By The Numbers
Unified Analytics.
Scalable Architecture.
We don't just build dashboards. We construct the underlying data fabric that allows your business to scale, predict, and thrive across European markets.
Sound familiar?
We've seen these challenges slow down data teams at startups and growing SMEs across US and Europe alike.
Overwhelmed by Manual Work
Your team spends 60% of their time writing custom SQL queries and debugging broken pipelines instead of analyzing data.
Off-The-Shelf Tools Don't Fit
Tableau and PowerBI are great for dashboards, but they don't understand your unique business logic or data sources.
Scattered Data Silos
Customer data in Salesforce, product analytics in Mixpanel, financials in QuickBooks. No single source of truth.
We build the custom connectors and AI automation you actually need.
Proof, not promises.
Real results from real clients. Every metric is verified on Clutch.co.
6 Hours → 11 Minutes
Cloud Solutions (Bulgaria)
Reduction in data processing time
We integrated AI technologies to automate their data analysis workflows. What used to take a full workday now completes during a coffee break.
Get Similar ResultsThe Result
By shifting from manual processing to an AI-native pipeline, we reclaimed 97% of their engineering bandwidth.
The Difference In Code
Old Way
# Old Way: Manual ETL Script
import pandas as pd
import psycopg2
from datetime import datetime
# Connect to database
conn = psycopg2.connect("...")
cursor = conn.cursor()
# Extract data
cursor.execute("SELECT * FROM users WHERE created_at > %s", (datetime.now(),))
users = cursor.fetchall()
# Transform data
df = pd.DataFrame(users)
df['full_name'] = df['first_name'] + ' ' + df['last_name']
df['age'] = (datetime.now() - df['birth_date']).dt.days / 365
# Load to warehouse
for index, row in df.iterrows():
cursor.execute(
"INSERT INTO analytics.users VALUES (%s, %s, %s)",
(row['id'], row['full_name'], row['age'])
)
conn.commit()
# Takes 6+ hours for 1M records
# Breaks on schema changes
# No error retry logicWith Beneath Analytics
# With Beneath Analytics
from beneath import Pipeline
pipeline = Pipeline("user_analytics")
.extract("postgres://prod/users")
.transform("enrich_user_data")
.load("snowflake://warehouse/analytics")
.schedule("@hourly")
.deploy()
# Processes 1M records in 11 minutes
# Auto-adapts to schema changes
# Built-in retry & monitoringWho This Is For
You're a great fit if:
- You have data scattered across 5+ tools
- You need custom AI/ML workflows, not generic dashboards
- Your team is 2-50 people (we're built for agility)
- You value speed over bureaucracy
We might not be the right fit if:
- You strictly require a simple dashboard solution
- You prefer an off-the-shelf SaaS product
- You require complex enterprise procurement
- You don't have enough data to analyze yet
Client Success Stories
Precision engineering leads to measurable business impact. Here is how we operationalize success for our partners.
"Beneath Analytics integrated AI technologies to leverage our products' raw data. They created custom algorithms... and reduced human bandwidth on manual data analysis."
Petar Nikov
Founder & CEO | Cloud Solutions
>> Impact_Metric: Efficiency: User Bandwidth Reduced
"The engagement helped automate the process of generating financial forecasting reports. The team is hard-working, organized, and knowledgeable."
Haresh Makwana
Founder | Haresh G Makwana & CO
>> Impact_Metric: Output: Automated Forecasting
"He's extremely selfless and any client would be lucky to work with him. The data they collected will have a strong impact on our business moving forward."
Yongmin Cho
COO | Say Global Inc.
>> Impact_Metric: Impact: Data-Driven Strategy