Karmane/restaurant-ai-phone-system-pricing-and-workflow

Restaurant AI Phone System Pricing and Workflow Dataset

Question this dataset helps answer: Which restaurant AI phone systems actually publish usable pricing or starting-price signals, and which ones match my reservation, ordering, POS, multilingual, and handoff workflow needs?

This dataset helps restaurant operators, multi-unit restaurant teams, and restaurant-tech consultants compare restaurant AI phone systems using official pricing, product, use-case, and workflow pages instead of manually re-reading scattered vendor sites. It is built for shortlisting, procurement research, budget planning, operational benchmarking, and integration screening.

Use the public sample to check:

• Which vendors publish self-serve pricing or at least a visible starting-price signal on official pages
• Which vendors explicitly mention reservations, waitlists, phone ordering, guest texting, or payment workflows
• Which vendors mention POS integrations, reservation platforms, multilingual support, or human handoff

Preview vs full dataset:

• Preview: 22 rows and 36 columns
• Full: 221 rows and 36 columns

Free sample: https://huggingface.co/datasets/Karmane/restaurant-ai-phone-system-pricing-and-workflow-sample Full dataset: https://thearticulated.gumroad.com/l/uhnlr?utm_source=hf_sample_readme&utm_medium=referral&utm_campaign=restaurant-ai-phone-system-pricing-and-workflow Loading example: load_dataset("Karmane/restaurant-ai-phone-system-pricing-and-workflow-sample")

Why this dataset exists

Restaurant operators already understand the buyer problem. Missed calls mean missed covers, missed takeout orders, missed repeat guests, and more interruptions for the staff who are trying to handle the rush. The market has responded with a growing set of AI phone systems, voice ordering tools, reservation assistants, and restaurant-specific answering platforms. The hard part is not finding vendors. The hard part is evaluating them without opening dozens of tabs, booking multiple demos, and comparing screenshots from pricing pages, restaurant use cases, integration lists, and FAQ content that all use different language.

This dataset turns that scattered surface area into one analysis-ready table. Each row is a source-backed signal collected from official vendor pages. Instead of claiming that one platform is "best," the dataset organizes what vendors publicly say about pricing visibility, plan structure, reservation support, waitlist handling, phone ordering, guest texting, multilingual support, POS integrations, reservation platform integrations, payment workflows, and human escalation paths. That makes it useful for buyers who need a shortlist quickly and for consultants who need repeatable comparison criteria across client accounts.

What the dataset covers

The current build contains 221 rows across 36 columns. Rows are signal-level, not just vendor-level. A single vendor can have multiple rows because different official pages may expose different pricing contexts, integrations, or workflow claims. This lets you filter the dataset for specific questions instead of flattening each product into one lossy summary row.

Vendor coverage spans restaurant-focused AI phone answering platforms, voice ordering products, reservation-first voice tools, and restaurant workflow pages from companies that explicitly position their products for restaurant operations. The collection stays inside official sources such as product pages, pricing pages, use-case pages, restaurant landing pages, and official blog or resource pages when they contain pricing or implementation detail that is still part of the vendor's own public documentation.

Example buyer tasks this dataset supports

• Build a shortlist of vendors with public pricing or at least visible starting-price language before talking to sales
• Separate reservation-first tools from order-first tools when comparing front-of-house versus takeout-heavy operations
• Screen for vendors that mention Toast, Square, Clover, SpotOn, Aloha, OpenTable, Resy, or SevenRooms on official pages
• Find vendors that talk about multilingual support, Spanish handling, or guest texting for service-heavy concepts
• Compare which vendors appear more SMB-friendly versus which ones are clearly selling into multi-unit or enterprise restaurant groups
• Create internal scoring models for procurement, implementation planning, or client advisory work

How the rows are constructed

Every row contains the exact source URL, the source domain, the source type, a normalized signal name, a human-readable text value, a short supporting excerpt, and directional confidence metadata. Pricing-like values are parsed when the page makes them visible and safe to normalize. Capability rows are created only when the page text contains explicit language tied to one of the tracked workflow buckets. This means the dataset is better suited for sourcing, screening, and comparison than for absolute product truth. It reflects what vendors publicly publish, not what a hands-on implementation review would discover after a trial.

