Apple Health and Fresh Fitness Food: How many calories did the team at Calcey burn to find a solution for data integration?

In December 2020, Fresh Fitness Food, one of London’s leading providers of bespoke nutrition, took the next step in on-the-go accessibility through the launch of its mobile app. Fresh Fitness Food (FFF) provides precision nutrition tailored to each individual’s needs and dietary restrictions. With the new mobile app, however, FFF has the potential to be so much more. 

The app can track fitness goals, daily activity, and calorie expenditure. It can then suggest and provide guided workouts and meal plans to help you achieve your goal. Real-time data can be entered on the go and accounted for in the design of your next meal. FFF employs a Michelin-star-trained menu consultant to create this bespoke meal plan and will deliver it right to your door. 

Calcey, who developed a new web portal for FFF, was the team behind the design of the mobile app. The app was meant to be easily connectable with other activity-tracking devices and programs such as Fitbits and Apple Health. However, soon after the launch, the team was made aware of a discrepancy. 

Where’s the fire?

The number of calories burnt each day was provided to the user through the FFF app. To do this, the app would integrate data from Apple Health or any other fitness monitoring system the user already had. When comparing the numbers provided by the app against those provided by Apple Health, it was found that they varied by 50-200 calories each day. The team at Calcey was puzzled over this inconsistency and began to search for an explanation. Seek and ye shall find – the team soon discovered the reason behind this.

Apple Health used a system of time intervals to produce the final number of calories burnt at the end of the day. It was a two-part calculation, using Active Energy – the energy expenditure while walking, exercising, etc- and Resting Energy – the energy spent for metabolic processes such as food digestion. Some time intervals however, would straddle two days. E.g. – 11.30 pm to 12.30 am. This was the cause of the discrepancy between the data recorded by the FFF app vs Apple Health. 

Douse the flames

Once this was brought to light, the next step for Calcey was to find a way to extract the correct number of calories belonging to each day. They did this by proportionately allocating calories based on time. Eg – If the time period was 11.30 pm – 12.30 am and the total calories burnt during this time was 100, 50 calories would be allocated to the first day and 50 to the second. Similarly, if it was 11.45 – 12.45 with the same number of calories, 25 calories would be allocated to the first day while 75 would be included in the count for the second day. 

The team wrote and re-programmed the FFF app to include this method of calculation and the change has been implemented for future downloads. Any user who was affected by this discrepancy in the month since the app’s launch was afforded the opportunity to have their calorie number corrected and updated. The team at Calcey does not anticipate any further stumbling blocks with the FFF app; however, should any arise, the team will spend as many calories as required to ensure the best possible service is provided to a client!

Integrating wearables with apps can deliver a range of benefits to users.

At Calcey, we believe that great things can happen at the intersection of business and technology. Be it in bio-tech, education, real estate, or finance, the intelligent use of technology can make things better for everyone.

Take for instance Fresh Fitness Food (FFF), a Calcey customer and a London-based startup that specialises in delivering bespoke healthy meals to suit a given caloric requirement of a customer. Every day, thousands of fitness addicts in and around London, including former England and Wasps rugby player James Haskell, depend on FFF to satisfy their nutritional requirements. 

When you are in the business of delivering healthy meals to fitness junkies, it is really important to ensure that each and every meal suits the needs and dietary preferences of EVERY SINGLE CUSTOMER. To do so, bespoke meal services usually ask customers for information such as height, weight, metabolic rate, allergies and more.

Collecting this data is often a cumbersome process. To make things worse, the human brain is notoriously bad at estimating, as many a scientist has pointed out before.

But, what about the wearables on our wrists? These tiny devices manage to gather a treasure trove of information on us with every passing day, and soon, medical professionals could use the data to even predict illnesses. Why can’t services such as FFF tap into this treasure trove of data instead of relying on inputs from customers?

Integrating wearables with apps can provide a few distinct benefits such as:

Access to real-time data

Wearables can provide apps with continuous access to a stream of real-time data, thus allowing them to deliver an optimal user experience at all times. For a custom meal service like FFF, access to real-time data will allow it to tweak meals to a customer’s shifting health attributes on a daily, weekly, or monthly basis.

