What's the difference between what you do and BI or Power BI?

Power BI and similar tools are descriptive: they show what happened. What we build is predictive and prescriptive — models that forecast what will happen and agents that act based on it. The practical difference: a BI dashboard answers "how many sales did we have?"; our AI answers "which customers will cancel in the next 30 days and what to do to retain each one".

Do I need lots of data to start an AI project?

It depends on what you need. For LLM fine-tuning (text agents, document analysis): works with hundreds of examples. For demand or churn prediction models: usually 6-12 months of history needed. For classification with transfer learning: works with limited data. The initial diagnosis defines what is possible with your current volume — and what needs to be collected.

How do you ensure the model works in my real business?

We validate with metrics that matter for your process, not generic accuracy. We test with real production data, not just lab holdout sets. We configure drift monitoring from the first deploy — when the model starts losing precision, you know before you feel it. And retraining is documented so your team can execute it without depending on us.

What are AI agents and how do they differ from an ML model?

An ML model makes a prediction (e.g., will this customer churn?). An AI agent acts: analyzes the contract, identifies clauses, generates the summary, categorizes it and sends it to the approver — all without human intervention. Agents combine LLMs + tools + process logic. They are the layer that turns predictions into real work automation.

Do you do fine-tuning of GPT, Claude, and other LLMs?

Yes. Depending on the case, we use: fine-tuning (for specific domain with many examples), RAG — Retrieval-Augmented Generation (for querying internal documents), advanced prompt engineering (for configuring agents in your context), or open-source models (LLaMA, Mistral) running on your infrastructure for sensitive data. The choice depends on cost, data privacy, and required performance.

Powertrend

Data Science, Machine Learning and AI Services

Sometimes you need an exquisite solution for the expansion and improvement of your business. We offer data science, machine learning and AI services to unlock hidden market potential and make your product more innovative, sustainable, and customer-focused in every aspect.

The transformative power of AI development is limited only by imagination — together, we can develop intelligent solutions to optimize your business. We're ready to create AI that evolves with your vision.

Schedule free diagnosis Chat on WhatsApp

Data Science, Machine Learning and AI Services

What Is AI Applied to Business?

AI Applied to Business means building models and agents that operate in your processes with autonomy — not generic BI tools or ML platforms you pay a monthly fee to use. We train models on your historical data (Python, scikit-learn, PyTorch, LangChain), fine-tune LLMs for your specific context, and build agents that execute real work: document triage, contract analysis, report generation, demand forecasting. The model, pipeline, and code stay with you — no platform lock-in.

Automate Repetitive Decisions

When your team makes the same decisions manually hundreds of times per week — triage, classification, approval, report generation. AI agents take over this work with more speed and less human error.

Predict Behaviors Before They Happen

When you need to know which customers will cancel, which product will run out, which order will be late — before feeling the impact. ML models trained on your history.

Extract Value from Unstructured Data

When your most valuable data is in emails, contracts, reports, PDFs, and forms. LLMs trained in your context automatically extract and structure this information.

Replace Generic BI with Specific AI

When generic dashboards show what everyone already knows. Models trained on your data reveal competitive patterns specific to your business.

Models trained on your historical data, not generic benchmarks
Agents operating processes autonomously — no manual supervision
LLM fine-tuning (GPT-4o, Claude) in your specific context
Documented and reproducible ML pipeline
Drift monitoring in production from the first deploy
Code 100% yours — zero ML platform lock-in
Dashboards built to answer business questions
Demand, churn, and anomaly forecasting with real data
Data engineering: structuring, cleaning, and ML preparation

Our AI Software Development Services

We offer a full range of AI software development services to transform your business. Our specialized team develops intelligent solutions using machine learning, deep learning, natural language processing, and computer vision to create innovative and efficient products.

Deep Learning and Machine Learning

We have experience building neural networks, computer vision systems, natural language processing models, and recommendation systems.

Data Engineering

We have data engineers and data scientists on our team to help acquire, clean, label, and preprocess your data to optimize it for machine learning.

