Data & AI
Machine Learning Development Services
Turn the data your business already collects into models that predict, recommend, detect and automate, built and run by a founder-led team with a senior engineer on every project.
Talk to the foundersWhat you get with Authect's machine learning development
Machine learning is at its best on questions your data can answer better than a rule of thumb: which customers are about to leave, how much stock to order, which transaction looks wrong. We help you find those questions, then build models that answer them reliably, inside the tools your team already uses.
You work directly with the founders and a senior engineer who handle the whole path: data preparation, modelling, evaluation, deployment and monitoring. We explain how each model reaches its results in terms your team can act on, and we measure it against the way you work today, so you know whether it's really helping.
Models, training code and data pipelines live in your own accounts from day one, documented so your team can retrain and extend them without us.
What we do
Our machine learning development services
From a single forecasting model to a full set of ML features inside your product, we cover the techniques most business problems need.
Custom ML model development
Classification, regression, forecasting and ranking models trained on your data, chosen for accuracy and for how easily your team can understand and maintain them.
Conversational AI and chatbots
Intelligent automation
Natural language processing
Deep learning and neural networks
Computer vision
Customer feedback
Working with Authect transformed our digital infrastructure. Their engineering team delivered our complex trading algorithm platform two weeks ahead of schedule without sacrificing code quality or security compliance. The seamless communication and deep technical expertise made them a true strategic partner.
Dmitry SokolovHead of Product, FinTech SolutionsSolve a hard business problem with a model built on your own data. Talk to the founders.
Contact usService benefits
How machine learning development benefits your business
- 01
Decisions backed by evidence
Models turn historical data into forecasts, scores and recommendations, and we show which factors drive each result, so managers can weigh it alongside their own experience.
- 02
Streamlined processes and operations
Repetitive sorting, matching and checking work moves to models that run in seconds, leaving your team to deal with the cases that genuinely need a person.
- 03
Personalisation customers notice
Recommendations, search results and offers tuned to each customer's behaviour, so people find what they want sooner and come back more often.
- 04
Risks spotted earlier
Predictive models give risk, finance and operations teams an early warning on churn, defaults, delays and failures, while there's still time to act.
- 05
Stronger data and physical security
Anomaly detection on logins, payments and network traffic, and computer vision that flags unusual activity on site, routed to the people responsible.
- 06
Models that stay accurate
Monitoring for data drift and falling accuracy, with retraining pipelines in place, so a model that works on launch day keeps working a year later.
Industries
Industry-specific machine learning use cases
Retail
Customer segmentation and behaviour analysis, demand and sales forecasting, dynamic pricing, recommender systems, attribution modelling for marketing spend, automated inventory management and footfall analysis with computer vision.
Fintech
Fraud and anomaly detection, real-time credit scoring, churn prediction, credit risk assessment, portfolio analytics and automation of rules-based transaction tasks, with explanations that support regulatory reviews.
Insurance
Risk models for underwriting and pricing, claims triage, fraud signals on suspicious claims, customer lifetime value and retention models, and document extraction for faster processing.
Logistics
Multi-stop pickup and delivery planning, dynamic route optimisation, fleet analytics, demand prediction and capacity planning, arrival time estimates and warehouse slotting.
Agriculture
Yield prediction from weather, soil and satellite data, crop and livestock monitoring with computer vision, breeding value prediction and models that help plan irrigation and inputs.
Healthcare
Patient risk scores and readmission prediction, scheduling and capacity forecasting, support for reading medical images and signals, always with clinicians reviewing the results.
Automotive
Predictive maintenance from vehicle and sensor data, quality inspection on production lines, demand forecasting for parts and fleet utilisation models for rental and sharing services.
How we work
How we build machine learning models that last
A model is only useful once it's running inside your business and trusted by the people using it. Our process is built around getting there, not around a demo.
Case studies
Our machine learning work
RoboticsAgTechComputer VisionShinkei SystemsShinkei Systems: AI Computer Vision & Robotic Automation for Aquaculture< 1sProcess Speed4xShelf-Life100%Humane
AI RoutingLogisticsPredictive AnalyticsSHPD & Logistics PartnerBlueBox: AI Dispatch, Predictive ETAs & Route Intelligence for LATAM & Europe95%ETA Accuracy< 50msDispatch Latency60%Less Support Overhead
Technologies
Tools and frameworks we build ML with
Proven, open tools your team can hire for and maintain, running in the cloud of your choice.
Languages and libraries
Python with pandas, NumPy and Polars for data work, and scikit-learn, XGBoost and LightGBM for the classic models that solve most business problems.
Deep learning
PyTorch and TensorFlow for neural networks, and pretrained models from the open-source community that we fine-tune on your data.
Language and vision models
Large language models from OpenAI, Anthropic, Google and open-source families for text tasks, and vision models for detection, segmentation and document reading.
Data pipelines
SQL, dbt and orchestration tools such as Airflow or Dagster, feeding clean, versioned data from your warehouse or lake into training and prediction.
Experiment tracking and MLOps
MLflow or Weights & Biases for tracking experiments, model registries for versioning, and CI pipelines that test and deploy models like any other code.
Cloud and serving
AWS, Microsoft Azure and Google Cloud, with models served through containers, serverless endpoints or on edge devices, depending on latency and cost needs.
FAQs
Machine learning FAQs
What is machine learning development?
Machine learning development is building models that learn from your data to predict, recommend, detect and automate. Examples include forecasting demand, spotting customers who are about to leave, flagging suspicious transactions and reading documents or images. We handle the whole path, from data preparation and modelling to evaluation, deployment and monitoring, so the model runs inside the tools your team already uses.
How do we know if our problem suits machine learning?
It suits machine learning if your data can answer the question better than a rule of thumb. In a problem and data review with the founders, we define the decision the model will support, how it will be measured and what data exists, and check quality and gaps before promising anything. We then test models against a simple baseline, so you know whether the extra complexity pays off.
How long does it take to build a machine learning model?
It depends on the problem and the state of your data. We start with a simple baseline, test more advanced models against it, evaluate the results with your team and then deploy and monitor. The baseline tells you early whether the idea is worth pursuing; getting to production takes longer, depending on the integrations and the thresholds and fallbacks you need.
How much does machine learning development cost?
It depends on scope: the problem, how much data preparation is needed, the integrations and how the model is served and monitored. We quote each project individually after a free scoping conversation with the founders, once we've looked at the data you have. For a rough first idea of the development side, try the software development cost calculator on our website.
How do you keep models accurate after launch?
We monitor accuracy and data drift in production and retrain on fresh data on a schedule, or sooner when the world your model learned from changes. Every model is versioned, with access controls and the logging needed to trace each prediction, and models are tested and deployed through CI pipelines like any other code, so accuracy doesn't quietly slip after launch.
Do we own the models and data pipelines?
Yes. Models, training code and data pipelines live in your own accounts from day one, documented so your team can retrain and extend them without us. We build with proven, open tools your team can hire for, such as Python, scikit-learn, PyTorch and MLflow, on AWS, Microsoft Azure or Google Cloud.