Better user experiences through precise data collection

For companies and apps operating in the health-tech or fit-tech space, the data collection process is the starting point of the value chain as well as the customer journey. As such, the accuracy of this data is extremely important, and more precise data makes for a better user experience. When paired with a fitness tracker, apps such as Fitbit Coach and Nike Training Club can deliver a vastly superior experience that very closely reflects the needs of the user (based on their physical attributes) compared to when they are used in isolation and depend on user input alone.

Insurance giant AIA’s Vitality program is a great example. By integrating insurance with fitness trackers, the company can incentivize users to take care of their health and use the data to fine-tune their underwriting practices over time.

All major wearable manufacturers such as Apple, Fitbit and Garmin provide developers with access to APIs that make it a breeze to integrate wearables with web and mobile apps. Given below are the basics of what you need to know if you ever intend to integrate a wearable with an app. For the sake of simplicity, we will only be focusing on integrating the Apple Health, Fitbit, and Garmin platforms with an iOS app.

Integrating Apple Health

Of all the wearable platforms, Apple’s HealthKit platform is probably the most feature-rich and easiest to integrate. Due to its laser-like focus on privacy, Apple requires developers to obtain the explicit consent of users before accessing data. In true Apple style, the company provides developers with a set of guidelines for the purpose, and developers are expected to inform users of exactly how and why their information would be used. Typically, this can be accomplished by making amendments to the app’s Info.plist file.

HealthKit relies heavily on subclassing. At its most basic level, this is how a code snippet would look:

class HKQuantitySample : HKSample

HealthKit has several different data types of which ‘Quantity Samples’ is the most common. This data type grants access to data such as a user’s height and weight, pulse rate, etc. which can then be used by services such as FFF to build a user profile.

Here’s a sample of how the code for a query to find out the basal energy burn would look like:

guard let quantityType = HKObjectType.quantityType(forIdentifier: HKQuantityTypeIdentifier.basalEnergyBurned) else {
  fatalError("*** Unable to create a step count type ***")
}
// Create the query
let query = HKStatisticsCollectionQuery(quantityType: quantityType,quantitySamplePredicate: nil,options: .cumulativeSum,anchorDate: anchorDate,intervalComponents: interval)

Integrating Fitbit

Fitbit is somewhat different from Apple in that it does not allow developers to access historical data. To get around this problem, developers can use Fitbit’s Health API (now known as the Web API) 

The Fitbit API uses OAuth 2 as its authentication protocol. Since integrating a service through the OAuth 2 protocol can be messy, developers can rely on Swift’s OAuth library to complete the integration. This method should serve well in most instances and doesn’t take much time to implement as well.

Once a connection is established, the Fitbit APIs Profile and Activity endpoints (or any other endpoint) can be used to obtain the necessary data.

Here’s an example of a GET request that can be entered to obtain information about activities completed by the user:

GET https://api.fitbit.com/1/user/[user-id]/activities/date/[date].json

Once processed, the API would spit out this response:

{
    "activities":[
        {
            "activityId":51007,
            "activityParentId":90019,
            "calories":230,
            "description":"7mph",
            "distance":2.04,
            "duration":1097053,
            "hasStartTime":true,
            "isFavorite":true,
            "logId":1154701,
            "name":"Treadmill, 0% Incline",
            "startTime":"00:25",
            "steps":3783
        }
    ],
    "goals":{
        "caloriesOut":2826,
        "distance":8.05,
        "floors":150,
        "steps":10000
     },
    "summary":{
        "activityCalories":230,
        "caloriesBMR":1913,
        "caloriesOut":2143,
        "distances":[
            {"activity":"tracker", "distance":1.32},
            {"activity":"loggedActivities", "distance":0},
            {"activity":"total","distance":1.32},
            {"activity":"veryActive", "distance":0.51},
            {"activity":"moderatelyActive", "distance":0.51},
            {"activity":"lightlyActive", "distance":0.51},
            {"activity":"sedentaryActive", "distance":0.51},
            {"activity":"Treadmill, 0% Incline", "distance":3.28}
        ],
        "elevation":48.77,
        "fairlyActiveMinutes":0,
        "floors":16,
        "lightlyActiveMinutes":0,
        "marginalCalories":200,
        "sedentaryMinutes":1166,
        "steps":0,
        "veryActiveMinutes":0
    }
}

Here’s another example of a GET request through which developers can obtain details about the user’s profile:

GET https://api.fitbit.com/1/user/[user-id]/profile.json

And the response:

