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List of Agents

Agents are tools that automatically research and enrich data about companies and people. Each agent does one specific job β€” and you can chain them together to build a full picture.

Agents are tools that automatically research and enrich data about companies and people. Each agent does one specific job β€” and you can chain them together to build a full picture.


πŸ“Œ Before You Start β€” How Agents Work

Inputs are flexible. Every agent has at least one field marked βœ… β€” that’s the only one you must provide. Everything else is optional, but the more you fill in, the more complete and accurate your results will be.

Agents work together. Many agents produce outputs that become the inputs of another agent. For example: find a company’s website first, then use that website to pull contacts, then use those contact names to find their emails. The chain examples below each agent show you exactly how to do this.


🏒 Firmographics

What kind of company is it? How big? Who owns it?


B2B or B2C β€” classify_b2b_b2c

Looks at a company’s website and tells you whether they sell to businesses (B2B), consumers (B2C), or both.

Input

Required

Description

website

βœ…

Company website URL

llm

β€”

AI model to use

Output

What you get

model_type

B2B, B2C, BOTH, or blank

errors

Any issues that came up

πŸ”— Chain it: Run find_business_website first if you don’t have the URL. Feed the result into icp_formula_builder to score companies by go-to-market type.


Industry β€” classify_industry

Labels a company with an industry (e.g.Β β€œHealthcare”) or a more specific sub-industry (e.g.Β β€œOutpatient Clinics”).

Input

Required

Description

company

βœ…

Company name

website

β€”

Website URL

address

β€”

City or state

mode

β€”

industry for broad label, sub_industry for specific

llm

β€”

AI model to use

Output

What you get

classification

Industry or sub-industry label

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website to get the URL first for better accuracy. Feed the label into icp_formula_builder as a scoring criterion.


Technician Count β€” count_technicians

Estimates how many technicians work at a company β€” useful for sizing field-service or trade businesses.

Input

Required

Description

website

βœ…

Company website URL

company

βœ…

Company name

llm

β€”

AI model to use

Output

What you get

technicians_count

Estimated number of technicians

source

Where the number came from

errors

Any issues that came up

πŸ”— Chain it: Use this output as a signal in icp_formula_builder to weight companies that have larger field teams.


Company Profile β€” enrich_company_firmographics

Pulls a full company profile from a single website URL β€” employees, revenue, founding year, HQ, tech stack, and more.

Input

Required

Description

website

βœ…

Company website URL

Output

What you get

company_name

Company name

industry

Industry label

description

Short company description

employees_count

Total employee count

size_range

Company size bucket (e.g.Β 50–200)

revenue_annual

Annual revenue in USD

revenue_annual_display

Formatted revenue (e.g.Β $1.2M)

revenue_annual_range

Revenue range

founded_year

Year the company was founded

company_type

e.g.Β Private, Non-Profit

ownership_status

e.g.Β Privately Held, PE-Backed

hq_location

Headquarters location

hq_country

HQ country

linkedin_url

Company LinkedIn page

website

Confirmed website URL

is_b2b

True if B2B

is_public

True if publicly traded

technologies_used

List of tools/tech detected

num_technologies_used

Count of technologies

raw

Full 170+ field data profile

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website to get the URL first. Feed company_name, industry, employees_count, and revenue_annual into icp_score to grade a company against your ICP formula.


Location Count β€” find_business_location_count

Counts how many physical locations a company operates and lists where they are.

Input

Required

Description

website

βœ…

Company website URL

llm

β€”

AI model to use

Output

What you get

location_count

Estimated number of locations

locations

List of location details

source_urls

Pages used to find the info

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website first. Use this count as a field in icp_formula_builder to favor multi-location businesses.


Parent Company or PE Firm β€” find_parent_company_pe

Looks up who owns a company β€” either its parent brand or the private equity firm behind it.

Input

Required

Description

company

βœ…

Company name

website

β€”

Website URL

mode

β€”

parent_company or pe_firm

llm

β€”

AI model to use

max_iterations

β€”

How many research steps to take (max 15)

Output

What you get

result

Name of parent company or PE firm

errors

Any issues that came up

πŸ”— Chain it: Once you have the parent company name, run enrich_company_firmographics on the parent to understand the full ownership picture.


Primary Market β€” find_primary_market

Tells you whether a company mainly serves residential, commercial, or industrial customers β€” based on their website.

