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An indicative cost of your first AI Project

15 March 2018

In a previous article, we mentioned that clients and potential clients invariably ask how they can use Artificial Intelligence to add value to their business. The next question inevitably asked is "how much will this cost?". From experience, almost all clients initially have no idea whatsoever.

An AI Project which includes Machine Learning, Natural Language Processing and a Web Interface or API is inherently difficult to price. The reason for this is that Machine learning is applied data science and cannot be considered regular programming. Initially, implementation is more difficult for organizations that already have a non-machine learning infrastructure as compared to a "tabula rasa" start-up.

Specifying requirements is the main problem, as they often change midway through a project as a result of new features, development issues, integration issues, user acceptance and more. Commonly, clients know that there is undoubtedly value to be obtained from their AI project but cannot define requirements and are not sure of the expected results. Given an unclear project definition it is not possible to create a matching project solution architecture.

Expected results are also another major difficulty. Critical to any Machine Learning project, clear expectations in regards to accuracy, precision, recall and scores must be set at the very start. It is important to clarify what can be done, what cannot be done, what the expected results are and what the value provided will be for a client’s business .

As an example, we will price a project where requirements involve building a specialized crawler that retrieves ‘about us’ texts from corporate web sites and feeds them to an AI Module that classifies them by conceptual similarity. Next in a similar manner, all current customer ‘about us’ pages are crawled and classified. The goal is to compare the first with the second set with a view to identifying those new companies from the first set that are good potential fits as new clients (i.e. AI based lead generation).


Let’s begin with the following assumptions:

o the client does not have an ML server

o the texts to be processed do not contain sensitive information

o the texts can be securely transferred offsite on to the cloud

The process will be as follows:

o Compare a number of word embedding methods and classification models

o Deploy the most accurate model as a web service

The Project Plan

Now let’s make a plan.

We consider three toolset options and proceed considering which one best matches a client’s current technology infrastructure.

Option 1

The project will live on Windows Azure.

We will use:

o The Azure ML Workbench

o An Azure Data Science Virtual Machine with GPU

o Word2vec word embeddings, Doc2vec

o TensorFlow, CNTK and Keras

Option 2

The project will live on Amazon Web Services (AWS)

We will use:

o 2 AWS instances (one for web and one as the ML server)

o TensorFlow, Keras, Elasticsearch, Flask

o AngularJS GUI

o Jupyter notebook

Option 3

The project will live on Fortuitapps servers

We will use:

o The Fortuit AI Engine


10 hours for instance provisioning and certificate installs

60 hours qualifying, evaluating, implementing, testing and selecting an optimal ML Model

30 hours User Interface (GUI) Development

10 hours of support, training and follow up

Total 110 Hours

So, for a total of 110 hours at a rate of £90 per hour the total cost would be £9900.


At Fortuitapps our aim is always to provide pricing at an appropriate level that will neither create a net loss or conversely producing an unfairly large profit margin. Our moto: “what we do works”.

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