Ever tried to research a potential client minutes before a sales call, only to find that your expensive data provider has outdated information? Yeah, me too. That's precisely why I spent last weekend building something different.
Here's a scenario that might sound familiar:
Your sales rep is about to jump on a call with a hot prospect. They quickly look up the company in your fancy data enrichment tool and confidently mention, "I see you recently raised your Series A!" Only to hear an awkward laugh followed by "Actually, that was two years ago. We just closed our Series C last month."
Ouch.
Static databases, no matter how comprehensive, share one fundamental flaw: they're static. By the time information is collected, processed, and made available, it's often already outdated. In the fast-moving world of tech and business, that's a real problem.
What if instead of relying on pre-collected data, we could:
That's exactly what we're going to build today using Linkup's API. The best part? It's just 50 lines of Python.
Time to write some code! But don't worry - we'll break it down into bite-sized pieces that even your non-technical coworkers could understand (well, almost ?).
First, let's create our project and install the tools we need:
mkdir company-intel cd company-intel pip install linkup-sdk pydantic
Nothing fancy here - just creating a new folder and installing our two magical ingredients: linkup-sdk for fetching data and pydantic for making sure our data looks pretty.
Before we start grabbing data, let's define what we actually want to know about companies. Think of this as your wishlist:
# schema.py - Our data wishlist! ?
from pydantic import BaseModel
from typing import List, Optional
from enum import Enum
class CompanyInfo(BaseModel):
# The basics
name: str = "" # Company name (duh!)
website: str = "" # Where they live on the internet
description: str = "" # What they do (hopefully not just buzzwords)
# The interesting stuff
latest_funding: str = "" # Show me the money! ?
recent_news: List[str] = [] # What's the buzz? ?
leadership_team: List[str] = [] # Who's running the show? ?
tech_stack: List[str] = [] # The tools they love ⚡
This is like telling a restaurant exactly what you want in your sandwich. We're using pydantic to make sure we get exactly what we ordered!
Now for the fun part - the engine that makes everything work:
# company_intel.py - Where the magic happens! ?
from linkup import LinkupClient
from schema import CompanyInfo
from typing import List
class CompanyIntelligence:
def __init__(self, api_key: str):
# Initialize our crystal ball (aka Linkup client)
self.client = LinkupClient(api_key=api_key)
def research_company(self, company_name: str) -> CompanyInfo:
# Craft our research question
query = f"""
Hey Linkup! Tell me everything fresh about {company_name}:
? The name of the company, its website, and a short description.
? Any recent funding rounds or big announcements?
? Who's on the leadership team right now?
?️ What tech are they using these days?
? What have they been up to lately?
PS: Only stuff from the last 3 months, please!
"""
# Ask the question and get structured answers
response = self.client.search(
query=query, # What we want to know
depth="deep", # Go deep, not shallow
output_type="structured", # Give me clean data
structured_output_schema=CompanyInfo # Format it like our wishlist
)
return response
Let's break down what's happening here:
Now let's wrap it in a nice API that your whole team can use:
mkdir company-intel cd company-intel pip install linkup-sdk pydantic
What's cool here:
Time to see our creation in action:
# schema.py - Our data wishlist! ?
from pydantic import BaseModel
from typing import List, Optional
from enum import Enum
class CompanyInfo(BaseModel):
# The basics
name: str = "" # Company name (duh!)
website: str = "" # Where they live on the internet
description: str = "" # What they do (hopefully not just buzzwords)
# The interesting stuff
latest_funding: str = "" # Show me the money! ?
recent_news: List[str] = [] # What's the buzz? ?
leadership_team: List[str] = [] # Who's running the show? ?
tech_stack: List[str] = [] # The tools they love ⚡
And voilà! Fresh, real-time company data at your fingertips!
Want to make it even cooler? Here are some fun additions you could make:
# company_intel.py - Where the magic happens! ?
from linkup import LinkupClient
from schema import CompanyInfo
from typing import List
class CompanyIntelligence:
def __init__(self, api_key: str):
# Initialize our crystal ball (aka Linkup client)
self.client = LinkupClient(api_key=api_key)
def research_company(self, company_name: str) -> CompanyInfo:
# Craft our research question
query = f"""
Hey Linkup! Tell me everything fresh about {company_name}:
? The name of the company, its website, and a short description.
? Any recent funding rounds or big announcements?
? Who's on the leadership team right now?
?️ What tech are they using these days?
? What have they been up to lately?
PS: Only stuff from the last 3 months, please!
"""
# Ask the question and get structured answers
response = self.client.search(
query=query, # What we want to know
depth="deep", # Go deep, not shallow
output_type="structured", # Give me clean data
structured_output_schema=CompanyInfo # Format it like our wishlist
)
return response
We've been using this in production for our sales team, and it's been a game-changer:
The possibilities are endless! Here are some ideas to take it further:
Ready to build your own? Here's what you need:
The days of static databases are numbered. In a world where companies pivot overnight, raise rounds weekly, and change their tech stacks monthly, real-time intelligence isn't just nice to have—it's essential.
What we built here is just the beginning. Imagine combining this with:
Have you built something similar? How do you handle the challenge of keeping company data fresh? Let me know in the comments!
Built with ☕ and a healthy obsession with fresh data
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