Data
Transaction history, images, events and metrics. Quality, completeness and relevance to the real process matter.
Machine Learning · Neural network development
Your data.
New possibilities.
We develop models for business and industry, from data preparation and training to integration into everyday workflows.
From data to a working solution
Start with your challengeMetric · Data · Deployment
Three foundations of a solutionThe task comes first.
The task comes first.
Then the model.
We start with the outcome your business needs, study data, identify features and select an algorithm. A neural network becomes part of the solution once its quality has been validated for your task.
Features
Transform source data into information a model can use. Agree preparation and labelling.
Algorithm
Compare approaches and validate results. Select architecture and computing resources for the task.
From question to applicationWhat can be improved
What can be improved
with your data.
We select methods for your process, from customer engagement to production and urban infrastructure.
Business efficiency
Analyse employee, sales and management process metrics.
Goal: identify factors influencing the outcome.Customer segmentation
Identify groups with similar behaviour and needs.
Goal: choose a more relevant approach for each audience.Personalisation
Select content and offers using interests and interaction history.
Goal: make the customer experience more relevant.Urban infrastructure
Route, passenger flow and road condition data.
Goal: support efficiency and safety decisions.Production processes
Analyse equipment parameters and process operations.
Goal: reveal patterns and optimisation opportunities.Lead-time forecasting
Forecast delivery and production times from historical data.
Goal: plan capacity and commitments more accurately.
Your starting pointStart from
Start from
where you are now.
Commission the full cycle or an individual stage. Choose the situation closest to your project.
Find out what a model can learn.
Review data sources, quality and completeness. Connect available history to the business goal and define pilot criteria.
- Sources, formats and data access
- Missing values, labels and representativeness
- Quality metric and baseline
Stage output: an experiment plan and data requirements.
Build the training foundation first.
Combine required sources, agree preparation and labelling, and identify useful features.
- Collect data and agree its structure
- Clean data and prepare features
- Separate training and validation datasets
Stage output: a prepared dataset and reproducible processing pipeline.
Turn the idea into a testable task.
Review the process to improve. Determine whether conventional automation is enough or machine learning is needed.
- Goal and business cost of errors
- Which data can be collected
- Validation scenario and deployment decision criteria
Stage output: a defined problem and justified next step.
Development and deploymentFrom the first hypothesis
From the first hypothesis
to use in your product.
Define quality criteria before training. Test the model on data it has not seen during training.
-
01
Problem definition
Define the business goal, constraints and measurable success criteria.
-
02
Data preparation
Collect, analyse and clean data. Prepare features and datasets.
-
03
Training and validation
Compare alternatives, assess errors and validate model quality.
-
04
Integration
Integrate the solution and agree post-launch quality monitoring.
Each stage has a clear output and a decision on the next step. Metrics and acceptance conditions are agreed for the project.
Test the hypothesis.
A basis for decisions.
Test on an agreed dataset, compare with a baseline and discuss errors that matter most to your process.
- Quality criteria and constraints
- Validation report and error examples
- Decision on further development or deployment
A model within
your infrastructure.
Agree interfaces, workload and deployment method. Connect model outputs with actions in your applications and systems.
- Integration with applications and accounting systems
- Computing resource selection
- Quality monitoring on new data
Technology for the taskFamiliar tools.
Familiar tools.
A solution built for you.
We use libraries for machine learning, data preparation, image processing and result analysis.
01 /TensorFlowMachine Learning
02 /KerasModel development
03 /scikit-learnAlgorithms and metrics
04 /OpenCVComputer Vision
05 /pandasTabular data
06 /NumPyNumerical computing
07 /SciPyScientific computing
08 /MatplotlibResult visualisation
Team experienceComplex data.
Complex data.
Real projects.
Machine learning, computer vision and data preparation from the MST portfolio.
Machine Learning · Biometrics
Fingerprint comparison
Image preparation, architecture experiments and comparison model validation.
Read the project
Computer Vision · Industry
QR codes in streaming video
Recognition system improvements for difficult production line conditions.
Read the project
Data Preparation · Geoanalytics
Remote sensing data
Creating datasets for subsequent analysis of the Earth's surface.
Read the project
Questions answeredDiscuss the essentials.
Discuss the essentials.
Before launch.
Does the task necessarily need a neural network?+
No. We first assess the task and available data. Depending on the goal, we compare machine learning with other algorithms and conventional automation.
Can we start without a prepared dataset?+
Yes. We begin by defining the problem and assessing sources, then agree collection, preparation and labelling needed to test the hypothesis.
How is solution quality assessed?+
Metrics depend on the task and error costs. Criteria are agreed before the pilot; validation uses a separate dataset and error analysis.
Can we commission just one stage?+
Yes. We can handle data collection and preparation, model development and training, or integration of an existing solution. Scope is agreed at the start.
How are costs and timelines estimated?+
After reviewing the task, we assess data, research and integration scope. An NDA can be signed if needed. Scope, timing and responsibilities are recorded in the contract.
Start with your challengeWhat challenge
What challenge
can we solve with your data?
Describe the process and intended outcome. We will review the source data and propose the next step: research, a pilot or integration.
- Business goal and current approach
- Available data and its format
- Constraints and quality criteria
Solution scope, timelines and terms are agreed for your project.
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