The schema is designed to balance flexible text with filterable fields. Boolean-like support columns are stored as normalized text flags so analysts can use them directly in spreadsheets, pandas, DuckDB, or BI tools without additional unpacking. Price metrics distinguish between monthly, yearly, per-location, per-order, per-minute, and custom-pricing signals. Because the collection is source-backed, the dataset also works as a citation map: when you see a signal you care about, you can jump back to the official page for review.

What the free sample proves

The public sample on Hugging Face is the discovery surface. It proves the schema, the row design, the source attribution model, and the practical filters buyers can run immediately. The sample is enough to answer early screening questions such as whether pricing is public, whether a vendor emphasizes reservations or phone ordering, and whether official pages mention integrations or multilingual workflows. The paid dataset extends that sample with the full row set, complete coverage, and the same column structure so the sample can act as a direct proof-of-work preview rather than a toy extract.

Limitations

This dataset is built from public official content, so it should not be treated as a substitute for implementation testing, a legal review, or a guarantee that a workflow works exactly as marketed. Some vendors publish pricing openly while others require a demo, which naturally makes pricing rows denser for transparent vendors. Some restaurant platforms mention integrations broadly without documenting exact setup depth on the same page. Language around AI ordering, answering, and reservations also varies by vendor, so normalized support flags are best-effort interpretations anchored to the visible source text.

Intended use

Use this dataset for research, benchmarking, procurement prep, restaurant-tech advisory work, spreadsheet comparison, dashboarding, market mapping, and operational planning. Do not use it as a guarantee of ROI, labor reduction, or fit for a specific restaurant concept without verifying the underlying source pages and running your own implementation review.

