Data & AI
Data Science Services
Turn the data your business already collects into forecasts, recommendations and decisions you can act on, built by a founder-led team and led by a senior engineer from the first question to production.
Talk to the foundersWhy choose Authect as your data science partner
Most data science projects don't fail because of the maths. They fail because the model never leaves a notebook, answers a question nobody asked, or can't be trusted by the people who have to use it. We start from the decision you want to improve, then work back to the data and the model that support it.
Our work combines statistics and machine learning with solid software engineering, so a promising model becomes a reliable service inside your products and workflows. Every project is led by a senior engineer, the founders stay involved from the first workshop, and the code, models and data pipelines are yours from day one.
Client 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 SolutionsFind out what your data can tell you, from the founders who will build the models.
Contact usWhat we deliver
Data science services we deliver
Data science sits at the centre of a family of services. We combine them as your project needs, with one senior engineer responsible for the whole picture.
AI development
We add artificial intelligence to the software you already run: assistants that answer questions from your own documents, features that classify, summarise or extract information, and tools that take repetitive work off your team. Each feature is designed around a real workflow, tested against real examples and reviewed by an engineer before it ships.
Machine learning development
Custom models for forecasting, scoring, classification, recommendations and anomaly detection, trained on your data and measured against the outcomes you care about. We explain what a model can and can't do in plain language, so the people relying on it know when to trust it.
MLOps
A model is only useful while it stays accurate. We put the pipelines in place to retrain, test, deploy and monitor your models automatically, with alerts when data or performance drifts, so they keep working long after launch without a data scientist watching them.
Intelligent automation
We combine models with workflow automation to handle the manual, rules-heavy work in your organisation: routing requests, reading documents, checking data and preparing reports, with a person stepping in only where judgement is needed.
What you get with Authect's data science services
We take your questions and your data and turn them into analytical models that show where the gaps and opportunities are, then build those models into the tools your people use every day.
Along the way, you get a clear picture of how your data flows, which predictions are reliable enough to act on, and what it would take to go further.
Every engagement includes:
- An analytics and modelling module built around your business questions
- Prediction and optimisation models packaged as services, where they add value
- Full source code, documentation and the reasoning behind each model
- Consulting on data flows, plus reports, presentations and demo sessions
- Quality targets agreed with you before modelling starts
- Deployment of the models into your production environment
Service benefits
How data science helps you reach your business goals
- 01
Grow sales with your own data
Personalised recommendations, smarter pricing and churn prediction help you keep customers longer, raise basket size and focus your sales effort where it's most likely to convert.
- 02
Run the business more efficiently
Forecasts for demand, staffing and stock let you plan resources, budgets and marketing with evidence rather than guesswork, and predictive maintenance catches problems before they become costly.
- 03
Manage risk with confidence
Models that flag unusual transactions, score risk and anticipate demand give your team an early warning, so they can act before a small issue becomes a loss.
- 04
Improve day-to-day operations
From call centre workloads to warehouse stock levels, analytics shows where time and money are being lost, and optimisation models suggest how to rebalance them.
- 05
Deliver a smarter customer experience
Natural language processing, computer vision and deep learning power assistants, search and self-service that understand what customers mean, at every touchpoint.
How we work
Our data science delivery process
Seven steps take a business question all the way to a model running in production. A founder and a senior engineer stay with you throughout, so you always know what the data is telling us and what comes next.
Ready to turn your data into insight that makes the business run better?
Contact usWhat we handle
What we'll take care of
Roadmap and hypotheses
An analysis and modelling roadmap, plus initial hypotheses built from your requirements and a first look at the data, so we test the ideas most likely to pay off.
Data access and consulting
We arrange access to the data sources and assets the work needs, and advise on how your data is used, stored and transformed along the way.
Descriptive analysis
Modules that describe what has happened and why, giving your team a trusted, shared view of the numbers before anyone starts predicting.
Predictive and optimisation modules
Models that forecast what is likely to happen, factor analysis that shows what drives it, and optimisation that recommends what to do about it.
Validation and interpretation
We gather the insights, check each model's performance and explain the results in plain language, with technical write-ups and visual reports for different audiences.
Ready-to-use deployment
Models packaged and deployed so they work inside your existing systems from day one, not as a separate tool your team has to remember to open.
Industries
Industry-specific use cases for data science
Healthcare
Deep learning that supports medical image review, predictive models that flag patients who may need attention sooner, virtual assistants for patient questions and personalised care and nutrition plans, all built with strict protection for health data.
