Artificial intelligence
Predictive AI and machine learning
The data your company already holds can suggest decisions. We build models that forecast demand, flag deviations and assess risk.
What we can do for you
Forecasting
Predicting demand, sales and team workload from history.
Anomaly detection
Alerts for unusual transactions, failures or drops in indicators.
Scoring and classification
Assessing risk, sales opportunities or the likelihood a customer leaves.
Recommendations
Product or action suggestions tailored to user behavior.
About this service
AI adoption in a company usually starts with a single process: handling customer inquiries, processing documents, searching company knowledge or forecasting. Depending on the task we build chatbots and assistants, AI agents, process automation, OpenAI API (ChatGPT) integration, generative solutions with RAG, or predictive models.
As a software house we handle the engineering side: connecting the model to your systems, data security (with GDPR in mind), control of model usage costs, and the ability to switch providers without rebuilding the application.
Why Cormo
We start from the data
We assess data quality and volume before we promise a result.
Understandable results
We explain what drives predictions so the team trusts them.
Deployed, not a notebook
The model reaches production with quality monitoring over time.
Technologies
- Python
- scikit-learn
- pandas
- PostgreSQL
- Docker
- FastAPI
Experience you can rely on
For years we have built business systems in PHP, Python and JavaScript. We treat language models and machine learning as one more component of such a system: first we establish which process should benefit and what data that requires.
We work in line with ISO/IEC 27001
Data security is part of our process, not an add-on at the end of a project.
How we work
- 01
Meeting
We learn the business goal, the users and the project constraints.
- 02
Estimate
We settle the scope, a first estimate and a schedule.
- 03
Delivery
We design, build and test in short iterations.
- 04
Support
We deploy, monitor and evolve the solution after launch.
Estimate calculator
Estimate your project in 2 minutes
Answer a few questions and we will prepare a first estimate and send it to your email. No strings attached.
See also
Chatbots and AI assistants
An assistant that answers customer and employee questions from your documents, price lists and procedures, and hands the case to a person when needed.
AI agents
An AI agent does not only answer, it acts: it fetches data, calls APIs, prepares documents and asks for approval where a person should decide.
Process automation
We take over repetitive work with documents, emails and spreadsheets. Your team handles exceptions and decisions instead of retyping data.
Generative AI
We connect language models to your company knowledge. With RAG, answers rely on current documents and not only on what the model learned earlier.
AI consulting
Before you invest in artificial intelligence, we check where it will bring a measurable result, which data is needed and what it will cost.
Data science and analytics
We organize data from many sources and present it in a form you can use daily: reports, dashboards and analyses for management and teams.
OpenAI (ChatGPT) integration
We connect OpenAI models to the apps, CRM and processes in your company. We design it so you can change the model provider without rebuilding the whole system.
AI document processing
Instead of retyping invoices, contracts and orders, the system reads them, pulls out the needed data and hands it to accounting software or an ERP system. A person checks only what is uncertain.
Computer vision
A camera or a phone photo can be a source of data. We build systems that recognize objects, read labels and detect deviations, and pass the results to your software.
AI for e-commerce
A store holds data that can turn into sales: order history, search queries and product descriptions. We add AI features that help customers and save your team time.
AI-powered applications
We help you build a new AI-based product or add AI features to an existing app or SaaS platform, so they are fast, secure and affordable to run.
Frequently asked questions
What can predictive AI do and what models do you build for companies?
We build machine learning models that use historical data to forecast demand, detect deviations and anomalies, and assess risk, for example the likelihood that a customer will leave. We deliver not only the model but also its connection to your systems and a view in which the results make sense to the people making decisions. That way forecasts turn into concrete actions, such as inventory planning or contacting customers at risk of leaving.
How does a predictive model project run and how long does it take?
We begin by sharpening the business question and checking whether the data is sufficient. Then we prepare the data, train several approaches, compare them on data the model has not seen before, and only then deploy the chosen variant. Time depends on the state of the data, since cleaning it is often the longest stage. We work iteratively and show early whether the model makes sense.
What determines the cost of a machine learning model?
The biggest factors are the quality and amount of data and the work needed to prepare it, the complexity of the problem, the required accuracy, how many sources must be integrated, and how the model runs (periodic reports or real-time answers on demand). We start the estimate with a short data assessment. That tells you whether the project has a chance before you commit larger costs.
How accurate are the forecasts, and can a model be wrong or lose quality?
No model predicts the future flawlessly, so we report its accuracy measured on test data and the situations where it fails. Over time reality shifts (different customer behavior, new products) and model quality drops, so we monitor it and retrain when needed. That way you know how far to trust the results and when to double-check them manually. We treat data in line with an ISO/IEC 27001 approach.
What happens to a predictive model after launch and who maintains it?
A model needs oversight: monitoring forecast quality, periodic retraining on new data and adjusting to changes in the systems it draws from. We can take this on as part of maintenance. The code, documentation and description of how data is prepared belong to you, so you can continue with us or with your own team.
Get in touch
Tell us about your idea
Describe what you need. We will reply with a proposal for the next steps.