Integrating Internal Teams with External Developers for Improved Agility


According to a recent survey from Forbes and the Project Management Institute, 92% of executives believe that organizational agility, or the ability to rapidly respond to market conditions and external factors, is critical to business success. An additional 84% agree that organizational agility is necessary to succeed in digital transformation.

What does your company need to improve its agility and succeed in digital transformation? Maybe it’s some additional expertise to get a specific project across the goal line, or maybe it’s software development resources to boost an internal team that’s already at capacity. Whatever the reason, partnering with an external developer can often be the right solution for organizations that need to expand their internal expertise, capacity, and project load to remain agile and competitive.

Software Development - Exploring the Field

With an estimated 100,000+ software and IT services companies in the United States alone, there are a lot of providers to choose from. How can companies narrow down the field to determine which partner is the best fit?


In our experience, business leaders want a software development partner that’s proven to deliver high-quality solutions quickly. Perhaps equally important is a partner with the resources and infrastructure to take ownership of a significant project (aka: they don’t need a ton of oversight to get the work done). While these are pretty evident differentiators, one that might not be so readily apparent is how well an external developer is able to integrate with internal teams.

Integrating internal IT teams with external developers can be difficult, but it’s absolutely critical to the success of development projects. We’ve found that the most successful developers have a “team” mindset that informs how they interact and integrate with their clients.


Three Qualities that Determine a “Good Integration” Partner

How does this “team” mindset manifest itself? Here are three qualities to look for in a provider that can indicate how well they will be able to integrate with your teams:

  1. Are they an extension of your team?
    A good software development partner will fully and deeply internalize the strategy, goals and culture of an internal team. They’ll work hand-in-glove with internal resources to develop solutions, just as if they worked directly for the company rather than an outside third party.

  2. Do they have ramp up and ramp down capabilities?
    Within every project work will ebb and flow. For example, there’s always a steep bump in the initial development phase of a project, but when the project is in maintenance mode, the work level is much lower. A good software development partner will be able to scale up and scale down depending on the level of work necessary.

  3. Can they provide overflow assistance on demand?
    What happens when you’re under deadline and a critical internal resource gets sick? What if your organization has an opening on its team as it looks for the right full-time candidate? How do you fill these gaps? A development partner with a “team” mindset will step in to offer overflow assistance on-demand, helping organizations plug the holes with a resource that already has deep knowledge of the organization and its internal workflows.


At DVmobile, we pride ourselves on the relationships we have with our clients. One of our most distinctive characteristics is how we approach working with internal teams. Their success is our success, their failures are our failures – we truly embrace the “team” mindset and, as a result, we have long-standing, successful relationships with clients. And our clients have access to a trusted partner who can help them be more agile and more competitive.

For more information about partnering with DVmobile, visit our homepage or contact us directly.


Jamie Murphy

Guest blogger for DVmobile, busy mama, & strategy wiz.


Opportunities for Accessible IoT


This Thursday, we celebrate Global Accessibility Awareness Day (GAAD). GAAD was started seven years ago to encourage developers, inventors, and businesses to adhere to the notion of “technology as the great equalizer” by thinking about how their services and products can best serve people with different disabilities.


The World Health Organization (WHO) estimates that more than 1 billion people globally need one or more assistive products. With the global elderly and disabled assistive devices market expected to surpass $26 billion by 2024, developing accessible IoT technologies is another way for companies to do well by doing good.

Opportunities for Accessible IoT

Image recognition, voice and touch control, and NLP are making our smart devices even smarter. And when they’re engineered to be accessible, they can significantly enrich the lives of people with disabilities – giving them greater independence and dignity.

For example:

  • Amazon’s Echo gives blind and mobility-challenged people more control over their environments – allowing them to activate smart home devices, turn on music, and even make purchases online with voice control.

  • Philips’ HUE Light Bulb can be set up to help people with cognitive impairments navigate through the house or remind them about things they still need to do.

  • Nortek’s 2Gig home security devices help people with disabilities lock their doors, turn on their alarms, and even communicate with a live representative in case of emergency – all with the touch of a finger.

These products weren’t created specifically for people with disabilities, but they greatly benefit from their development.

