Your data sources are messy. Your data layer doesn’t have to be.
We build data pipelines that connect scraped data, third-party feeds, and internal systems into one reliable, queryable infrastructure. Matching, deduplication, and error handling included.
We respond in 12 hours on average
What We Build
You depend on outside data sources, but each one arrives in a different format, on a different schedule, with different failure modes.
One pipeline that normalizes everything before it reaches your systems.
We connect scraped websites, partner feeds, government databases, and SaaS exports into a single clean data layer. Your team queries one source of truth, not twelve spreadsheets.
The same entity appears differently across sources. Products, companies, or people don’t match up without manual work.
Automated matching with configurable precision and recall trade-offs.
We build matching pipelines that reconcile records across sources using deterministic and fuzzy logic. You define what “same” means for your business. We make the system enforce it at scale.
Some data sources break regularly. Formats shift, fields disappear, and nobody notices until a report is wrong.
Built-in validation, alerting, and fallback logic for unreliable inputs.
External data is inherently unstable. We design pipelines that detect anomalies, quarantine bad records, and alert your team before broken data reaches production. When a source changes shape, the pipeline adapts or fails loud.
Your internal databases hold valuable context, but connecting them with external feeds requires manual exports and fragile scripts.
Automated joins between your systems and outside data, refreshed continuously.
We bridge internal databases (your CRM, ERP, product catalog) with external feeds so enrichment happens automatically. No CSV uploads, no copy-paste, no stale snapshots.
Your team needs dashboards and ad-hoc queries, but the data is scattered across systems that don’t talk to each other.
Fast, queryable analytical databases with visualization built in.
We set up analytical datastores optimized for the queries your team actually runs. ClickHouse for speed on large volumes, Postgres for flexibility, Superset for self-serve dashboards your team can own.
Critical data is trapped in legacy systems, old databases, or web portals with no export capability.
Extract, normalize, and load legacy data without vendor cooperation.
When the old system has no API and the vendor won’t help, we combine scraping, database extraction, and transformation to rescue your data and load it into modern infrastructure.
How We Deliver
Managed Data Pipeline
We build, host, and operate your pipelines end to end. You consume clean data.
Self-Hosted Infrastructure
We build on your infrastructure, whether cloud, dedicated servers, or on-premise. Your security perimeter, your rules.
Dashboard & Reporting
Self-serve dashboards your team can query, filter, and export from without engineering help.
API Layer
A documented REST API that exposes your unified data to any system that needs it.
Database Access
Direct access to a hosted analytical database, ready for your BI tools or custom queries.
Batch File Delivery
Structured files delivered on your schedule, in the format your downstream systems expect.
Why Stratalis for data engineering
Scraping-native engineers
Most data engineering teams treat external data as someone else’s problem. We started there. Our engineers understand unstable, adversarial data sources at a level that pure data teams don’t. That experience shapes every pipeline we build.
Full-stack, not just pipelines
We write production software, not just SQL scripts. Python, TypeScript, Kotlin, FastAPI. When a pipeline needs a custom UI, a webhook handler, or an API layer, we build it ourselves. No handoff to another vendor.
Non-functional requirements, thought through
We think about what you might not have specified. Performance at 10x your current volume. Required uptime. Precision vs. recall trade-offs in matching. Lifetime cost of the infrastructure. We raise these questions before they become problems.
Cost-aware engineering
We don’t overengineer. A ClickHouse instance handles what others solve with a Spark cluster. A well-written Python script replaces a managed ETL service. We optimize for your real requirements, not for résumé- driven architecture.
Fixed-price quotes
We scope carefully and quote a fixed price. No hourly billing, no open-ended retainers. You know the cost before we start.
"Clean data, ready to use upon delivery, and a provider that adapts as our needs evolve. Stratalis is reliable, responsive and competitive."
See projects built by Stratalis
Competitor Price Collection for a Major Insurer
Powering a Grocery Price Comparison App
Messy data sources? We can fix that.
Tell us what you’re working with. We’ll tell you what a clean data layer looks like and what it costs.
Get a QuoteOur data engineering solutions
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Give your support agents AI-powered search across your ticket history. Find similar cases in seconds. Works with any helpdesk, deployed for as low as 1,200 EUR.
ATS Data Migration
Migrate your applicant tracking system with full candidate history, pipeline stages, and custom fields intact. Flat-rate pricing, done in 1 to 3 weeks.
Automated Email Processing
Automate the processing of incoming emails: parse content, extract data, trigger actions. Invoices, orders, alerts, reports. Handled without human intervention.
Automated Job Ad Posting
Automate the posting, updating, and removal of job ads on any job board. One submission, every platform, always in sync.
Who It's For
Our Tech Stack
Data Engineering
Pipeline orchestration, transformation, and analytical storage
Software Development
Production-grade code for APIs, services, and custom tooling
Web Scraping
External data collection built on our core scraping infrastructure
Use Cases
FAQ
If you have a data engineering team, call us when they need web scraping or struggle integrating scraping-sourced data with internal systems. If you don’t, we’re much cheaper than building one.
Our hourly rate isn’t particularly cheap, but we focus on high-ROI, right-sized engineering with low overhead. For small and midsized projects, and customers who make decisions fast, we beat larger firms on speed, cost, and signal-to-noise.
ClickHouse and Postgres are our defaults for analytical and relational workloads. We have an engineering mindset: we use open-source data engineering products when they’re right, and we program custom solutions when that’s what the problem actually calls for. We come from both the data and software worlds.
Yes. We regularly take over from or work alongside in-house scraping setups that outgrew their original design. We’ll audit what you have, keep what works, and rebuild what doesn’t.
It depends on the project. Every pipeline includes validation rules, anomaly detection, and alerting. Bad records get quarantined, not silently passed through.
For scraping-sourced data, we can go further with human or AI-based sampling, independent of the main pipeline, to catch errors that automated validation alone would miss. You’ll know when something breaks before your reports do.
Fixed quotes based on the number of sources, data volume, and complexity of transformation and matching logic. We scope carefully so the price holds. No hourly billing.
Most projects go from kickoff to production data in 2 to 6 weeks, depending on the number of sources and the complexity of matching rules. We scope fast and start fast.