Datasets:

flatcircle
/

public-company-geo-visibility-v1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
buyer_type: string
category_id: string
category_name: string
models_scored: list<item: string>
  child 0, item: string
private_or_noncovered_spoiler_mentions: struct<databricks: int64, pipedrive: int64, zoho_crm: int64, google_travel_surface: int64, bamboohr: (... 38 chars omitted)
  child 0, databricks: int64
  child 1, pipedrive: int64
  child 2, zoho_crm: int64
  child 3, google_travel_surface: int64
  child 4, bamboohr: int64
  child 5, gusto: int64
  child 6, rippling: int64
prompt_ids_scored: list<item: string>
  child 0, item: string
providers_scored: list<item: string>
  child 0, item: string
public_vendor_count: int64
response_origins: list<item: string>
  child 0, item: string
responses_scored: int64
top_public_vendor_company: string
top_public_vendor_id: string
top_public_vendor_score_0_to_100: double
top_public_vendor_ticker: string
scoring: struct<position_scores: struct<1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, (... 132 chars omitted)
  child 0, position_scores: struct<1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64>
      child 0, 1: int64
      child 1, 2: int64
      child 2, 3: int64
      child 3, 4: int64
      child 4, 5: int64
      child 5, 6: int64
      child 6, 7: int64
      child 7, 8: int64
  child 1, position_score_for_9_or_later: int64
  child 2, unranked_mention_score: int64
  child 3, absent_score: int64
  child 4, category_score_method: string
version: string
categories: list<item: st
...
pi: string, env_key: string, model: string, temperature: (... 120 chars omitted)
          child 0, provider_id: string
          child 1, enabled: bool
          child 2, api: string
          child 3, env_key: string
          child 4, model: string
          child 5, temperature: double
          child 6, max_output_tokens: int64
          child 7, store: bool
          child 8, env_key_alternates: list<item: string>
              child 0, item: string
          child 9, anthropic_version: timestamp[s]
  child 7, prompt_variants: list<item: struct<prompt_id: string, template: string, applies_to_buyer_types: list<item: string>>>
      child 0, item: struct<prompt_id: string, template: string, applies_to_buyer_types: list<item: string>>
          child 0, prompt_id: string
          child 1, template: string
          child 2, applies_to_buyer_types: list<item: string>
              child 0, item: string
run_design: struct<response_input_file: string, response_origin_allowed_values: list<item: string>, recommended_ (... 75 chars omitted)
  child 0, response_input_file: string
  child 1, response_origin_allowed_values: list<item: string>
      child 0, item: string
  child 2, recommended_model_surfaces: list<item: string>
      child 0, item: string
  child 3, prompt_variant_ids: list<item: string>
      child 0, item: string
as_of_date: timestamp[s]
inspiration: struct<source_url: string, source_summary: string>
  child 0, source_url: string
  child 1, source_summary: string
to
{'name': Value('string'), 'version': Value('string'), 'as_of_date': Value('timestamp[s]'), 'inspiration': {'source_url': Value('string'), 'source_summary': Value('string')}, 'run_design': {'response_input_file': Value('string'), 'response_origin_allowed_values': List(Value('string')), 'recommended_model_surfaces': List(Value('string')), 'prompt_variant_ids': List(Value('string'))}, 'live_capture': {'request_timeout_seconds': Value('int64'), 'max_retries': Value('int64'), 'retry_base_sleep_seconds': Value('float64'), 'sleep_between_calls_seconds': Value('float64'), 'store_raw_response': Value('bool'), 'system_instruction': Value('string'), 'providers': List({'provider_id': Value('string'), 'enabled': Value('bool'), 'api': Value('string'), 'env_key': Value('string'), 'model': Value('string'), 'temperature': Value('float64'), 'max_output_tokens': Value('int64'), 'store': Value('bool'), 'env_key_alternates': List(Value('string')), 'anthropic_version': Value('timestamp[s]')}), 'prompt_variants': List({'prompt_id': Value('string'), 'template': Value('string'), 'applies_to_buyer_types': List(Value('string'))})}, 'scoring': {'position_scores': {'1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64')}, 'position_score_for_9_or_later': Value('int64'), 'unranked_mention_score': Value('int64'), 'absent_score': Value('int64'), 'category_score_method': Value('string')}, 'categories': List({'category_id': Value('string'), 'category_name': Value('string'), 'buyer_type': Value('string'), 'default_prompt': Value('string'), 'analysis_question': Value('string'), 'public_vendors': List({'vendor_id': Value('string'), 'company_name': Value('string'), 'ticker': Value('string'), 'exchange': Value('string'), 'product_names': List(Value('string')), 'aliases': List(Value('string'))}), 'private_or_noncovered_spoilers': List({'vendor_id': Value('string'), 'aliases': List(Value('string'))}), 'source_urls': List(Value('string'))})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              buyer_type: string
              category_id: string
              category_name: string
              models_scored: list<item: string>
                child 0, item: string
              private_or_noncovered_spoiler_mentions: struct<databricks: int64, pipedrive: int64, zoho_crm: int64, google_travel_surface: int64, bamboohr: (... 38 chars omitted)
                child 0, databricks: int64
                child 1, pipedrive: int64
