Our AI Impact

 for the health of people



 Our AI Impact

 for the health of planet



 Our AI Impact

 for the health of business



“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.” 
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    ⇲ Implement & Scale
    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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      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
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        Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring. 
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          This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions. 
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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. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

                  Frequently asked questions


                  Frequently asked questions



                  1. How could Synaptiq help my organization?

                  We help companies determine their strategy and roadmap for data and AI, lay the foundation, and address a growing number of use cases in many industries. For example, here is a walk-through of how we work with construction companies.

                  1. How do I get started with Synaptiq?

                  Two options:  1) Find a use case that urgently needs to be addressed and has datasets to automate; 2) Engage with Synaptiq on a Strategy or Discovery project so we can help you build a business case. Feel free to reach out – we're happy to chat with you about your idea.

                  1. How do I know if my data is mature enough for AI development?

                  It depends on your use case. For every AI project, there is an initial analysis of your existing data to determine if it is robust enough to build a worthwhile model.  Here's a helpful article on data maturity, internal teams, and AI engagements.

                  1. What are some examples of having “good data” for my problem?

                  Examples include: 1) The data covers most, if not all, the cases the AI model will experience in a real environment; 2) A significant portion of the data for a supervised model is labeled; and 3) The quality of the data is robust. Important note: we typically don’t know 100% if the data is enough until we build the first model – which is why we start with a POC. Contact us to learn more.

                  1. Are there tools that already exist in the market that can solve my problem?

                  It depends on the problem. If you are trying to classify standard objects (e.g., cats), then yes. There are pre-built models and cloud services that can do this. But, it’s rarely the case that an existing solution will fit your exact need especially for critical business problems. Generic data for mass-market tools yields generic results. Here's a case study on how a global immigration law firm realized custom was a must-have.

                  1. Can my internal team handle this project or do I need an outside vendor?

                  It depends on your team’s capabilities. It’s rare to find a data scientist with enough real-world experience to guide you down the most efficient path. But - if you have them, they are a rare breed and a hot commodity! Here's an article on how you can get to the bottom of this quickly.

                  1. How do I go about picking the right vendor?

                  1) Make sure whoever you pick is bringing real-world experience to the table; 2) Ensure they are a good chemistry fit and excellent communicators; 3) Don’t make decisions purely based on money as you will fail 99% of the time; 4) Start small so that you can make sure the relationship works before investing heavily. Contact us to learn more about how we work.

                  1. How do I pick the right internal leadership to manage the vendor relationship?

                  The ideal leadership structure is within both the business and technology organizations in the company. The executive sponsor is typically a business leader who is looking to save money or make money. The technical leader is someone who can coordinate internal technical resources to get things done.  Read more about building the right team to get the job done.

                  1. How do I build a business case and “prove the ROI” to my CEO?

                  It’s not as simple as this. AI is often an experiment – not a definite thing.  We recommend getting a small budget ($50 - $100k) for a POC and, at the culmination of the POC, build a more robust business case for greater investment. Here's a helpful article on our approach.

                  1. How do I get the budget I need to accomplish my goals?

                  Most decision-makers have a discretionary budget, and this is typically the best place to earmark money for a POC. Alternatively, depending on the organization, there may be opportunities to pitch for additional funds through quarterly budgeting processes. Ultimately, a significant investment in AI requires a series of presentations, financial models, and an evolving business case to plan for a significant amount of money in the annual budgeting process. Feel free to reach out if you would like to set up a call.

                  1. What are the implications of AI failure?

                  It can be catastrophic if you choose to try to solve a problem that is too hard and expensive to do in a reasonable amount of time. Not only will this ruin the support for future AI projects, but it will also put your organization at a competitive disadvantage. The key is to start small. Failing on a small project is part of the “process of learning” – and the lessons learned from these failures can help your organization truly understand the investment required to be successful. There are no shortcuts. Here's a good article with our biggest pointers.

                  1. How do I guarantee success?

                  You don’t know it will be successful 100% - success depends on a multitude of factors including aligned expectations, available data, and your team’s ability to support Synaptiq’s project requirements. Synaptiq has worked with over 55 clients across 20 industries and has learned through experience what it takes to be successful. But, Synaptiq is only one party of two that makes a project successful. Communication is paramount. Here's a case study on how we built a great relationship – and ultimately, a great product – with a financial services firm.

                  1. How do I do everything I can on my end to make sure this project is as successful as possible?

                  Start with strategy and roadmapping. Ensure you have the data you need; even if it’s disorganized we can help you get it in order and a strategy and governance in place. Make sure your resources are aligned to support the needs of the project. Provide “air cover” that allows your resources to make this project a priority. Remove any blockers for the project to be successful - e.g., system access. Keep your leadership team informed of progress including challenges so they, too, learn what it takes to create AI solutions (don’t surprise them at the end with a big “reveal.”) If you want to learn more, let's set up a call.