Our machine learning team has brought dozens of successful projects to market. We’ll guide you through the process to help you minimize your risk and dodge the mistakes that push ML projects off course and over budget.
Pick the right problem
Your project begins with an intensive on-site Strategy Day.
It’s an exploratory, full-day session where we get to know your business in depth. We help you define the right problem to start with and choose the best approach based on your business, domain, goals, and data.
Preliminary validation of the solution helps to avoid costly missteps— like asking the wrong question, lacking sufficient data or choosing an overly complex problem.
Validate your solution
We leverage our engineering skill, experience, and pre-built compute infrastructure to arrive at PoC for your ML solution in 4 weeks or less.
Getting to PoC quickly and affordably is essential to get buy-in from your team early in the process. And to minimize the risk of a production solution that falls short of expectations.
At this stage, we validate the algorithm to be sure that it solves your problem. And then make adjustments or redirect our efforts based on what we’ve learned.
Your ML solution goes live
Our team works in 2-week sprints to develop your solution, with bi-weekly calls to discuss our progress. Real-time communication via Slack or MS Teams keeps issues moving forward and helps bypass communication bottlenecks.
Then we take the solution live to evaluate its performance in your production workflow.
Finally, we automate everything, fine-tune the details, and hand over your solution— congratulations, your data is now officially working for you!
Build on your success
A successful ML application isn’t an endpoint— it’s a launching pad. So what’s next?
We can help you quickly scale your solution to other locations, projects or products. Or build completely new strategies for helping to slay inefficiencies, improve processes or boost revenue.
Or your engineering team can take it from here and start building on what they’ve learned. We’re here to consult when your team gets stuck.
Our engineers know how to specify projects and communicate results. You don't need to deal with ‘sales team.’
We work with a few clients at a time, so you have our full attention. The CEO & CTO manage your project personally and know every detail inside and out.
You never get a bill from us for hours worked. We agree to a project price and that’s what you pay. No cost overruns. No overtime.
We work alongside your team and they get full access to our code throughout the project. So your team can learn from us to level up their ML skill set.
Proactive project management and a custom-built solution that works:
Expert interviews, data analysis, and academic research
Feature engineering, algorithm selection, and tuning
Keep it, use it and repurpose it as needed
From thousands to billions of decisions per day
Auto-importing data, exporting results, or API accessibility.
Full access to plans, issues & our git repo
We keep the project on schedule and you in the loop
Weekly engineering calls & bi-weekly milestone presentations
Slack or Microsoft Teams to keep things moving
We use tools that allow for rapid iteration, reproducibility, easy scaling, and solid architecture. These are the standard building blocks of a Data Revenue machine learning solution: