Automated data pipelines
Bring data from your tools into one consistent dataset. Keep reports and analytics supplied with fresh information without repeated exports.
Sound familiar?
Garbage in, garbage out
Your dashboards and reports are only as good as the data behind them. And right now, that data is messy, incomplete, and inconsistent.
Duplicate records everywhere
The same customer appears three times in your CRM. The same product has five different names. Nobody trusts the numbers.
Spreadsheet chaos
Critical business data lives in dozens of spreadsheets with different formats, naming conventions, and update frequencies.
Not AI-ready
You want to use AI and analytics, but your data isn’t structured or clean enough to feed into any model. The foundation is missing.
Hours spent cleaning data manually
Your team spends more time preparing data than analyzing it. By the time it’s clean, the opportunity has passed.
Clean once. Messy again.
A one-off cleanup helps until the next import. New records bring back the same problems, and your team starts fixing them all over again.
Reliable automation starts with reliable data.
26%
of organizational data was considered untrustworthy by data leaders
Salesforce · State of Data & Analytics, 2025 ↗84%
said their data strategy needed a complete overhaul for their AI ambitions to succeed
Salesforce · State of Data & Analytics, 2025 ↗Think about your team
Would you trust this data to trigger an action?
Check a sample for duplicates, missing fields and conflicting records. Fix the data for one useful workflow first.
Estimates and responses from data and analytics leaders in Salesforce’s 2025 global research.
What we build.
Automated data pipelines that continuously clean, transform, and prepare your data – so it’s always ready when you need it.
Data cleaning & deduplication
Turn duplicate records into one reliable view. Standardize names and formats, preserve history, and send uncertain matches to your team.
Data quality monitoring
Catch missing fields, unexpected values, and failed imports before they affect decisions. Give your team a clear list of what needs fixing.
Data models & relationships
Connect customers, orders, products, and payments in a structure that reflects your business. Make the questions you ask easier to answer.
AI-ready datasets
Organize and label the information your AI needs. Build consistent datasets for the task, with checks for gaps and sensitive data.
Format standardization
Agree how dates, currencies, units, and categories should look. Make data from different tools comparable without spreadsheet fixes.
Historical data imports
Bring older records and spreadsheets into your new data structure. Keep the history your team needs for comparisons and decisions.
Source & change history
Trace a number back to where it came from and see how it changed. Resolve questions without searching through old exports.
Validation before release
Check totals, required fields, and record counts before data reaches your reports. Hold incomplete batches for review instead of passing problems downstream.
We’ve done this before.
OVB Moja Firma
Made sales comparisons reliable with cleaner data.
- Reported time saving on repetitive tasks
- 20%
- Monthly active users
- 2,500
“Strong technical skills with a deep understanding of our business.”
Marian Pogran, OVB
OVB Aero
Brought scattered client records into one trusted view.
- Tools integrated
- 5
- Daily users
- 2,000
- Efficiency gain reported in a user survey
- 20–30%
“Felt like an extension of our internal team.”
Marian Pogran, OVB
BCB Cloud
Kept machine readings usable through signal loss.
- Machine usage without on-site checks
- Remote data
- Service tasks and technician schedules
- One plan
“Outloud helped us turn a complex telemetry concept into a market-ready product through strong collaboration and agile execution.”
Tomas Oslanec, CEO, Black Control Box
How we get there.
01
Audit your data
We inventory every data source, assess quality, identify duplicates and gaps, and understand how data flows through your organization.
02
Design the data model
We design a clean schema that supports your analytics and AI goals. We define transformation rules, deduplication logic, and quality standards.
03
Build the pipelines
We build automated ETL pipelines that clean, transform, and load your data on schedule. With monitoring and alerting built in.
04
Validate and maintain
We verify data quality, set up ongoing monitoring, and provide documentation so your team can trust and extend the data foundation.
50+
Projects delivered
10+
Years of experience
4.9*
Rating on Clutch
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