Machine learning and predictive analytics
We develop models to forecast demand, classify information, identify patterns and detect anomalies. We choose supervised or unsupervised learning techniques based on the problem and the data.
Artificial intelligence · NewInntech
Artificial intelligence built for your business.
We turn data, knowledge and processes into AI solutions. From predictive models to assistants and agents, with integration designed around your operations.
Our capabilities
The right approach for each challenge.
We start with the problem you need to solve. We combine data engineering, machine learning and generative AI based on your goals, available information and business systems.
Generative AI and business knowledge
We build assistants that search your documents and help create or summarize content. With RAG, we connect language models to authorized sources for contextual answers and references you can check.
Agents and intelligent automation
We connect models to tools and APIs to coordinate tasks: finding information, preparing responses or updating systems. We define permissions, boundaries and approval points within each workflow.
Computer vision
We apply deep learning to image and video analysis: object detection, classification and visual inspection. We assess image quality, latency and hardware requirements for each scenario.
Language and document processing
We integrate optical character recognition (OCR) and natural language processing (NLP) to extract fields, classify documents and organize requests. We include validation and exception review in the workflow.
MLOps and model integration
We bring models into your applications through APIs, microservices or batch processing. We version models and data, monitor their behavior and prepare updates for cloud or on-premise environments.
AI in context
From a business need to a practical application.
These examples illustrate different starting points. For each project, we validate feasibility with your data and agree on how to measure results.
Predictive machine learning
Anticipate demand.
Use sales history, inventory and seasonal patterns to estimate demand and support purchasing decisions.
What we evaluateForecast error compared with the current method and the usefulness of predictions for the operations team.
Generative AI + RAG
Make knowledge accessible.
Search manuals, procedures or technical documentation through an assistant that respects access permissions and cites its sources.
What we evaluateSource relevance, faithfulness of answers and the ability to recognize when information is missing.
Agents + integration
Streamline requests.
Classify a request, check its status across systems and prepare the next action, with human approval when the process requires it.
What we evaluateCorrectly completed tasks, handling time and exception management.
How we work
From the first use case to a working solution.
We work in stages with clear acceptance criteria. We validate the value of a pilot before expanding investment and scope.
Define the objective
We identify the process, users and decision to improve. We agree on a baseline, metrics and scope.
Prepare the data
We review quality, permissions and coverage. We organize the data and separate training, validation and test sets when the model requires it.
Validate the pilot
We compare alternatives using representative cases. Together with your business team, we evaluate quality, errors, response time and cost per use.
Integrate and support
We connect the solution, document its operation and define monitoring. We track changes in data and performance to decide when to update.
Engineering that sustains AI
Connected to your technology ecosystem.
We choose models, tools and infrastructure based on workload, privacy and budget. We integrate the solution with your applications and define how to operate, maintain and scale it.
Data, machine learning and vision
Pythonscikit-learnTensorFlowKerasYOLO
Application integration
APIsNode.jsPythonMicroservices
Deployment and infrastructure
AWSMicrosoft AzureGoogle CloudOn-premise
Before we begin
Clear decisions from the start.
Questions that help define the scope of an AI solution.
What kind of AI does my business need?
It depends on the goal. To estimate values or identify patterns, we assess machine learning. For text and knowledge, language models and RAG. To coordinate actions across tools, agents. We can combine these approaches or start with simpler automation if it solves the problem.
How much data do I need to get started?
There is no single threshold. Quality, coverage and relevance to the goal matter. A predictive model needs representative historical data; a document assistant needs reliable, current sources. The initial assessment identifies what is available, what is missing and which pilot is feasible.
Do we need to train a model from scratch?
Not always. We can use pretrained models, connect your information through RAG or evaluate fine-tuning for specific tasks. RAG adds context retrieved from documents; fine-tuning changes model behavior through training. The choice depends on the use case and its evaluation.
How are data and human review handled?
We define what information the solution can use, who can access it and where it is processed. We review permissions, retention and provider terms. For actions that require business oversight, we include validation, traceability and human approval before execution.
Start with a concrete opportunity
What could AI solve for your business?
Tell us which process you want to improve and what information you have. Together, we can assess the approach, feasibility and next step.