{
    "user": {
        "aboutMe":<value>,
        "avatar":<value>,
        "avatar150":<value>,
        "avatar640":<value>,
        "city":<value>,
        "clockTimeDisplayFormat":<12hour|24hour>,
        "country":<value>,
        "dateOfBirth":<value>,
        "displayName":<value>,
        "distanceUnit":<value>,
        "encodedId":<value>,
        "foodsLocale":<value>,
        "fullName":<value>,
        "gender":<FEMALE|MALE|NA>,
        "glucoseUnit":<value>,
        "height":<value>,
        "heightUnit":<value>,
        "locale":<value>,
        "memberSince":<value>,
        "offsetFromUTCMillis":<value>,
        "startDayOfWeek":<value>,
        "state":<value>,
        "strideLengthRunning":<value>,
        "strideLengthWalking":<value>,
        "timezone":<value>,
        "waterUnit":<value>,
        "weight":<value>,
        "weightUnit":<value>
    }
}

Integrating Garmin

Unlike Fitbit, Garmin’s Health API uses OAuth 1 as its authentication protocol. Don’t worry though, because Swift’s OAuth library supports both OAuth 1 and OAuth 2 protocols.

To fetch data, developers can use the Garmin API’s Activities and Dailies classes. Here’s a sample code snippet that can be used to obtain a daily summary of the user’s activity.

Here’s the GET request:

GET https://healthapi.garmin.com/wellness- api/rest/dailies?uploadStartTimeInSeconds=1452470400&uploadEndTimeInSeconds=1452556800

And here’s the response:

{ "summaryId": " EXAMPLE_67891", "calendarDate": "2016-01-11", "activityType": "WALKING", "activeKilocalories": 321, "bmrKilocalories": 1731, "consumedCalories": 1121,
"steps": 4210,
"distanceInMeters": 3146.5, "durationInSeconds": 86400, "activeTimeInSeconds": 12240, "startTimeInSeconds": 1452470400, "startTimeOffsetInSeconds": 3600, "moderateIntensityDurationInSeconds": 81870, "vigorousIntensityDurationInSeconds": 4530, "floorsClimbed": 8, "minHeartRateInBeatsPerMinute": 59, "averageHeartRateInBeatsPerMinute": 64, "maxHeartRateInBeatsPerMinute": 112, "timeOffsetHeartRateSamples": {
    "15": 75"30": 75,
     "3180": 76,
     "3195": 65,
     "3210": 65,
     "3225": 73,
     "3240": 74,
     "3255": 74
},
"averageStressLevel": 43, "maxStressLevel": 87, "stressDurationInSeconds": 13620, "restStressDurationInSeconds": 7600, "activityStressDurationInSeconds": 3450, "lowStressDurationInSeconds": 6700, "mediumStressDurationInSeconds": 4350, "highStressDurationInSeconds": 108000, "stressQualifier": "stressful_awake", "stepsGoal": 4500, "netKilocaloriesGoal": 2010, "intensityDurationGoalInSeconds": 1500, "floorsClimbedGoal": 18
}

And that’s all there is to it, really (at least from a developer’s point of view). Happy coding! 

If you are interested in finding out how we can help unleash a new wave of growth for your company with the smart use of technology, contact us via our website.

A handy primer on React Hooks / React Hooks is what happens when you bring a cannon to a knife fight

A little over a year ago, React 16.8 shipped with an additional API that lets developers use state and other features in React without writing a class. Known as ‘Hooks’, this additional API has grown in popularity amongst developers and is now a common feature in everything from open-sourced applications to enterprise apps.

Crucially though, React hooks are completely opt-in, which means there is no need to rewrite existing code. Hooks are also 100% backward compatible and don’t contain any breaking changes.

Why Use React Hooks?

Hooks were developed with the intention of solving a range of seemingly unconnected problems, which were hampering the evolution of React—a language that is not yet a decade old.

Hooks make it possible to:

• Reuse stateful logic between components
  Hooks allow you to reuse logic between components without changing their architecture or structure.
  
• Understand components easily
  When components become larger and carry out many operations, they become increasingly difficult to understand. Hooks solve this problem by allowing developers to separate a given component into various smaller functions which are related to each other.
  