Retail
Recommendation engines, demand forecasting, dynamic pricing and promotions, visual search and computer vision for shelf checks and fraud prevention, plus assistants that help shoppers find what they need.
Media & Entertainment
Content recommendations and personalisation, audience analytics, targeted campaigns, generative tools that help with content production and models that support moderation and copyright protection.
Fintech
Fraud detection, credit and risk scoring, personalised financial planning, portfolio optimisation and the automated reporting that keeps you on the right side of regulators.
Insurance
Models that support underwriting, claims triage and fraud detection, risk analysis for loss prevention and language models that help agents and customers through policy questions.
Logistics
Route planning and fleet optimisation, predictive maintenance for vehicles and equipment, delivery time estimates and demand forecasts that keep warehouses stocked without overstocking.
Energy
Forecasting consumption and generation, predictive maintenance for plant and grid assets, anomaly detection on sensor data and optimisation models that balance cost and capacity.
Methods
Methods and techniques we apply
We pick the simplest method that answers your question well. Sometimes that's a clear chart of historical data; sometimes it's a neural network. You'll always know why we chose it.
Descriptive analytics
Making sense of historical data to show what happened, where and why, so reporting and decisions rest on facts everyone agrees on.
Predictive analytics
Forecasting demand, churn, risk or lead quality, so you can put effort and budget where they're most likely to pay off.
Prescriptive analytics
Weighing likely outcomes to recommend the best next action, from stock orders to pricing, rather than leaving the final step to instinct.
Machine learning algorithms
Supervised learning for classification and regression, unsupervised learning for segmentation and anomaly detection, chosen to fit the data you have.
Neural networks and deep learning
Convolutional and recurrent networks and transformers for language, images, audio and sequences, when simpler models aren't enough.
Process optimisation
Combining models with automation to streamline repeatable processes, so your people spend their time on the cases that need a human.
Technologies
Technologies we work with
We choose tools for your problem and your team, favouring widely used, well-supported technology your own engineers can pick up after us.
Languages
Python for most modelling and data work, SQL for anything close to the warehouse, and TypeScript or other languages when a model needs to live inside an application.
Machine learning frameworks
scikit-learn, XGBoost and LightGBM for structured data; PyTorch and TensorFlow for deep learning; Hugging Face libraries for language and vision models.
Data processing
pandas and Polars for analysis, Apache Spark for large datasets, and dbt for transformations that need to be tested and documented.
Data platforms and warehouses
Snowflake, Databricks, BigQuery, Amazon Redshift and PostgreSQL, depending on where your data already lives and what it costs to run.
Experiment tracking and MLOps
MLflow and Weights & Biases for experiments and model registries, Airflow and similar orchestrators for pipelines, and Docker and Kubernetes for deployment.
Cloud AI services
The machine learning services on AWS, Microsoft Azure and Google Cloud, used where they save time without locking you in.
Visualisation and reporting
Power BI, Tableau, Looker and lightweight notebooks or web dashboards, so results reach the people who need them in a form they'll actually use.
Insights
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FAQs
Data science services FAQs
What are data science services?
Data science services help you get value from your data using statistics, machine learning and software engineering. In practice that means exploring your data, building models that predict, classify or recommend, and putting those models into the systems your people and customers use, so they influence real decisions.
What is the difference between data science and data analytics?
Data analytics mostly looks backwards: it describes what happened and why, usually through reports and dashboards. Data science goes further, using models to predict what is likely to happen and to recommend what to do. Most projects need both, and we often start with analytics to make sure everyone trusts the numbers before we build models on top of them.
What are the stages of a data science project?
Our projects move through seven stages: defining the problem, collecting and exploring the data, preparing it, building and training models, evaluating and testing them, deploying the best one into production, and monitoring and maintaining it. We agree the success measure at the start, so every stage is judged against the same goal.
How much data do we need to start?
Less than many people expect, but it has to be relevant. A solid history of clean transaction or operational records is often enough for forecasting, and modern language and vision models can work with far fewer examples. In the first weeks we assess what you have and tell you honestly whether it supports the goal.
Who owns the models and the code?
You do, from day one. The source code, trained models, data pipelines and documentation live in repositories and cloud accounts in your name, so your own team or any future partner can run and improve them without depending on us.
How do you make sure a model can be trusted?
We test every model on data it hasn't seen, compare it against a simple baseline, and check how it behaves for different groups and edge cases. We explain the results in plain language, and once it's live we monitor accuracy and alert you when the data starts to drift.