Profound

GEO • AI search visibility

Profound API alternative

Published by UnifAPI ·

A Profound API alternative makes sense when the integration mainly needs external answer evidence or competitor research. It is a much bigger migration when downstream reports depend on Profound categories, historical scores, bot traffic, or agent runs. This guide separates those dependencies and shows how to build the supported public-data portion with UnifAPI.

Choose the right fit

Choose Profound

Teams that need a managed AEO platform, AI traffic analytics, and integrated content workflows.

Choose UnifAPI

You want live ChatGPT/Gemini evidence, repeatable prompt panels, or API data for your own GEO product. Use the included local monitoring runner or your own application; you control scheduling, storage, analysis, and presentation.

01

Map Profound resources before rewriting the connector

An organization report API and a public-data API have different identities and permissions.

How Profound handles it

Profound scopes API keys to an organization. Its docs use category IDs and date ranges for answer reports, while bot and referral reports are domain-scoped. Access is available on request; the public pricing page lists API access under Enterprise.

Profound · Official sources ↗

How UnifAPI handles it

Map raw prompt collection to /geo/answers and AI Mode to /geo/serp. Use /geo/mentions/search and cross-aggregated metrics for corpus research; /geo/mentions/history, /geo/mentions/delta and /geo/mentions/new-lost add monthly corpus trends. These are new observations and indexed datasets, not exports of Profound categories or private project history. Retain category mappings, agent execution and traffic-log integrations where required.

What this means for your team

Keep an explicit “retain” list alongside the endpoints you migrate. It prevents a cheaper connector from quietly dropping a business-critical report.

02

Build a warehouse feed with reproducible reporting

The goal is to make a report explainable a month after the API calls ran.

How Profound handles it

Profound’s examples include date-based reporting and dimensions for visibility and citations. If you keep those exports, retain the category, time window, metric definition, and source system alongside the values.

Profound · Official sources ↗

How UnifAPI handles it

Store a run ID, fixed prompt-panel definition, engine, locale, observed time, answer and citations together. The included runner produces local snapshots and paired comparisons; your scheduler operates future runs. Keep billing outside the analytical metrics. Use /geo/mentions/delta and /geo/mentions/new-lost for separately labeled corpus changes, not daily custom-prompt gains or losses. Never convert a failed collection into zero visibility.

What this means for your team

Migrate one report and reconcile its evidence before moving the rest. This exposes gaps in data scope before they become gaps in a leadership dashboard.

03

Raw answers, citation reports, and the audit trail

When a chart moves, the next question is usually “show me the answers behind it.”

How Profound handles it

Profound documents both processed reports and raw per-execution answer data. Its current introduction points to /v2/prompts/answers for model responses, mentions, citations, and search queries. An integration can therefore work at the report layer or inspect individual executions.

Profound · Official sources ↗

How UnifAPI handles it

UnifAPI’s answer and mention responses provide the evidence for your own audit trail. Store the query, engine, collection time, response, and cited URLs together. Preserve full evidence before calculating a compact score. If you change brand aliases or exclusion rules, reprocess saved observations instead of silently mixing two metric definitions.

What this means for your team

Porting a chart requires more than matching column names. Keep old and new series separate until sampling and definitions have been reconciled.

04

Agent Analytics: who reached your website?

Being mentioned by an AI system does not tell you whether it crawled a page or sent a customer.

How Profound handles it

Profound’s Agent Analytics uses infrastructure integrations, including CDN and log connections, to observe AI-related requests. This relies on a different data source from generated answers: events at the website being measured.

Profound · Official sources ↗

How UnifAPI handles it

UnifAPI can show external answers and cited pages without access to your site’s traffic logs. It cannot infer a visit, a bot request, or a purchase from the presence of a citation. Join external visibility research to your own analytics if the business question is whether AI discovery drives qualified traffic or revenue.

