A small team that ships data systems for a living.
Serenlytics was founded by engineers who got tired of watching good models die in PowerPoint. We work hands-on with your team, ship production systems on a fixed timeline, and document the result so you can run it without us.
A simple four-step engagement.
Each step has a written deliverable and a stop point. If the next phase isn't the right move, we say so.
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Step
01
Discover
Two weeks. We sit with your data, your team, and your KPIs. Output: a 1-page scope, a baseline, and a go/no-go recommendation.
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Step
02
Design
Architecture, data contracts, evaluation plan, and rollout strategy — reviewed with your engineers before any code lands.
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Step
03
Build
Fixed-fee delivery in your repo and your CI. Weekly demos. The work merges as it's reviewed — no big-bang handoff.
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Step
04
Optimize
Post-launch tuning, monitoring, and a runbook. Then we step out — or stay on retainer if that's what the system needs.
Meet the engineers.
We are data engineers and machine learning practitioners who have built systems at scale for top tech companies.
Solomon Owerre, Ph.D.
Founding ML Engineer
Former Director of Data Science at Loblaw and Sr. ML Engineer at Wiser Solutions. Specializes in architecting highly optimized data warehouses, LLM-driven workflow automation, and building scalable data pipelines.
Joachim Nsofini, Ph.D.
Founding Data Scientist
Senior Data Scientist at FCC / FAC Canada. Quantum Information physicist turned data scientist. Specializes in predictive modeling, NLP, and designing robust data processing pipelines for enterprise scale.
What we won't compromise on.
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Smallest change first
We propose the narrowest scope that moves the metric. Bigger engagements happen after the first one earns the next.
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Reviewable artifacts
Pull requests, design docs, eval reports. Nothing important lives in a private notebook.
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No lock-in
We use the stack you already pay for. When we leave, your team can extend the system without us.
Ready to scope the first phase?
A 30-minute call is usually enough to know whether we're a fit.