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Dataset Preview

record_id	vendor_id	vendor_name	vendor_segment	target_buyer	official_website_url	source_url	source_domain	source_type	source_title	signal_type	signal_category	signal_name	signal_value_text	normalized_signal_value	price_amount_usd	price_metric	pricing_transparency	pricing_model_summary	reservation_support	phone_ordering_support	waitlist_support	sms_support	pos_integration_support	reservation_platform_integration_support	human_handoff_support	multilingual_support	payment_processing_support	multi_location_support	industry_focus_tags	confidence_score	confidence_level	notes	source_excerpt	observed_date	last_collected_at
9d5086dfb6e8d37d	ai-answer-now	AI Answer Now	restaurant-phone-platform	Small restaurant teams that want simpler AI answering plans with public pricing	https://aianswernow.com/	https://aianswernow.com/	aianswernow.com	Official product page	Restaurant AI Phone Answering — 24/7, Never Misses an Order | AiAnswerNow	capability_signal	workflow	human_handoff_support	The AI can transfer any call to a human staff member.	yes			yes	usage-based pricing	unknown	unknown	unknown	yes	yes	unknown	yes	yes	yes	unknown	hospitality	84	medium	Capability row captured from an official sentence that explicitly mentions the workflow or integrati... [9 more chars]	The AI can transfer any call to a human staff member.	2026-06-11 00:00:00+00:00	2026-06-11 02:28:11+00:00
fd2d5e26b6ee98dc	ai-answer-now	AI Answer Now	restaurant-phone-platform	Small restaurant teams that want simpler AI answering plans with public pricing	https://aianswernow.com/	https://aianswernow.com/	aianswernow.com	Official product page	Restaurant AI Phone Answering — 24/7, Never Misses an Order | AiAnswerNow	capability_signal	workflow	multilingual_support	English, Spanish, Chinese, Russian, Italian, and Hindi — all included in every plan at no extra char... [3 more chars]	yes			yes	usage-based pricing	unknown	unknown	unknown	yes	yes	unknown	yes	yes	yes	unknown	hospitality	84	medium	Capability row captured from an official sentence that explicitly mentions the workflow or integrati... [9 more chars]	English, Spanish, Chinese, Russian, Italian, and Hindi — all included in every plan at no extra char... [3 more chars]	2026-06-11 00:00:00+00:00	2026-06-11 02:28:11+00:00
850686e59bdf522f	ai-answer-now	AI Answer Now	restaurant-phone-platform	Small restaurant teams that want simpler AI answering plans with public pricing	https://aianswernow.com/	https://aianswernow.com/	aianswernow.com	Official product page	Restaurant AI Phone Answering — 24/7, Never Misses an Order | AiAnswerNow	capability_signal	integrations	pos_integration_support	Does it integrate with my POS?	yes			yes	usage-based pricing	unknown	unknown	unknown	yes	yes	unknown	yes	yes	yes	unknown	hospitality	84	medium	Capability row captured from an official sentence that explicitly mentions the workflow or integrati... [9 more chars]	Does it integrate with my POS?	2026-06-11 00:00:00+00:00	2026-06-11 02:28:11+00:00
a4d4b3c45cac2b88	ai-answer-now	AI Answer Now	restaurant-phone-platform	Small restaurant teams that want simpler AI answering plans with public pricing	https://aianswernow.com/	https://aianswernow.com/	aianswernow.com	Official product page	Restaurant AI Phone Answering — 24/7, Never Misses an Order | AiAnswerNow	capability_signal	workflow	sms_support	AiAnswerNow answers every call 24/7, takes orders, upsells in 6 languages, and sends SMS confirmatio... [3 more chars]	yes			yes	usage-based pricing	unknown	unknown	unknown	yes	yes	unknown	yes	yes	yes	unknown	hospitality	84	medium	Capability row captured from an official sentence that explicitly mentions the workflow or integrati... [9 more chars]	AiAnswerNow answers every call 24/7, takes orders, upsells in 6 languages, and sends SMS confirmatio... [3 more chars]	2026-06-11 00:00:00+00:00	2026-06-11 02:28:11+00:00
d9c2b431ba0845a8	ai-answer-now	AI Answer Now	restaurant-phone-platform	Small restaurant teams that want simpler AI answering plans with public pricing	https://aianswernow.com/	https://aianswernow.com/	aianswernow.com	Official product page	Restaurant AI Phone Answering — 24/7, Never Misses an Order | AiAnswerNow	page_summary	summary	official_page_summary	Your restaurant misses 20-30% of dinner rush calls. AiAnswerNow answers every call 24/7, takes order... [95 more chars]	official-summary			yes	usage-based pricing	unknown	unknown	unknown	yes	yes	unknown	yes	yes	yes	unknown	hospitality	78	medium	Summary row built from the official page metadata description.	Your restaurant misses 20-30% of dinner rush calls. AiAnswerNow answers every call 24/7, takes order... [95 more chars]	2026-06-11 00:00:00+00:00	2026-06-11 02:28:11+00:00

Access Requirements (Paid Dataset)

This dataset is behind manual gated access.

To obtain access:

1. Purchase the dataset here:
   https://thearticulated.gumroad.com/l/uhnlr?utm_source=hf_paid_readme&utm_medium=referral&utm_campaign=restaurant-ai-phone-system-pricing-and-workflow

2. Provide your Hugging Face username at checkout.

3. Return to this Hugging Face page and click:
   "Request Access"

4. Your access will be approved within 1-12 hours.

Once approved, you can use the Python snippet at the bottom of this README to load the dataset.

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Dataset Structure

Total rows: 221

Total columns: 36

Splits

• data: 221 rows

Data Files

• data: data/data.parquet

Data Dictionary

The table below describes the columns included in this dataset.

