How do we work
Development methodology of AI projects
is a mixture of agile software building approach
with meticulousness of scientific research.
Development methodology of AI projects
is a mixture of agile software building approach
with meticulousness of scientific research.
Successful deployment of AI-enabled technology requires work in fields of R&D and software engineering,
with expertise in machine learning and a deep understanding of a client's business.
The main difference from other software projects is the presence of a modelling step - an iterative
process of Machine Learning model creation that consists of data analysis, feature engineering, testing
and results analysis.
Final effect depends on input data quality, technology itself (model architecture) and precision in
defining a business problem.
AI project development cycle is the process of discovery - if and how a certain business problem could be tackled with the use of machine learning models. It requires both a lot of intuition and experience at the beginning and a scientific meticulousness and scrutiny in the process.
01
identification of the project's business objectives
indication of recommended technical paths for project development
estimating costs and client engagement in the next phase of work
creating and discussing a report on the progress of the project
With a coherent vision of our common goals, we start cooperation.
02
verification of the technical feasibility and market testing of the project
preparation of selected elements of the project for testing and demonstration purposes
identification of project risks
preparation of a project map and pricing
review of the feasibility report
We verified the project assumptions theoretically and experimentally. At this stage, the pricing, scope of work and timescale are more precise.
03
software development in 1-2 weekly cycles
meetings aimed at receiving feedback and directing changes
concise information on progress and obstacles in work
realisation of the main functionality of the project
testing in an environment similar to the production environment
04
selection of the final hardware or cloud carrier
ensuring: scalability, reliability and security of AI project
integration with external systems
performing approval tests
At the end of this stage you get a complete product that successfully runs in a production environment.
05
monitoring and fixing of identified errors
counteracting bias and data drift
adapting the AI model to changes in systems
We will not leave you alone. You can count on our support both in project development and in fixing potential problems.
02
verification of the technical feasibility and market testing of the project
preparation of selected elements of the project for testing and demonstration purposes
identification of project risks
preparation of a project map and pricing
review of the feasibility report
We verified the project assumptions theoretically and experimentally. At this stage, the pricing, scope of work and timescale are more precise.
04
selection of the final hardware or cloud carrier
ensuring: scalability, reliability and security of AI project
integration with external systems
performing approval tests
At the end of this stage you get a complete product that successfully runs in a production environment.
05
monitoring and fixing of identified errors
counteracting bias and data drift
adapting the AI model to changes in systems
We will not leave you alone. You can count on our support both in project development and in fixing potential problems.
Being a remote-first company we know, how critical is information flow. Here's what we use:
Mattermost
A self-hosted company chat that ensures privacy.
Owncloud
To share the data with clients and between the team. It's encrypted and located on our private servers in EU.
Gitlab
We use it in two ways - as a code versioning system and for tracking project level issues and tasks.
Languages
Our development team is using polish and english. Our consultants also speak german, french and arabic.
Numlabs proved to us that thanks to their knowledge and experience, they can create solutions able to process a vast amount of data. The results of their work helped us strengthen our position on the market and impressed our customers.
— Andrzej Kwiecień, CEO
Apriside ApS
Numlabs have joined our project at a relatively early stage and have proven to be a professional and trustworthy partner until the end. Despite the challenging nature of the task, they managed to communicate each challenge and collaboratively work on solutions with the wider team. We will certainly work together again.
— Maciej Zasada, Technical Director
UNIT9 LTD
Working with specialists from Numlabs was a pleasure. The most important thing was a creative approach to both practical and scientific parts of the problem. The effect is outstanding, our support and resistance zones are one of its kind in the world and provides an edge to our users.
— Marcin Tuszkiewicz, CEO
Inwestio Sp. z o.o.
