Decisions your data can actually back up
We build the pipelines, warehouses and dashboards that turn scattered data into something your team can trust: instrumentation you can rely on, and answers you can act on.
What data and analytics covers
Getting your data together, making it trustworthy, and turning it into answers your team can use.
Data pipelines
Moving data from your product and tools into one place, reliably.
Data warehousing
A single source of truth, structured for the questions you actually ask.
Product analytics
Event instrumentation and the metrics that tell you what your users really do.
Dashboards and reporting
The numbers your team needs, current and self serve.
Data quality
Validation and monitoring, so no decision ends up built on a broken pipeline.
ML and AI foundations
The clean, structured data that AI features quietly depend on.
Product Growth acts on the instrumentation this builds. AI Engineering builds on the data foundation.
What we work with
Warehouses
Pipelines and transformation
Product analytics
Streaming
BI and dashboards
Quality and governance
Everything downstream is only as good as the instrumentation and data quality under it. A beautiful dashboard on a broken pipeline is worse than no dashboard at all, because people believe it, so this is where we start.
How we work it
Instrument for the question
We start from the decisions you're trying to make and instrument for those, instead of collecting everything and hoping some of it turns out useful.
Make it trustworthy
We add validation and monitoring to the pipelines, because a number nobody trusts gets argued with instead of acted on.
Put it where people work
We deliver the answers in dashboards and tools your team can use without coming back to us, so the data keeps serving you after we're gone.
Who does this work
Data Engineers
Pipelines, warehousing and the transformation layer.
Analytics Engineer
Metric definitions, dashboards and the models the business reads.
Tech Lead
Architecture, tooling and the build versus buy calls.
Where this capability fits
Success cases
Wrist Goal is a smartwatch app delivering live football scores and match events to Huawei wearables, built by Somnio and launched natively on HarmonyOS NEXT with a template-based architecture ready to scale to future tournaments.
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We partnered with the Canadian Automobile Association (CAA) to elevate member services through technology, delivering a seamless experience across Ontario.
What our clients say
“Their approach started with a Product Discovery phase, including user research, UI/UX design improvements, and technical assessments to ensure scalability. Their proactive work made a real difference in the project's success”
“Somnio Software has delivered an MVP that meets the changing needs of AI users. They've communicated effectively, have been highly responsive, and their project management is excellent. Their developers have become thought partners.”
Ready to Start Your Journey?
I would love to talk to you about your project or needs.
Fill in the form or send us an email to hello@somniosoftware.com
Got an idea? We’ve got the skills.
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Schedule a call
Feel free to select a time at your convenience!
FAQs
Still have some doubts?
No worries, here are some frequently asked questions that may help you.
Data engineering builds the pipelines and infrastructure that move and store data reliably. Data analytics uses that data to answer questions and inform decisions. One makes the data trustworthy and available, the other turns it into something you can act on, and a healthy data practice needs both.
A data warehouse is a central store that brings data from your product and tools together, structured for analysis. You probably need one once answering a basic question means pulling from several systems by hand, or once different teams keep quoting different numbers for the same thing.
With the decisions you're trying to make, not the tools, and it's more common than you'd think. We define the handful of metrics that actually matter, then fix the instrumentation behind them. Most analytics problems are really a tracking problem wearing a dashboard.
Yes, and usually that's the case. We work inside your warehouse and tools and gently point out where the setup is holding back the questions you need to answer.
Usually, yes. AI features are only as good as the data behind them, and clean, structured, accessible data is what makes the difference between a demo and something dependable. → AI Engineering