• Navigate classes (without tearing your hair out)
  To the novice, Classes in React can become quite confusing. To complicate matters further, computers also tend to get confused by Functions and Classes. For instance, minifies/uglifies don’t play well with Classes and can cause problems. With Hooks, developers can use more of React’s features without opting for Classes. This makes sense because when you really think about it, React components have always been conceptually similar to functions. In essence, Hooks embrace functions without discarding everything that is great about React.

Before going any further, there are two main ‘rules’ that need to be kept in mind.

• Make sure to not use Hooks inside loops, conditions, or nested functions;
• Only use Hooks from inside React Functions.

The Different Types of Hooks

React 16.8 ships with 10 in-built hooks, but the most basic and commonly used ones are:

useState()

The useState() hook allows developers to update, handle and manipulate state inside functional components without needing to convert it to a class component.

useEffect()

The useEffect() hook accepts a function that would contain effectual code. In functional components, effects like mutations, subscriptions, timers, logging, and other effects are not allowed to be placed inside a functional component. Doing so would lead to a lot of bugs and inconsistencies when the UI is rendered.

When useEffect() hook is deployed, the effectual function passed into it will execute right after the render has been displayed on the screen. By default, effects are executed after the render has been completed, but you can also execute them when certain values change. 

useContext()

The useContext() hook accepts a context object, i.e. a value that is returned from React.createContext, and then returns the current context value as appropriate.

Prior to the introduction of the useContext hook, developers would need to set up a contextType or a to access the global state passed down from a provider in a class component.

useRef()

The useRef hook is a function that returns a mutable ref object whose .current property is initialized with the passed argument (initialValue). The returned object will persist for the full lifetime of the component.

Experienced developers will recognise the useRef hook as something that is used to access DOM nodes or React elements. However, it can also be used to keep any mutable value around similar to how you would use instance fields in classes.

Before vs. After: A Code Example

In order to demonstrate how effective Hooks can be, let’s try to build a simple counter. If we were to use Classes, this is how the code would look:

class Example extends React.Component {
  constructor(props) {
    super(props);
    this.state = {
      count: 0
    };
  }
  render() {
    return (
      <div>
        <p>You clicked {this.state.count} times</p>
        <button onClick={() => this.setState({ count: this.state.count + 1 })}>
          Click me
        </button>
      </div>
    );
  }
}

But, if we were to rewrite this using Hooks, this is how it would look:

 import React, { useState } from 'react';
 
  function Example() {
     const [count, setCount] = useState(0);
 
     return (
      <div>
        <p>You clicked {count} times</p>
        <button onClick={() => setCount(count + 1)}>
         Click me
       </button>
      </div>
    );
  }

Note the following:

• Line 1: The useState Hook from React is imported. It lets us keep the local state in a function component.
• Line 4: Inside the Example component, a new state variable is declared by calling the useState Hook. It returns a pair of values, to which names can be given. We’ve called our variable count because it holds the number of button clicks. We initialize it to zero by passing 0 as the only useState argument. The second returned item is itself a function. It lets us update the count so we’ve named it setCount.
• Line 9: When the user clicks, we call setCount with a new value. React will then render the Example component again, passing the new count value to it.

Bringing It All Together

Here is a little tutorial on how to implement a few basic hooks. In order to illustrate how each hook can be used, we will be attempting to build a small app within React.

import React, { useState, useRef, useEffect } from "react";
export default function Alarm(props) {
 let snooze = 600;
  const intervalRef = useRef();
  const [alarm, setAlarm] = useState(props.alarmInSec);
  useEffect(() => {
    const id = setInterval(function () {
      setAlarm(alarm => alarm - 1);
    }, 1000);
    intervalRef.current = id;
    return () => {
      clearInterval(intervalRef.current);
    }
  }, []);
  return (
    <div>
      <p>{new Date(alarm * 1000).toISOString().substr(11, 8)}</p>
      <button onClick={() => setAlarm(alarm + snooze)}>
        SNOOZE ALARM
         </button>
    </div>
  );
}

Done! You, young padawan, are now on your way to becoming a great React ninja. Congratulations!

A few tips from us on how to scale an app as an MVP gains traction

“Premature optimization is the root of all evil.” – Donald Knuth

Building an app and scaling it to millions of users is tough. As any seasoned developer knows, there is a lot that can go wrong, which is why it is always better to follow an iterative approach starting with an MVP (minimum viable product). The logic is sound: assemble a small team, develop in small increments to gather customer feedback with fewer costs and validate business and technical hypotheses before committing large resources to an idea.