Input

Required

Description

website

βœ…

Company website URL

llm

β€”

AI model to use

Output

What you get

market

Residential, Commercial, Industrial, or a combination

confidence

How confident the result is (0–100)

reasoning

Why it landed on that label

errors

Any issues that came up

πŸ”— Chain it: Use this output to segment your list before running icp_score.


Franchise Status β€” get_franchise_status

Tells you if a business is a franchise location, a corporate-owned store, or an independent β€” with a confidence score and reasoning.

Input

Required

Description

company_name

βœ…

Company name

website

β€”

Website URL

llm

β€”

AI model to use

Output

What you get

status

Franchise, Corporate-Owned, or Independent

confidence

Confidence score (0–100)

reasoning

Why it got that label

parent_company

Parent brand if it’s a franchise

errors

Any issues that came up

πŸ”— Chain it: If parent_company is returned, run find_parent_company_pe to understand ownership. If it’s a franchise, use find_franchise_contacts to find the local decision-maker.


🌍 Online Presence

What does their digital footprint look like?


Clean URL β€” clean_website_url

Takes a messy or inconsistent URL and returns a clean, standardized version you can safely store or use downstream.

Input

Required

Description

url

βœ…

Any URL (messy or clean)

mode

β€”

tld = registered domain only Β· clean = normalized URL Β· clean_no_3p = rejects social/directory sites

Output

What you get

result_url

Clean URL or domain

is_social

True if the URL is a social media profile

errors

Any issues that came up

πŸ”— Chain it: Always run this first before passing a URL into any other agent to avoid errors from inconsistent formatting.


Website Modernity β€” detect_website_modernity

Checks if a company’s website looks modern or outdated based on how it’s built.

Input

Required

Description

website

βœ…

Company website URL

llm

β€”

AI model to use

Output

What you get

website_modernity

Modern or Old

reasoning

What signals were used to decide

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website to get the URL first. Combine with tech_stack_scan for a full digital health picture.


Google Business Profile β€” find_business_on_google

Finds a company’s official Google Maps listing and returns their address, phone, rating, review count, and coordinates.

Input

Required

Description

company_name

βœ…

Company name

address

β€”

Street address

city

β€”

City

state

β€”

State

country

β€”

Country

latitude

β€”

GPS latitude

longitude

β€”

GPS longitude

llm

β€”

AI model to use

Output

What you get

place_id

Google Maps place ID

title

Business name on Google

address

Full formatted address

phone

Phone number

website

Website URL

rating

Star rating

reviews

Number of reviews

gps_coordinates

Lat/lng location

confidence

Match confidence (0–100)

errors

Any issues that came up

πŸ”— Chain it: Feed website into tech_stack_scan or enrich_company_firmographics. Feed address into classify_location_type. Feed google_maps_url into enrich_google_reviews.


Find Website β€” find_business_website

Finds a company’s official website from just their name and optional location.

Input

Required

Description

company_name

βœ…

Company name

address

β€”

Street address

city

β€”

City

state

β€”

State

llm

β€”

AI model to use

Output

What you get

website

Discovered website URL

source

Where the URL was found

confidence

Confidence score (0–100)

errors

Any issues that came up

πŸ”— Chain it: This is the starting point for most workflows. Feed website into enrich_company_firmographics, contact_extractor, tech_stack_scan, find_company_social_urls, and more.


Social Profiles β€” find_company_social_urls

Finds a company’s official Facebook, Instagram, LinkedIn, and Yelp profiles.

Input

Required

Description

company

βœ…

Company name

website

βœ…

Website URL

address

β€”

Company address (helps verify the right profile)

max_iterations

β€”

How many search steps to take (max 25)

llm

β€”

AI model to use

Output

What you get

facebook_url

Facebook profile URL

facebook_confidence

Confidence in the Facebook match

instagram_url

Instagram profile URL

instagram_confidence

Confidence in the Instagram match

linkedin_url

LinkedIn profile URL

linkedin_confidence

Confidence in the LinkedIn match

yelp_url

Yelp business URL

yelp_confidence

Confidence in the Yelp match

errors

Any issues that came up

πŸ”— Chain it: Run find_business_website first to get the URL. Use the linkedin_url to cross-reference employees found via contact_extractor.


Tech Stack β€” tech_stack_scan

Scans a company’s website and tells you what software and technology they’re running β€” CMS, payments, analytics, POS, and more.

Input

Required

Description

url

βœ…

Website URL

company_name

βœ…

Company name

categories

β€”

Specific tech categories to check (e.g.Β CMS, payments)

techs

β€”

Specific technologies to look for

enable_online_research

β€”

Turn on additional web research

crawl_limit

β€”

Max number of pages to crawl

llm

β€”

AI model to use

Output

What you get

profile

Full tech profile by category

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website first. Combine with detect_website_modernity for a complete digital presence snapshot.


