Artificial Intelligence & Automation
Full coverage of the 6 areas of modern AI, software-only: machine learning (supervised, unsupervised, reinforcement learning), deep learning with multi-layer neural networks for complex data, natural language processing for chatbots, machine translation, sentiment analysis and information extraction, computer vision (see dedicated service), robotics software (ROS/ROS2, simulation environments, motion planning, perception stack, sensor fusion — the intelligence that drives the machine, not the electronics that build it), rule-based expert systems for regulated domains (finance, healthcare, legal). End-to-end coverage: data pipelines, training, fine-tuning, RAG, autonomous agents, function calling, guardrails, MLOps and drift monitoring in production. Concrete, measurable AI in your operational flows — not demos.
What we deliver
- Training and fine-tuning of supervised, unsupervised, and reinforcement learning models on proprietary datasets
- Multi-layer deep learning architectures for time series, tabular data, and complex signals
- NLP pipelines: named entity recognition, sentiment analysis, information extraction, neural machine translation
- RAG (Retrieval-Augmented Generation) implementation with vector stores and private or open-weight LLMs
- Autonomous agents with function calling, tool use, and multi-step orchestration (LangChain, LlamaIndex, custom frameworks)
- Robotics software: ROS/ROS2 stack, motion planning, perception stack, and sensor fusion
- MLOps: CI/CD for models, production drift monitoring, dataset versioning, and model registry
- Rule-based expert systems for regulated domains (finance, healthcare, legal) with verifiable audit trails
When you need it
Manufacturing company with repetitive manual processes
Your operators handle tasks that could be classified, extracted, or decided automatically. Operational costs keep rising and scalability is capped. You need a system that learns from historical data and replaces or assists those steps in production.
Software house adding AI to an existing product
The product works, but competitors are shipping predictive and conversational features. You don't have an internal ML team and don't want to build one. You need a partner who can bring the model into your product without rewriting the architecture.
Company in a regulated sector requiring traceable decision systems
Finance, healthcare, or legal: you need to automate decisions but can't use black-box models. You need a rule-based or hybrid system with explainable outputs, auditable logs, and compliance with sector-specific regulations.
Company with autonomous machines that need smarter software
The hardware already exists. The problem is the perception, planning, and control software. You need ROS/ROS2 and sensor fusion expertise to evolve autonomous behavior without touching the physical platform.
Frequently asked questions
How long does it take to get a model into production?
It depends on data availability and quality. With a clean dataset and a well-scoped use case, a first deployed model takes 6-12 weeks. If data needs to be collected, labeled, or cleaned, expect 3-5 months. Continuous monitoring and retraining are separate workstreams and need to be scoped at design time.
We already use ChatGPT or other LLMs — what do you add?
A generic LLM doesn't know your data, your processes, or what it should and shouldn't say in your context. We build the application layer: RAG on your private knowledge base, agents with controlled access to internal systems, output validation, and hallucination monitoring in production.
Our data is sensitive. How do you handle privacy?
Models can run on private cloud, on-premise, or in isolated VPC environments. For regulated sectors we work with open-weight models deployed internally — no data leaves your infrastructure. The deployment architecture is agreed before writing any code.
What happens when the model degrades over time?
Data drift is natural: real-world data shifts away from the training distribution. We implement statistical monitoring on input and output distributions, with configurable alert thresholds. When performance drops below a defined level, a semi-automatic or manual retraining process is triggered based on system criticality.
We need explainable AI for our auditors. Is that feasible?
Yes. For regulated contexts we use interpretable-by-design models (decision trees, logistic regression with feature engineering) or post-hoc techniques like SHAP and LIME on more complex models. In both cases we produce technical documentation of decision outputs suitable for audit use.
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