits and enterprise data access

If you are building an internal system, a common architecture is:

1. Store a library of target prompts.
2. Run scheduled evaluations across assistants.
3. Extract:
   • brand mention
   • rank/position
   • cited URLs
   • competitors mentioned
   • sentiment/context
4. Push results into your data warehouse.
5. Build dashboards in tools like Looker, Tableau, or Grafana.

One caveat: there is not yet a universal “Google Search Console for all AI assistants.” Each platform measures visibility differently, and some rely on controlled querying rather than direct access to assistant internals.

If you’re looking for enterprise API-first tooling, I’d prioritize vendors that expose raw data access rather than only dashboards.

Yes. A new category of tools has emerged around AI answer visibility, LLM optimization (LLMO/GEO), and agent analytics. They vary quite a bit depending on whether you want marketing insights ("how often does ChatGPT mention my brand?") or engineering-focused APIs for tracking and experimentation.

For an engineering team looking to monitor multiple AI assistants and integrate the data into internal systems, here are the strongest options:

If your engineers want raw programmatic access

The most useful capabilities are:

• Query multiple assistants from one workflow
• Store historical responses
• Track answer changes over time
• Compare competitors
• Run prompts on a schedule
• Export structured JSON
• Webhooks or REST APIs
• Team authentication and permissions

Some platforms expose this directly, while others are primarily dashboard products with enterprise APIs.

If you're willing to build your own

Many companies assemble an internal system using:

• Official APIs from model providers (OpenAI, Anthropic, Google)
• Scheduled prompt execution
• Response normalization
• Embedding similarity to detect answer drift
• Time-series storage
• Internal dashboards (Grafana, Metabase, Looker)

Advantages:

• Full control
• Lower cost at scale
• Custom metrics
• Ability to test hundreds or thousands of prompts

Disadvantages:

• Doesn't capture the exact consumer experience in products like ChatGPT Search or Perplexity's UI, which may use retrieval systems, personalization, or other components beyond the raw APIs.

If you're interested in "answer placement"

If by "placement" you mean questions like:

• "Does ChatGPT recommend us?"
• "Are we ranked #1 or #5?"
• "Which competitors appear alongside us?"
• "What citations are shown?"
• "Which prompts trigger our brand?"

then you're looking for what's commonly called Generative Engine Optimization (GEO) or AI Search Analytics. These platforms periodically execute prompts across assistants, extract entities, rankings, citations, sentiment, and competitors, then expose that data through dashboards and APIs.

Recommendation by use case

• Engineering-first API: Profound is one of the more mature options if API access and automation are priorities.
• Marketing + product teams: Goodie AI or Peec AI provide strong visibility dashboards with collaboration features.
• Custom analytics platform: Build on model APIs if you need complete control over prompts, storage, and metrics.

If your goal is observability for hundreds or thousands of prompts across ChatGPT, Gemini, Claude, and Perplexity with an API for internal tooling, I can also recommend an architecture and vendors based on your expected query volume and budget.