Website Status β€” website_active_inactive

Checks if a URL is a real, live website or a parked domain, placeholder, or dead page.

Input

Required

Description

url

βœ…

Website URL

llm

β€”

AI model to use

Output

What you get

overall_category

Active or Inactive

inactive_reason

Why it’s inactive (e.g.Β parked, for sale)

reasoning

Supporting explanation

errors

Any issues that came up

πŸ”— Chain it: Run this before investing in any enrichment on a new URL to avoid wasting agent runs on dead sites.


πŸ‘€ Contacts

Who are the real people behind the business?


Website Contacts β€” contact_extractor

Scrapes a company website and pulls the names, titles, emails, and phones of people listed there β€” up to 10 contacts.

Input

Required

Description

url

βœ…

Website URL

max_pages

β€”

How many pages to scrape (default 25, max 100)

target_roles

β€”

Filter by job title (e.g.Β CEO, VP Sales)

Output

What you get

full_name_1 … full_name_10

Full names of up to 10 contacts

title_1 … title_10

Job title for each person

email_1 … email_10

Email for each person

phone_1 … phone_10

Phone for each person

contacts_count

Total contacts found

pages_scraped

Pages that were scanned

errors

Any issues that came up

πŸ”— Chain it: Use find_business_website first. Take any name + company and run find_email_waterfall or find_phone_waterfall for contacts that are missing emails or phones.


Company Email, Phone & Address β€” find_company_email_phone_address

Pulls the general contact info for a company (not a specific person) directly from their website.

Input

Required

Description

company

βœ…

Company name

website

βœ…

Website URL

max_iterations

β€”

How deep to search (default 5, max 10)

llm

β€”

AI model to use

Output

What you get

email

General company email

phone

Main phone number

address

Physical mailing address

source

Which pages the data came from

errors

Any issues that came up

πŸ”— Chain it: Feed phone into phone_intel to validate it before dialing. Feed address into classify_location_type to see if it’s a home office, commercial space, or warehouse.


Find Email β€” find_email_waterfall

Finds the best work email for a specific person by trying multiple lookup sources in order, stopping at the first hit. Optionally validates deliverability.

Input

Required

Description

first_name

βœ…

First name (or use full_name)

last_name

β€”

Last name

full_name

β€”

Full name

company_name

β€”

Company name

company_domain

β€”

Company domain (e.g.Β acme.com)

contact_linkedin_url

β€”

The person’s LinkedIn URL

should_validate

β€”

Run email validation? (default: yes)

validator

β€”

zerobounce or findymail

provider_order

β€”

Custom lookup order

Output

What you get

email

Best email found

validated

Whether it passed validation

validator_used

Which validator ran

winning_tool

Which lookup source found it

attempts_log

Full step-by-step trail

cost_usd

Total lookup cost

confidence

Confidence score (0–100)

errors

Any issues that came up

πŸ”— Chain it: Use find_small_business_owner or contact_extractor to get a name first. Use find_person_linkedin to add their LinkedIn URL for better match accuracy.


Franchise Contacts β€” find_franchise_contacts

Finds up to 3 local contacts (name, title, email, phone) for a specific franchise location β€” not the corporate brand.

Input

Required

Description

brand_name

βœ…

The franchise brand (e.g.Β Jiffy Lube)

entity_name

βœ…

The specific location or entity name

location

β€”

City and state

llm

β€”

AI model to use

Output

What you get

full_name_1 … full_name_3

Names of up to 3 contacts

title_1 … title_3

Title for each person

email_1 … email_3

Email for each person

phone_1 … phone_3

Phone for each person

errors

Any issues that came up

πŸ”— Chain it: Run get_franchise_status first to confirm it’s a franchise and get the brand name. Then take returned names into find_email_waterfall or find_phone_waterfall for contacts with missing details.


Home Address β€” find_person_home_address

Runs a skip-trace lookup using a phone or email to find someone’s current residential address, including previous addresses and match confidence.

Input

Required

Description

phone

βœ… (or email)

Phone number

full_name

β€”

Person’s full name

email

β€”

Email address

state

β€”

US state to narrow results

Output

What you get

current_address

Current home address

matched_name

Name on the matched record

confidence

Confidence score (0–100)

match_score

Record match quality

name_matched

Whether the name aligned

matched_emails

Other emails on the record

matched_phones

Other phones on the record

previous_addresses

Prior addresses

person_link

Link to the source record

errors

Any issues that came up

πŸ”— Chain it: Use find_phone_waterfall or find_email_waterfall first to get the phone/email. Feed current_address into classify_location_type to see what type of location it is.