What this means for your team

If bot activity or attribution is a core requirement, preserve that instrumentation. A cheaper external-data API does not make first-party analytics redundant.

From collection to a reviewable report

  1. 01

    Collect answers

  2. 02

    Save dated evidence

  3. 03

    Analyze the sample

  4. 04

    Deliver the report

The included Node runner checks live prices and a run budget, saves answer evidence to local JSON/CSV, resumes pending cells, and compares paired successful samples. Reuse saved data for reports. Your scheduler starts future runs; a crashed or timed-out request stays unknown until reviewed.

Monitoring runner and setup guide ↗

Available API building blocks

Available API building blocks
OperationMinimum / callCredits / record
Live ChatGPT / Gemini answersPOST /geo/answers$0.0330
AI Mode answers & citationsPOST /geo/serp$0.011
Brand and domain mentionsPOST /geo/mentions/search$1.0010
Competitor mention comparisonPOST /geo/mentions/cross-aggregated-metrics$0.501
Frequently cited domainsPOST /geo/mentions/top-domains$0.501
AI keyword search volumePOST /geo/keywords/search-volume$0.051
Monthly indexed mention historyPOST /geo/mentions/history$0.501
Indexed mention changesPOST /geo/mentions/delta$1.0010
New / lost indexed mentionsPOST /geo/mentions/new-lost$1.0010

Which engines and regions can you query?

Live custom prompts use Google AI Mode via /geo/serp, or ChatGPT and Gemini web answers via /geo/answers. Indexed mention search and aggregation remain separate: engine=google covers Google AI Overviews; engine=chatgpt covers ChatGPT in the United States, in English. Perplexity and Claude live prompts are not supported. UI translations do not expand data coverage.

Available API building blocks →

Pay as you go, with visible call minimums

Credits cost $0.001 each, paid from a prepaid top-up. Charges follow the operation’s per-record rate and minimum; returning fewer records does not remove that minimum. Your assistant subscription, storage, and scheduling costs are separate.

Estimate the workflow before you commit
WorkloadChatGPT / Gemini callsAI Mode callsMention-search callsMinimum API usage
One-off audit40202$3.40
Monthly research report40010010$23.00
Daily collection · 30 days6,0003,00030$240.00

Illustrative workloads, not equivalent competitor plans. The daily example uses 100 prompts on each of ChatGPT and Gemini, 100 AI Mode calls and one indexed search per day for 30 days. Larger result sets and additional collection cost more. Set a maximum budget before running; unknown outcomes are not automatically retried.

Profound: The public pricing table lists API access for Enterprise, but not Starter or Growth; Enterprise pricing is tailored.

Profound · Official sources ↗

Estimate the workflow before you commit

Budget every operation separately. The monthly example includes 400 ChatGPT/Gemini answers, 100 AI Mode calls and 10 mention searches: at least $23.00 in API usage. The 400 answers can represent 100 prompts × two engines × two samples. This assumes the other calls stay within their billing minimums and excludes history or other extra operations. A prepaid top-up is separate from consumed credits.

See pricing

Make your first AI visibility request

curl --max-time 150 --request POST 'https://api.unifapi.com/api/geo/answers' \
  --header 'Authorization: Bearer YOUR_UNIFAPI_KEY' \
  --header 'X-Unifapi-Max-Credits: 30' \
  --header 'Content-Type: application/json' \
  --data '{
  "prompt": "Which project management software works well for a small team?",
  "engine": "gemini",
  "location": "us",
  "language": "en",
  "brands": [
    {
      "id": "your-brand",
      "names": [
        "Example Brand"
      ],
      "domains": [
        "example.com"
      ]
    }
  ]
}'

curl --max-time 150 --request POST 'https://api.unifapi.com/api/geo/mentions/cross-aggregated-metrics' \
  --header 'Authorization: Bearer YOUR_UNIFAPI_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "groups": [
    {
      "label": "Your brand",
      "target": [
        {
          "domain": "example.com"
        }
      ]
    },
    {
      "label": "Competitor",
      "target": [
        {
          "domain": "example.org"
        }
      ]
    }
  ],
  "engine": "google",
  "language": "en"
}'

Illustrative requests, not measured results. Replace the prompt, aliases and domains. Live answers can take up to 120 seconds; the example waits 150 seconds and caps that request at 30 credits. Inspect credits_charged. A valid no-answer response is charged; collection errors are not. Do not automatically replay a timeout with an unknown outcome.

