AI-ifying Business Processes
AI applications range from simple rule-based systems to self-learning models, to complex multi-agent systems that...
CONSTRUCTION & REAL ESTATE
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Discover how crafting a robust AI data strategy identifies high-value opportunities. Learn how Ryan Companies used AI to enhance efficiency and innovation.
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LEGAL SERVICES
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Discover how a global law firm uses intelligent automation to enhance client services. Learn how AI improves efficiency, document processing, and client satisfaction.
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HEALTHCARE
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A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery.
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LEGAL SERVICES
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Learn how Synaptiq helped a law firm cut down on administrative hours during a document migration project.
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GOVERNMENT/LEGAL SERVICES
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Learn how Synaptiq helped a government law firm build an AI product to streamline client experiences.
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Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. |
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I’ve seen many different approaches to AI initiative investment over the past 10 years consulting in this space. Whether you’re an innovator in a small or large business, it’s critical to understand successfully investing in AI is not just about hiring data scientists.
It’s about:
On the smaller side of the scale, our clients tend to initially spend tens of thousands of dollars to “test the waters”.
The client has a use case.
They have a hypothesis of what their data can solve.
And they have a set of data.
In nearly all cases, we’ve found, after multiple rounds of training and tuning models, that the client’s data isn’t as robust as they thought. Upon this realization, clients spend months or even years going back and improving their data on their own before they are ready to actively work on training AI models again.
On the other end of the spectrum, larger companies typically spend hundreds of thousands of dollars to “dip their toes” in AI. While they, too, come to the table with a use case, hypotheses, and data set, they often provide:
Thoughtful insight where they have a problem that could likely be solved by their data.
Engineers that know the data and help prepare the data.
Domain experts that can explain the data.
More times than not, client engineers and domain experts learn through our consulting process how AI really works and are inspired to improve their data along the way. The end result is more production-ready models, sooner.
In the end, those companies that underinvest in AI fail out of the gates and fall behind. And a failed first AI project means months or years of reconsideration while competitors eat their lunch.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.
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