But what happens after the MVP is out? What then?

The reception to the MVP may show that there is a business opportunity to pursue. The logical move at this stage is to put the pedal to the metal and allocate more resources toward the project.

This is when the trouble starts. It is normal for startups to adopt shortcuts in the engineering and architecting process in order to ship an MVP on time while making do with extremely limited budgets. Unbeknownst to them, this approach can often lead to costly setbacks such as those below:

• New development efforts are hampered as new developers get lost in pieces of code that were hastily put together and are now difficult to understand;
• The number of bugs keeps increasing and the development team starts spending more time fixing bugs than on developing new features;
• Resource-intensive processes such as AI modules may have been implemented in sub-optimal ways to reduce development complexity and time, but turn into a headache for developers to fix later on 
• the app starts to slow down as the number of concurrent users increases, sometimes even crashing under moderate loads

It is at this point that founders, CTOs, and developers advocate for a complete rewrite of the app which is seldom realistic. The product has just started to gain momentum, and the pressure on both the top line and the bottom line of the company would be too high.

In the search for a quick remedy, developers typically consider scaling the app using hardware, which is now quite cheap thanks to large volumes of AWS/Azure credits that are doled out by service providers for free in order to drive sign-ups. Though this approach will make scalability issues go away for some time, it won’t actually solve the problem. In effect, it ‘kicks the can down the road’, thus leading the original problem to snowball into something much bigger and harder to fix.

That is why we at Calcey are strong advocates of Scalable MVPs and techniques. 

Most people fail to understand the thinking behind MVPs / Credit: uxdesign.cc

When first building an MVP, it really does not make sense to build an app that can cater to millions of users, when you aren’t even sure if you can sign up ten users. On the other hand, building an extremely limited MVP will almost always result in a costly rewrite, in the event the MVP manages to gain traction. In this context, Scalable MVPs constitute a middle-of-the-road approach, and allow developers to limit the effort that goes into building an MVP while leaving the door open for easy scalability, should the need arise.

Even though an MVP is typically developed in haste, adhering to core design principles (SOLID) and best practices is important. When best practices such as separation of concerns, dependency injection, interface segregation, and the open-close principle are already in place, it is much easier to extend the application beyond the MVP stage.

Scalable MVPs and techniques deliver the following benefits:

Horizontal Scalability

A scalable MVP allows developers to manage server loads cheaply and efficiently through horizontal scaling. Compared to vertical scaling, horizontal scaling is less cumbersome and comes with the inherent benefit of elasticity, enabling development teams to add resources as needed. Developers can also turn to orchestration services such as AWS Elastic Beanstalk to automatically handle capacity provisioning, load balancing, scaling, and application health monitoring in the background, with minimal need for human intervention.

However, there is a caveat. Horizontal scalability is largely dependent on the database the MVP or app is built on. If there is a reasonable chance of user numbers growing rapidly, it is important to be mindful of the database’s need to be able to cater to a large number of parallel requests, a common scenario with multi-tenant applications. Therefore it is worthwhile to consider opting for NoSQL databases such as AWS DynamoDB and Azure Cosmos DB that support horizontal scaling out of the box, thus allowing for throughput to be easily increased based on demand.

From a technical standpoint, the difference in performance between a database with 1000 records or 10 billion records is marginal at best. However, NoSQL databases are not silver bullets. As with everything, they do come with some limitations. Designing your data model with a NoSQL mindset is tough, and requires the presence of developers with prior experience. But, paying attention to such factors early on, will make scaling up much easier later on.

Reduced Re-architecting

As an app moves through its cycle of maturity, re-architecting becomes almost unavoidable. However, re-architecting is a tedious process, but if correct principles have been followed in building the MVP, the degree of re-architecting required will be less because performance testing is easier. Once current performance levels and bottlenecks have been identified, the next steps usually reveal themselves. For instance, an MVP that was originally written in Cordova may have to be rewritten in native iOS and Android in order to accommodate a better user experience and features that draw on functionality native to a given device. Since a scalable MVP follows the same logic as building blocks, it becomes easier to decide which components must be developed, modified, or done away with, while making sure that the core functionality of the app remains unchanged in the eyes of the user.