LinkedIn Profile β€” find_person_linkedin

Finds someone’s LinkedIn profile URL using their name and optional company context.

Input

Required

Description

name

βœ…

Full name

company

β€”

Company name

website

β€”

Company website URL

max_iterations

β€”

Search depth (max 15)

llm

β€”

AI model to use

Output

What you get

linkedin_url

LinkedIn profile URL (/in/…)

errors

Any issues that came up

πŸ”— Chain it: Feed linkedin_url into find_email_waterfall or find_phone_waterfall to improve match accuracy. Often used after find_small_business_owner or contact_extractor.


Find Phone β€” find_phone_waterfall

Finds the best phone number for a specific person by trying multiple lookup sources in order, stopping at the first hit.

Input

Required

Description

first_name

βœ…

First name (or use full_name)

last_name

β€”

Last name

full_name

β€”

Full name

company_name

β€”

Company name

company_domain

β€”

Company domain

contact_linkedin_url

β€”

The person’s LinkedIn URL

provider_order

β€”

Custom lookup order

Output

What you get

phone

Phone number found

winning_tool

Which lookup source found it

attempts_log

Full step-by-step trail

cost_usd

Total lookup cost

confidence

Confidence score (0–100)

errors

Any issues that came up

πŸ”— Chain it: Run after find_small_business_owner or contact_extractor. Feed the returned phone into phone_intel to validate it before reaching out.


Small Business Owner β€” find_small_business_owner

Identifies the owner or key decision-maker at a small or local business by name, title, and LinkedIn profile.

Input

Required

Description

company_name

βœ…

Company name

website

β€”

Company website

address

β€”

Business address or city/state

max_iterations

β€”

Search depth (max 15)

llm

β€”

AI model to use

Output

What you get

full_name

Owner’s full name

first_name

First name

last_name

Last name

title

Their title (e.g.Β Owner, CEO, Founder)

linkedin_url

Their LinkedIn profile URL

confidence

Confidence score (0–100)

source

Where the info came from

errors

Any issues that came up

πŸ”— Chain it: This is a key starting point for SMB outreach. After finding the owner, run find_email_waterfall with full_name + company_domain for their email, and find_phone_waterfall for their phone. Add linkedin_url to either for better accuracy.


βœ… Data Quality

Is this data valid, real, and worth acting on?


Location Type β€” classify_location_type

Takes a physical address and tells you what type of property it is β€” residential, commercial, or industrial β€” using satellite imagery and AI.

Input

Required

Description

address

βœ…

Full street address

llm

β€”

AI model to use

Output

What you get

location_type

Residential, Commercial, Industrial/Warehouse, or N/A

image_link

Google Maps link to the location

errors

Any issues that came up

πŸ”— Chain it: Use addresses returned from enrich_company_firmographics, find_business_on_google, or find_person_home_address.


IP Geolocation β€” enrich_ip_address

Looks up an IP address and returns the city, region, country, postal code, and organization it belongs to.

Input

Required

Description

ip

βœ…

IP address to look up

Output

What you get

city

City tied to the IP

state

State or region

country

Country

postal

Postal code

coordinates

Lat/lng location

hostname

Resolved hostname

org

Organization or ISP

errors

Any issues that came up


Phone Validation β€” phone_intel

Tells you whether to call or skip a phone number, based on carrier data, line type, and optional DNC/TCPA screening.

Input

Required

Description

phone

βœ…

Phone number (any format)

person_name

β€”

Person’s name

company_name

β€”

Company name

skip_dnc

β€”

Skip the DNC check

Output

What you get

recommendation

CALL or SKIP

confidence

Confidence score (0–100)

primary_reason

One-line explanation

phone

Normalized phone number

trestle

Trestle carrier data

rpv

RPV enrichment data

searchbug

DNC/TCPA screening data

verdict

Full verdict object

errors

Any issues that came up

πŸ”— Chain it: Always run after find_phone_waterfall or find_small_business_owner before dialing. Use validate_biz_phone instead if you just need a quick line-type check without the full CALL/SKIP analysis.


Business Phone Validation β€” validate_biz_phone

A lightweight phone check: cleans the number to a standard format and guesses whether it reaches a decision-maker or a gatekeeper.