column	pandas_dtype	dataset_type	description
record_id	object	string	Text column. Stable unique identifier for the row-level official signal.
vendor_id	object	string	Text column. Machine-friendly slug for the restaurant AI phone vendor or product.
vendor_name	object	string	Text column. Display name of the vendor or product surface represented by the row.
vendor_segment	object	string	Text column. High-level market segment such as restaurant-phone-platform, qsr-voice-platform, restaurant-reservations-platform, or multi-unit-restaurant-platform.
target_buyer	object	string	Text column. Specific buyer persona or operational team the vendor appears to target.
official_website_url	object	string	Text column. Primary official website URL for the vendor or product.
source_url	object	string	Text column. Exact official page URL used to derive the row.
source_domain	object	string	Text column. Domain extracted from the source URL.
source_type	object	string	Text column. Best-effort page type such as Official pricing page, Official use-case page, Official blog page, or Official product page.
source_title	object	string	Text column. HTML title of the official source page when available.
signal_type	object	string	Text column. Broad row family such as page_summary, pricing_signal, or capability_signal.
signal_category	object	string	Text column. Specific grouping for the row, such as pricing, reservation_support, phone_ordering_support, or integrations.
signal_name	object	string	Text column. Normalized machine-friendly name for the captured signal.
signal_value_text	object	long text	Free-text column containing longer text values. Human-readable text extracted or normalized from the official page.
normalized_signal_value	object	string	Text column. Compact normalized grouping value for the signal.
price_amount_usd	float64	float	Decimal numeric column. Parsed numeric USD price when a public price-like value was visible on the source page.
price_metric	object	string	Text column. Best-effort metric attached to the observed price such as per_month, per_location, per_order, or custom_pricing_signal.
pricing_transparency	object	string	Text column. Best-effort yes, partial, or unknown flag for public pricing visibility on the page.
pricing_model_summary	object	string	Text column. Condensed summary of the pricing model implied by the page text.
reservation_support	object	string	Text column. Best-effort yes or unknown flag for reservation or table-booking workflows.
phone_ordering_support	object	string	Text column. Best-effort yes or unknown flag for voice ordering, takeout, delivery, or call-based order capture.
waitlist_support	object	string	Text column. Best-effort yes or unknown flag for waitlist, table-ready, or queue-style restaurant workflows.
sms_support	object	string	Text column. Best-effort yes or unknown flag for text-message or SMS guest workflows.
pos_integration_support	object	string	Text column. Best-effort yes or unknown flag for POS system integration claims such as Toast, Square, Clover, SpotOn, or Aloha.
reservation_platform_integration_support	object	string	Text column. Best-effort yes or unknown flag for reservation platform integrations such as OpenTable, Resy, or SevenRooms.
human_handoff_support	object	string	Text column. Best-effort yes or unknown flag for human transfer, escalation, or live-staff routing workflows.
multilingual_support	object	string	Text column. Best-effort yes or unknown flag for Spanish or broader multilingual support claims.
payment_processing_support	object	string	Text column. Best-effort yes or unknown flag for payment capture or pay-by-phone claims.
multi_location_support	object	string	Text column. Best-effort yes or unknown flag for multi-unit or multi-location workflows.
industry_focus_tags	object	string	Text column. Pipe-delimited restaurant workflow tags inferred from official page text.
confidence_score	int32	integer	Whole-number numeric column. Directional 0-100 confidence score based on how directly the source supports the row.
confidence_level	object	string	Text column. Text label derived from confidence_score.
notes	object	string	Text column. Interpretation notes or source-specific caveats for the row.
source_excerpt	object	long text	Free-text column containing longer text values. Short supporting excerpt from the official page used to create the row.
observed_date	datetime64[ns, UTC]	datetime	Date or timestamp column. UTC date when the source page was observed.
last_collected_at	datetime64[ns, UTC]	datetime	Date or timestamp column. UTC timestamp when the collection run gathered the row.

Intended Use

This dataset is intended for research, experimentation, analysis, and model prototyping.

Loading the Dataset

import os
from datasets import load_dataset

HUGGINGFACE_API_KEY_KARMANE = os.environ.get("HUGGINGFACE_API_KEY_KARMANE")

dataset = load_dataset(
    "Karmane/restaurant-ai-phone-system-pricing-and-workflow",
    token=HUGGINGFACE_API_KEY_KARMANE,
)

print(dataset)
print(dataset[list(dataset.keys())[0]][0])

# getting the DataFrame itself
# df = dataset[list(dataset.keys())[0]].to_pandas()

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Karmane. (2025). Restaurant AI Phone System Pricing and Workflow Dataset. Hugging Face. https://huggingface.co/datasets/Karmane/restaurant-ai-phone-system-pricing-and-workflow