IoT Accessibility Challenges


While there is tremendous opportunity, implementing accessibility standards within IoT solutions is very complicated. That’s because there is no universal set of accessibility standards for IoT. Mobile accessibility is different from web accessibility, which is different from kiosk accessibility. And IoT, which integrates a tangible product with an accompanying app, website and/or web service, faces even more complex challenges.  With IoT developers have to consider both the physical and web/mobile/kiosk requirements necessary to create solutions that are accessible for all people across multiple interfaces.

DVmobile is working with our partners to understand the unique challenges of developing accessible IoT, SaaS/PaaS/PDaaS, and mobile solutions. For example, we’re well connected with the Blind Institute of Technology, the leaders in educating companies on how to hire and cultivate success for the visually impaired. While we don’t have all the answers around IoT accessibility, we know that integration and collaboration with the communities most affected by accessibility challenges is a good place to start.

To learn more about how DVmobile works to build accessible solutions for our clients, connect with us:


Lauren Kenney

Digital Alchemist at DVmobile, hot salsa lover, and automation expert.


Designing Intelligent Apps Just Became a Whole Lot Easier


“Easy” probably isn’t the first word that comes to mind when you think of machine learning (ML) and AI. But that might not be the case for long.

While the complexity and capability of ML continue to grow – e.g. in the last year alone, deep learning has significantly advanced NLP, GANs and recommender systems– the ML software development process is getting simpler.

Use ML Without Reinventing the Wheel

That’s because, in the race to determine who will become the industry standard AI platform, some of the industry’s major players are opening up their AI capabilities to third-party developers. For example:

  • Google’s AutoML allows developers to create ML software through a drag-and-drop process.

  • AWS’ APIs and Deep Learning AMIs help developers add intelligence to their products in a plug-and-play manner, and provide the infrastructure and tools to accelerate deep learning in the cloud. 

  • Microsoft’s Computational Network Toolkit helps developers create deep learning models for things like speech and image recognition.

  • Apple’s Core ML allows developers to easily integrate ML models into apps using just a few lines of code.


With AI capabilities at their fingertips, developers can, for the first time, incorporate ML into custom applications in a way that’s not exorbitantly expensive or time-consuming.

Finding the Right Models

As platforms open up their ML engines, and more ML models are freely available, developers will need to focus on finding the right models for their needs. For example, if a developer is building an app to help health providers better manage EHRs via voice recognition; they might want to consider a deep learning model like Deep Boltzmann Machine (DBM). Similarly, if a developer is creating a retail app to recommend or promote purchase pairings based on previous user data (e.g. if someone buys chips, they’re likely to buy beer), they might want to incorporate association rules through something like the Apriori algorithm.

But success doesn’t just depend on choosing the right model. There is a whole host of other things that have to happen to make an intelligent app work – things like training the model with the right training set and testing it to evaluate the performance of the algorithm on a particular function/problem. But the hardest work (actually creating the ML engine itself) is already done. Developers with ML integration expertise (like DVmobile) can help organizations work through the rest of the checklist fairly easily. And as a result, we’re beginning to see the first big wave of third-party intelligence-integrated apps hit the market.

If your organization is ready to ride this wave and experience the power that ML can bring to applications, operations, and customer engagement, let’s connect.


Jamie Murphy

Guest blogger for DVmobile, busy mama, & strategy wiz.


How to Strike up A Conversation – 5 Best Practices for Successful Chatbots

5 Best practices for successful chatbots

Have you heard the term “conversational marketing”?

It’s essentially a euphemism for AI-enabled chatbots (bots). In the near term, these bots are expected to revolutionize how companies interact with existing and potential customers. In fact, Gartner predicts virtual agents (bots) will participate in a majority of commercial interactions between people and businesses in less than three years.

The rise of bots doesn’t really come as a surprise to us. We’ve been implementing conversational apps, complete with natural language processing (NLP) into client solutions since 2010. We’ve developed bots across a wide range of industries - from retail to home security to healthcare – and along the way, we’ve developed the following five best practices for successful chatbots:

Understand the End User

1. Understand the End User – All the bells and whistles don’t matter at all if the bot doesn’t serve the needs of the user. So, understanding the reasons a user might initiate a chat, the experiences that led them to take that action and their resulting attitudes are critical to designing a successful bot. In our process, we use design thinking and empathic design to understand where stakeholders are coming from and what their needs are, so we can build chatbot features and functionality to meet them where they are.