Input

Required

Description

phone

βœ…

Business phone number (any format)

Output

What you get

output

Cleaned phone in standard format

status

Likely Decision-Maker or Likely Gatekeeper

errors

Any issues that came up

πŸ”— Chain it: Use after find_company_email_phone_address for a quick check. For a deeper analysis, use phone_intel instead.


πŸ“Š Market Intelligence

How is this business performing and what are they up to?


Ads Intelligence β€” enrich_ads_intelligence

Shows you whether a company is running ads on Google, Meta, or LinkedIn β€” and what those ads look like.

Input

Required

Description

website

βœ…

Company domain (e.g.Β acme.com)

platforms

β€”

Limit to specific platforms: google, meta, or linkedin

Output

What you get

google_ads

Google ad data for the domain

meta_ads

Meta ad data for the domain

linkedin_ads

LinkedIn ad data for the domain

errors

Any issues that came up

πŸ”— Chain it: Run after find_business_website to see if a company is spending on ads as a buying intent signal.


FMCSA Carrier β€” enrich_fmcsa

Pulls official carrier data from the FMCSA SAFER database for trucking and logistics companies β€” authority status, fleet size, cargo types, and more.

Input

Required

Description

company_name

βœ… (one of these)

Carrier name

usdot_number

βœ… (one of these)

USDOT number

mc_number

βœ… (one of these)

MC/MX number

city

β€”

City

state

β€”

State

Output

What you get

fmcsa_legal_name

Legal company name

dba_name

Doing-business-as name

usdot_status

USDOT status

operating_authority_status

Operating authority status

drivers

Driver count

power_units

Number of trucks/units

cargo_carried

What types of cargo they carry

fmcsa_link

Direct link to their FMCSA record

errors

Any issues that came up


Google Reviews β€” enrich_google_reviews

Pulls reputation data from Google Maps β€” star rating, review volume, how often the business responds, and a sentiment analysis of recent reviews.

Input

Required

Description

company_name

βœ… (one of these)

Company name

website

βœ… (one of these)

Website URL

address

β€”

Business address

google_maps_url

β€”

Direct Google Maps link

llm

β€”

AI model to use

Output

What you get

google_business_score

Star rating

last_review_date

Most recent review date

days_since_last_review

How long since the last review

reviews_response

Does the business respond to reviews?

reviews_response_rate

Response rate %

reviews_last_1_month

Reviews in the past month

reviews_last_3_months

Reviews in the past 3 months

reviews_sentiment

Positive, Negative, or Neutral

reviews_snippets

Sample review text

errors

Any issues that came up

πŸ”— Chain it: Use find_business_on_google first to get google_maps_url for best accuracy. Combine with enrich_company_firmographics for a full company picture.


Hotel Details β€” enrich_hotel_hospitality

Pulls key facts about a hotel or venue: room count, event space, pricing range, and service tier.

Input

Required

Description

company_name

βœ…

Hotel or property name

website

β€”

Hotel website URL

address

β€”

Hotel address or city/state

max_iterations

β€”

Research depth (max 20)

llm

β€”

AI model to use

Output

What you get

num_rooms

Total room count

num_event_spaces

Event/meeting spaces

event_capacity_value

Capacity number (sq.ft or seats)

lowest_price

Low end of nightly rate

highest_price

High end of nightly rate

avg_daily_rate

Average nightly rate

service_type

Full Service / Select Service / Motel

errors

Any issues that came up


Club Membership β€” find_country_club_membership

Returns the types of memberships a country club or private club offers (e.g.Β Golf, Social, Tennis).

Input

Required

Description

company

βœ…

Club name

website

βœ…

Club website URL

max_iterations

β€”

Research depth (max 20)

llm

β€”

AI model to use

Output

What you get

membership_types

Comma-separated list of membership types

errors

Any issues that came up


Theatre Seating β€” find_theatre_seating_capacity

Returns the total seating capacity of a theatre or venue, with a source citation.

Input

Required

Description

website

βœ…

Theatre website URL

llm

β€”

AI model to use

max_iterations

β€”

Research depth (max 15)

Output

What you get

seating_capacity

Total seats

source

Where the number was found

errors

Any issues that came up


Job Openings β€” job_openings

Checks how many open jobs a company has β€” including Indeed ratings, review counts, and a breakdown of roles matching your search titles.