Unit Testing

Once they gain traction, MVPs tend to expand rapidly, adding lots of features within a very short time. This will make it necessary to refactor the existing code, which can be very dangerous. However, scalable MVPs make it possible to carry out unit tests, which makes it safer to carry out refactoring. Writing unit tests does consume more time and would definitely have a negative impact on the delivery timelines of the MVP. But, if the development team has the experience and is comfortable with writing unit tests, it eventually offers great payback allowing the team to both develop and test faster, without getting bogged down in manual regression testing every time they need to release a new feature.

Launching an app and scaling it to handle requests from millions of users is hard. However, using scalable MVPs and techniques from Day One can make life a tad easier.

For more ideas on cutting-edge technology from the minds at Calcey, follow us on Facebook, Linkedin, and Twitter. 

Here’s how Calcey helped a London-based food-tech startup transform its business with AWS Lambda

When you think about it, the process of ordering food through an app does seem like magic. A few taps here and there, and voila! Your food is already on its way. But unbeknownst to us, there is a lot that goes on behind the scenes. Take the case of Fresh Fitness Food (FFF), a healthy meal delivery service based in London with whom we have had the pleasure of working. Every day, FFF delivers thousands of bespoke meals to its customers across London.

To borrow a metaphor, if the complexity of a regular meal delivery app can be equated to the experience of driving a car on a busy road, the complexity involved in ensuring a bespoke meal delivery service such as FFF runs smoothly is akin to piloting a helicopter. Every minute adjustment from the user’s end can set off a chain reaction in the algorithm that powers the company.

Like most other technologically powered services, FFF too is powered by an algorithm whose main function is to carefully put together a set of bespoke meals to suit a given daily caloric requirement, which can vary from customer to customer. The actual meals are then prepared accordingly and delivered on a pre-set schedule. At first glance, this looks easy, but when you have to tweak thousands of meals a day, it quickly becomes a nightmare.

On a given day, FFF has to balance and create meal templates for anywhere between fifty to hundreds of new orders. While the existing infrastructure was capable of managing this workload, any change to existing recipes or meals invalidated all previous meal/recipe templates due to changes in the nutrients between meals. This created a need to re-balance and create new meal templates for a large number of orders within a short timeframe, and the existing infrastructure could not cope with this increased demand for speed and simultaneous processing power.

When we first spoke to the FFF team, we found that FFF was running its own legacy meal balancing algorithm on a regular server which was also used to host all their backend IT infrastructure. As a result, whenever the resource-intensive algorithm was run, it slowed things down enormously for the entire company. With the algorithm needing 2-3 minutes to process each order, re-balancing FFF’s full order volume would have taken up to 4 days, and left quite a few Londoners hungry and disgruntled in the process.

Our Solution

Once we realised that FFF’s ability to scale and grow as a business significantly depended on the efficiency of the meal balancing algorithm, it became clear that whatever solution we came up with must also be scalable seamlessly. Since FFF was not a large corporation with deep pockets, we also had to be mindful of running costs.

After careful deliberation, the Calcey team zeroed in on using AWS Lambda to host the new meal-balancing algorithm. AWS Lambda is a server-less service provided by AWS and is used to isolate time-consuming computing activities away from regular IT infrastructure. A process running on Lambda is easily scalable, provided the underlying dependencies can support it.

In deploying the retooled meal balancing algorithm for FFF, we had to throttle the performance of the Lambda service, in order to ensure harmony with the capabilities of the existing database. But we also gave FFF the ability to increase the number of concurrent executions by increasing the resources available to the database, should it ever become necessary.

Even with performance throttled, Lambda has been successful in speeding up FFF’s meal-balancing algorithm tenfold. What would have taken four days will now take only eight hours. That’s good enough for FFF’s current scale of operations and near-term growth plans. And the operating cost of this dramatic performance enhancement? USD 15 per month. Not to mention the significant cost savings reaped by shifting meal balancing from the existing servers to Lambda.

Our experience retooling a key business process using AWS Lambda is also a great lesson for developers and executives everywhere. Having understood FFF’s requirements, there was no need to overhaul the entire IT infrastructure, as suited-up consultants may tell you. Instead, with a little bit of ingenuity and pragmatism, it is possible to fashion outstanding IT solutions to business problems without burning a huge hole in a company’s budget.

If your business is also looking to move key business processes to the cloud, talk to us and we will be happy to help!

Cover image credits: thecoach.co.uk