                child 2, zoho_crm: int64
                child 3, google_travel_surface: int64
                child 4, bamboohr: int64
                child 5, gusto: int64
                child 6, rippling: int64
              prompt_ids_scored: list<item: string>
                child 0, item: string
              providers_scored: list<item: string>
                child 0, item: string
              public_vendor_count: int64
              response_origins: list<item: string>
                child 0, item: string
              responses_scored: int64
              top_public_vendor_company: string
              top_public_vendor_id: string
              top_public_vendor_score_0_to_100: double
              top_public_vendor_ticker: string
              scoring: struct<position_scores: struct<1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, (... 132 chars omitted)
                child 0, position_scores: struct<1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64>
                    child 0, 1: int64
                    child 1, 2: int64
                    child 2, 3: int64
                    child 3, 4: int64
                    child 4, 5: int64
                    child 5, 6: int64
                    child 6, 7: int64
                    child 7, 8: int64
                child 1, position_score_for_9_or_later: int64
                child 2, unranked_mention_score: int64
                child 3, absent_score: int64
                child 4, category_score_method: string
              version: string
              categories: list<item: st
              ...
              pi: string, env_key: string, model: string, temperature: (... 120 chars omitted)
                        child 0, provider_id: string
                        child 1, enabled: bool
                        child 2, api: string
                        child 3, env_key: string
                        child 4, model: string
                        child 5, temperature: double
                        child 6, max_output_tokens: int64
                        child 7, store: bool
                        child 8, env_key_alternates: list<item: string>
                            child 0, item: string
                        child 9, anthropic_version: timestamp[s]
                child 7, prompt_variants: list<item: struct<prompt_id: string, template: string, applies_to_buyer_types: list<item: string>>>
                    child 0, item: struct<prompt_id: string, template: string, applies_to_buyer_types: list<item: string>>
                        child 0, prompt_id: string
                        child 1, template: string
                        child 2, applies_to_buyer_types: list<item: string>
                            child 0, item: string
              run_design: struct<response_input_file: string, response_origin_allowed_values: list<item: string>, recommended_ (... 75 chars omitted)
                child 0, response_input_file: string
                child 1, response_origin_allowed_values: list<item: string>
                    child 0, item: string
                child 2, recommended_model_surfaces: list<item: string>
                    child 0, item: string
                child 3, prompt_variant_ids: list<item: string>
                    child 0, item: string
              as_of_date: timestamp[s]
              inspiration: struct<source_url: string, source_summary: string>
                child 0, source_url: string
                child 1, source_summary: string
              to
              {'name': Value('string'), 'version': Value('string'), 'as_of_date': Value('timestamp[s]'), 'inspiration': {'source_url': Value('string'), 'source_summary': Value('string')}, 'run_design': {'response_input_file': Value('string'), 'response_origin_allowed_values': List(Value('string')), 'recommended_model_surfaces': List(Value('string')), 'prompt_variant_ids': List(Value('string'))}, 'live_capture': {'request_timeout_seconds': Value('int64'), 'max_retries': Value('int64'), 'retry_base_sleep_seconds': Value('float64'), 'sleep_between_calls_seconds': Value('float64'), 'store_raw_response': Value('bool'), 'system_instruction': Value('string'), 'providers': List({'provider_id': Value('string'), 'enabled': Value('bool'), 'api': Value('string'), 'env_key': Value('string'), 'model': Value('string'), 'temperature': Value('float64'), 'max_output_tokens': Value('int64'), 'store': Value('bool'), 'env_key_alternates': List(Value('string')), 'anthropic_version': Value('timestamp[s]')}), 'prompt_variants': List({'prompt_id': Value('string'), 'template': Value('string'), 'applies_to_buyer_types': List(Value('string'))})}, 'scoring': {'position_scores': {'1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64')}, 'position_score_for_9_or_later': Value('int64'), 'unranked_mention_score': Value('int64'), 'absent_score': Value('int64'), 'category_score_method': Value('string')}, 'categories': List({'category_id': Value('string'), 'category_name': Value('string'), 'buyer_type': Value('string'), 'default_prompt': Value('string'), 'analysis_question': Value('string'), 'public_vendors': List({'vendor_id': Value('string'), 'company_name': Value('string'), 'ticker': Value('string'), 'exchange': Value('string'), 'product_names': List(Value('string')), 'aliases': List(Value('string'))}), 'private_or_noncovered_spoilers': List({'vendor_id': Value('string'), 'aliases': List(Value('string'))}), 'source_urls': List(Value('string'))})}
              because column names don't match