Input

Required

Description

company

βœ…

Company name

website

β€”

Website URL

location

β€”

City and state

titles_to_search

β€”

Specific job titles to look for

llm

β€”

AI model to use

max_iterations

β€”

Research depth (max 20)

Output

What you get

indeed_rating

Company rating on Indeed

indeed_reviews

Review count

indeed_jobs_count

Total job count on Indeed

work_wellbeing_rating

Wellbeing score if available

jobs

Matched roles by title

total_count

Total openings found

sources

Where the data came from

errors

Any issues that came up

πŸ”— Chain it: Use with find_business_website to have a URL ready. Combine with enrich_company_firmographics to see if hiring activity correlates with company growth signals.


🎯 ICP Scoring

Does this company match your ideal customer profile?


ICP Formula Builder β€” icp_formula_builder

Takes a sample of your companies and builds a reusable scoring formula β€” either automatically from your data fields or from a plain English description of your ideal customer.

Input

Required

Description

sample_companies

βœ…

20–1,000 company records from your list

mode

β€”

general (auto-built) or custom (you define it)

prompt

β€”

Plain English description of your ideal customer (for custom mode)

formula

β€”

A pre-built formula structure (overrides prompt)

available_fields

β€”

List of field names/types in your data

llm

β€”

AI model to use

Output

What you get

formula

The scoring formula (save this for reuse)

validation

Stats on how your data distributes across the formula

missing_fields

Fields the formula needs that aren’t in your data

enrichment_suggestions

Cosmos agents to run to fill in the gaps

available_fields

Fields that were found in your data

errors

Any issues that came up

πŸ”— Chain it: Enrich your sample companies with enrich_company_firmographics, classify_industry, and classify_b2b_b2c first. Then save the formula output and pass it into icp_score for every company you want to grade.


ICP Score β€” icp_score

Grades a single company against your ICP formula β€” returning an A/B/C/D letter grade, a numeric score, and a breakdown of every criterion.

Input

Required

Description

company

βœ…

A company data record (dict)

formula

βœ…

Your saved formula from icp_formula_builder

Output

What you get

overall_grade

A, B, C, D, or unscored

composite_score

Numeric score (0–4)

criteria_grades

Grade for each scoring criterion

signals

Notes explaining the scoring

errors

Any issues that came up

πŸ”— Chain it: Run icp_formula_builder once to create your formula. Then run icp_score on every company in your pipeline. More enriched company records = more accurate grades.


πŸ€– AI Research

When you need a custom answer, not a structured data field.


OrbAI β€” orb_ai

A general-purpose research agent. Give it any question or instruction β€” it searches the web, reads pages, and returns a structured or plain-text answer.

Input

Required

Description

input

βœ…

Your question or research instruction

model

β€”

AI model to use

output_schema

β€”

JSON schema if you want a structured output

max_iterations

β€”

How many research steps to take (max 20)

Output

What you get

output

Final answer (text or structured object)

steps

Step-by-step trace of what the agent did

reasoning

Optional explanation of the reasoning

confidence

Optional confidence score

source

Where the answer came from

errors

Any issues that came up

πŸ”— Chain it: Use when no specific agent fits your question. Great for pulling custom fields that aren’t in any other agent β€” then feed results into icp_score.


Ask Orb β€” ask_orb

A quick one-question research agent. Ask it anything and get a short, sourced answer β€” perfect for table cells or quick lookups.

Input

Required

Description

question

βœ…

Your question

Output

What you get

verdict

Short answer: Yes/No/Unknown or a brief value

detail

1–2 sentences backing up the answer

source

Where the answer came from

errors

Any issues that came up

πŸ”— Chain it: Use after find_business_website or enrich_company_firmographics to answer follow-up questions about a company that aren’t covered by a dedicated agent.


πŸ”— Common Workflows

These are the most common ways to chain agents together.

Find and qualify any company from just a name

find_business_website β†’ enrich_company_firmographics β†’ classify_industry β†’ icp_score

Full SMB outreach chain

find_business_website β†’ find_small_business_owner β†’ find_person_linkedin β†’ find_email_waterfall + find_phone_waterfall β†’ phone_intel

Franchise location targeting

get_franchise_status β†’ find_franchise_contacts β†’ find_email_waterfall + find_phone_waterfall

Local business reputation check

find_business_on_google β†’ enrich_google_reviews + classify_location_type

Build and apply an ICP scoring system

[batch enrich with enrich_company_firmographics + classify_industry + classify_b2b_b2c] β†’ icp_formula_builder β†’ icp_score

Verify contact before outreach

find_phone_waterfall β†’ phone_intel β†’ (if CALL) β†’ reach out