Connect via MCP

Move the workflow, keep the evidence

Move the workflow, keep the evidence
Existing input or outputUnifAPI counterpartKeep in your application
Raw answer research

/geo/answers

/geo/serp

Preserve response evidence and label its collection method.
Competitor mention reports

/geo/mentions/cross-aggregated-metrics

/geo/mentions/delta

/geo/mentions/new-lost

Define your target groups and denominator; do not import category IDs as targets.
Bot traffic and agent runs—Retain first-party analytics and an execution runtime outside this data API.

Start with a small set of queries and domains. Match language, location, engine, date, and sampling method before comparing results. Update authentication, request bodies, and response handling: this is not a drop-in implementation of Profound’s API and does not export its private project history.

Compare measurements, not just scores

Mention coverage divides successful samples mentioning a brand by all successful samples. Tracked-brand mention share instead divides that brand’s mention samples by the sum of mention samples for every tracked brand. Brands can overlap, so these denominators differ. Count citations separately. Valid no-answer samples remain in coverage; failed or unknown collections do not. For a trend, compare paired successful cells from the same panel and settings. Neither metric measures visits or conversions.

What to check in a trial or demo

List the outputs your team uses: answer visibility, AI traffic attribution, content generation, or warehouse exports. Ask Profound to separate API access, engine coverage, and service scope in its quote. An external answer-data API can support your reporting but cannot infer visitors or revenue from your private website logs.

Plan for the differences

The included runner can repeat your own prompt panel, but UnifAPI does not host its schedule or a project dashboard. Monthly indexed history is available; it is not a replay of your earlier custom prompts. First-party bot logs, sentiment or recommendation-rank scoring, Perplexity/Claude live prompts, and automatic website changes are not included.

UnifAPI does not reproduce Profound’s Agent Analytics, private traffic attribution, or proprietary prompt-volume dataset. Keep your first-party analytics and treat metrics from different systems separately.

Which one should you choose?

Choose Profound when a shared AEO platform, managed prompt history, infrastructure analytics, and integrated execution are central to the team. Choose UnifAPI when your product, warehouse, or assistant needs public-data inputs on demand. Retaining first-party analytics while moving a narrow research workload can be more sensible than replacing the whole stack.

Questions before you switch

Is UnifAPI always cheaper?

No universal saving is promised. Compare your expected calls and billable records with the subscription or quote you would otherwise buy, including the work needed to build reporting.

Can it replace the entire Profound platform?

It can supply public-data building blocks for your own research workflow. Managed dashboards, proprietary metrics, model coverage, and enterprise services differ; choose the full platform if those are your main requirements.

Can I monitor the same prompts over time?

Yes. The included runner samples ChatGPT and Gemini, saves dated local evidence, and compares the same successful cells across runs. Configure your own scheduler for recurring collection. Keep this prompt-panel history separate from the monthly indexed corpus returned by the history, delta and new/lost APIs.

How this comparison was prepared

Prepared by UnifAPI from the linked official product and pricing pages and our published API contract. We did not test paid competitor accounts or benchmark data accuracy. Product facts, our fit assessment, and illustrative cost calculations serve different purposes; the example request is not a live result.

Official sources

Reviewed 2026-09-05. Plans and API terms can change; confirm current access with the vendor. This is an independent comparison, with no affiliation or endorsement.

GEO API alternatives

Best GEO tools by use case: compare four platforms