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Public Company GEO Visibility v1

Published by Flat Circle: https://flatcircle.ai

This dataset audits which public-company products appear in live LLM purchase-recommendation answers. It covers six categories where consumers or businesses commonly ask for vendor shortlists and where public companies are credible vendors.

Idea credit: a Reddit post in r/ClaudeAI described a GEO auditor that queries LLMs with category recommendation prompts and scores brand mentions/rank position: https://www.reddit.com/r/ClaudeAI/comments/1shmpxv/i_built_a_geo_auditor_with_claude_code_and_here/

Files

File	Rows	Description
`data/model_responses_public.jsonl`	19	Sanitized live model response rows. Raw provider payloads and response IDs are omitted.
`data/vendor_universe.jsonl`	48	Public-company vendors plus private/non-covered rival aliases by category.
`data/response_mentions.jsonl`	114	One response-vendor mention/position/score row per public vendor.
`data/visibility_scores.jsonl`	36	Vendor-level category visibility scores.
`data/category_summary.jsonl`	6	Category leaders and rival counts.
`data/visibility_scores.csv`	36	CSV copy of vendor-level scores.
`data/category_summary.csv`	6	CSV copy of category summaries.
`docs/FINDINGS_SUMMARY.md`	n/a	Reader-facing snapshot summary.

Canonical replication guide: https://gist.github.com/jamesdmoran/b58435aef453dce9e1164c952a5881e9

Method

The dataset uses the LLM Surface Audit pattern:

Define recommendation categories, public vendors, product names, and aliases in config/dataset.json.
Capture live model responses with exact prompt, provider, model, temperature, timestamp, and category metadata.
Parse vendor mentions and estimated list positions deterministically.
Score each public vendor from 0 to 100 based on mention frequency and rank.
Count private or non-covered rivals separately as "other rivals surfaced."

Scores are not investment ratings. They are an attention and consideration signal for how LLMs form shortlists.

Caveats

Model answers can drift over time.
Aliases for short names and product families require manual review.
CRM includes OpenAI, Claude, Gemini, and Perplexity in this package. Other categories use OpenAI, Gemini, and Perplexity until Claude is run across the full panel.
The public package includes generated model answer text for auditability, but omits full raw provider payloads and credentials.
The PNG is generated locally for article publishing and is intentionally not part of the Hugging Face dataset package.
Check provider terms before redistributing large volumes of model output.

Rerun

Use the canonical replication guide for the full workflow:

https://gist.github.com/jamesdmoran/b58435aef453dce9e1164c952a5881e9

Required secrets are environment variables only:

export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...   # or CLAUDE_API_KEY
export GEMINI_API_KEY=...      # or GOOGLE_API_KEY
export PERPLEXITY_API_KEY=...

Then run:

python scripts/capture_live_responses.py --prompt-ids generic_best
python scripts/build_png_card.py

License

Dataset packaging, derived rows, config, and documentation are released under CC BY 4.0. Third-party model outputs and API-derived content may be subject to the relevant provider terms.

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