Custom AI Development

Our engineers can provide AI development services tailored to your needs. We develop AI systems that can analyze data, discover patterns, make predictions, and optimize complex processes.

Chatbots and Virtual Assistants

We create conversational AI solutions such as chatbots and virtual assistants that can engage in complex dialogues on platforms like Facebook Messenger, Slack, and custom web/mobile interfaces.

Computer Vision

We use advanced computer vision techniques such as object detection, image classification, and facial recognition to build AI systems capable of analyzing and understanding visual data.

AI Integration

We help companies integrate AI technologies into their existing systems and workflows. Our AI specialists help determine use cases and select appropriate algorithms and datasets to integrate AI according to your needs.

Generative AI Consulting

For companies interested in using generative AI for creative tasks such as art, music, writing, etc., we offer consulting on the latest generative AI methods.

AI Team Augmentation

We provide highly skilled AI engineers and data scientists to work as temporary or contract employees at your company.

Prompt Engineering

We help companies using AI for natural language generation craft high-quality prompts that produce coherent, consistent, and controllable responses.

Our Process

Our AI development process is structured, collaborative, and results-focused. We work together with the client from understanding the problem to production monitoring, ensuring that each delivered solution truly solves the proposed challenge and generates positive impact.

Discovery

Our AI development journey begins with in-depth consultations to understand your business complexities. We identify opportunities where AI solution development can generate transformative results aligned with your goals.

Data Exploration

The foundation of AI lies in data. Our experts analyze and collect relevant data, ensuring a solid foundation for custom AI model development.

Model Development

Our experienced artificial intelligence developers create and optimize models tailored to your specific needs.

Integration

Seamless integration with existing systems is followed by rigorous testing. Our QA team ensures impeccable functionality, accuracy, and performance for a flawless user experience.

Deployment

Launch day marks the deployment of your custom AI solutions. Continuous monitoring ensures maximum performance, adapting to real-world scenarios for sustained success.

Support and Maintenance

Our commitment goes beyond deployment. We offer ongoing support and maintenance, promptly addressing any issues and implementing updates to keep your AI software at its peak.

AI stack we use

Frameworks + language models: OpenAI GPT-4o, Claude (Anthropic), Hugging Face, LangChain

What changes when AI operates on your data

Time Savings and Efficiency

Performance Monitoring and Optimization

Intelligent Resource Planning

Code Quality Improvement

Frequently Asked Questions

What's the difference between what you do and BI or Power BI?: Power BI and similar tools are descriptive: they show what happened. What we build is predictive and prescriptive — models that forecast what will happen and agents that act based on it. The practical difference: a BI dashboard answers "how many sales did we have?"; our AI answers "which customers will cancel in the next 30 days and what to do to retain each one".
Do I need lots of data to start an AI project?: It depends on what you need. For LLM fine-tuning (text agents, document analysis): works with hundreds of examples. For demand or churn prediction models: usually 6-12 months of history needed. For classification with transfer learning: works with limited data. The initial diagnosis defines what is possible with your current volume — and what needs to be collected.
How do you ensure the model works in my real business?: We validate with metrics that matter for your process, not generic accuracy. We test with real production data, not just lab holdout sets. We configure drift monitoring from the first deploy — when the model starts losing precision, you know before you feel it. And retraining is documented so your team can execute it without depending on us.
What are AI agents and how do they differ from an ML model?: An ML model makes a prediction (e.g., will this customer churn?). An AI agent acts: analyzes the contract, identifies clauses, generates the summary, categorizes it and sends it to the approver — all without human intervention. Agents combine LLMs + tools + process logic. They are the layer that turns predictions into real work automation.
Do you do fine-tuning of GPT, Claude, and other LLMs?: Yes. Depending on the case, we use: fine-tuning (for specific domain with many examples), RAG — Retrieval-Augmented Generation (for querying internal documents), advanced prompt engineering (for configuring agents in your context), or open-source models (LLaMA, Mistral) running on your infrastructure for sensitive data. The choice depends on cost, data privacy, and required performance.