Preset scenarios

2. Preset Scenarios – Regardless of whether or not you’re using AI to inform your bot’s capabilities or not, you need to pre-set your bot with a few scenarios that it is bound to encounter. These may be things like: onboarding, missing inputs or vague or irrelevant requests. The more cases your bot is programmed to handle, the better your bot will perform and the more satisfied your users will be.

Give your bot a personality

3. Have a Personality – Just because it’s a bot doesn’t mean it has to sound like one. By applying deep neural networks to NLP, today’s bots can communicate more “humanly”, and in turn, more effectively. With this in mind, give your bot a bit of personality - just make sure it’s one that aligns well with your brand. For example, for brands with a serious, pithy personality, a bot interaction might begin with a “Hello,” while more fun loving, or light brands might initiate a session with a “Hey there!” or even a “Hi!” Regardless of the personality that your bot adopts, make sure that it’s consistent within every interaction.

be bot-obvious

4. Be Bot-Obvious – No bot is going to sound 100% human 100% of the time. Rather than confusing and frustrating users, we suggest being upfront with them. Tell them directly that they’re interacting with a bot. This builds trust with the user and makes them more understanding if things don’t go right. Here are some ways to let users know that they’re interacting with a bot rather than a live representative:

   • Put the word “bot” in your bot’s name – e.g.

   • Initiate the first conversation with an intro from the bot and a brief description of what tasks the bot can perform

Handoff to a human if needed

5. Offer Human Contact – Even the best bot in the world will be insufficient at some point. A user will make a request that the bot is not programmed to handle or will speak a language that the bot isn’t coded to understand, and human interaction will be required. Make sure that your bot offers a clear way for users to contact a human who can help them when/if the bot fails.

These are just a few of the chatbot best practices we’ve gleaned over the years. To see these best practices in action and more information about how DVmobile creates conversational apps, integrates NLP into existing apps or helps companies develop their bot strategy, contact us!


Danny Sanchez

UI/UX Designer at DVmobile, Men-at-Work fan, and 3D printing guru.


Google Cloud Healthcare API – What Google-Enabled Machine Learning Could Mean for Patient Care

Google Cloud Healthcare API

At the recent HIMSS18 conference, Google unveiled its new cloud healthcare API. This tool will help healthcare providers and healthtech companies collect and manage various types of medical data – including EHR data, administrative reporting data, and imaging data – via the cloud and is purportedly designed to address the significant interoperability challenges in healthcare data. 

  HIMSS Global Conference & Exhibition is the industry's largest health information and technology educational program and exhibition center.

HIMSS Global Conference & Exhibition is the industry's largest health information and technology educational program and exhibition center.


Google Cloud, not unlike other public cloud providers, is finding ways to differentiate in the competitive cloud market. Google is betting that, if healthcare data can be aggregated and easily accessible, companies will be better able to leverage this data to launch new analytics and machine learning projects. The upside is that this capability will open the door to identifying new trends, patterns, and insights that could significantly improve patient care and patient outcomes.

Interoperability is expected to pose a continued challenge throughout 2018 (and beyond) - Google’s healthcare API may help enable a more seamless healthcare data exchange.

“Our goal with the Cloud Healthcare API is to help transform the healthcare industry through the use of cloud technologies and machine learning. Healthcare is increasingly moving to the cloud, and the adoption of machine learning will allow the industry to unlock insights that can lead to significant clinical improvements for patients.”
– Google

What kinds of improvements to patient care is Google referring to? Here are just a few examples of machine learning projects impacting patient care:

  1. Google already has developed a machine learning algorithm to identify cancerous tumors on mammograms.

  2. Stanford researchers have trained a deep learning algorithm to identify skin cancer.

  3. A deep machine-learning algorithm has been proven to help diagnose diabetic retinopathy in retinal images.

Machine Learning is helping diagnose patients.

DVmobile has deep expertise in the healthcare market – developing healthcare solutions for the cloud and partnering with industry leaders to keep pace with healthcare industry standards (including those around data). For more information about how we can help your organization contact us through the button below, or read more on our Healthcare Industry portal.

  Healthcare Industry portal

Healthcare Industry portal


Kris Soden blog author

Kris Soden

SAFe SPC4 certified consultant, proud dad, and former CU Buffs lineman.