AI & Machine Learning Services

Custom LLMs, AI solutions, computer vision and NLP

Custom models you own
instead of third-party API dependencies
Privacy-first by default
Local LLMs, anonymized training data, your infrastructure.
Production-ready AI
Integrated into your product — not side-project demos.

Built on your data, deployed in your environment. No third-party APIs, no data leaks, no compliance headaches.

Where AI Creates Real Business Value

We specialize in the areas where AI is delivering real business value right now: AI solutions, language understanding, and visual analysis. Plus the infrastructure to make it production-grade.

LLM & AI Solutions
Custom large language models, retrieval-augmented generation (RAG) systems, domain-specific assistants and AI agents. We fine-tune open-weight models on your data, deploy them on your infrastructure and integrate them into your product through clean APIs.
Common use cases
- Domain-specific chatbots and virtual assistants
- Document Q&A and knowledge search (RAG)
- AI solutions
- Content analysis
Natural language processing
Text classification, intent analysis, summarization, extraction, sentiment analysis, and correction. From simple classifiers to custom models trained on your domain vocabulary.
Common use cases
- Support ticket classification and routing
- Content moderation and toxicity filtering
- NLP Analysis
- Document extraction and structuring
Computer vision
Image classification and segmentation, object identifications, visual content understanding. We handle both custom model development and integration of pre-trained models fine-tuned to your needs.
Common use cases
- AI image analysis
- Visual content moderation
- Product image classification and tagging
- Document and image analysis
Speech & voice AI
Speech recognition, voice synthesis and voice-driven interfaces. We build models that understand spoken language, generate natural-sounding speech and analysis sentiment — from custom voice assistants to voice analysis.
Common use cases
- Speech-to-text transcription tuned to your domain vocabulary
- Text-to-speech synthesis for voice assistants and audio content
- Custom voice assistants for products and support workflows
- Voice analysis

Custom ML models
Classical machine learning where it makes sense: classification, regression, tree-based ensembles (XGBoost, LightGBM, CatBoost). We don't push AI where simpler models work better — and cost less to run.
Data pipelines & ML infrastructure
Every ML project lives or dies by its data. We build the pipelines that clean, transform and feed data into models, plus the infrastructure to keep everything running reliably.
MLOps & deployment
Docker, Kubernetes, CI/CD for models, experiment tracking and monitoring. We handle deployment, versioning and observability so your model doesn't silently degrade six months after launch.

What Can AI Actually Do For Your Business?

AI delivers real value in specific, concrete ways. Here's what it actually looks like when it's working in a business like yours.

Language & text
- Your support team manually reads and routes hundreds of tickets dailyAI classifies and assigns them instantly
- Moderators can't keep up with users contentAI detects toxic and inappropriate content automatically
- Documents arrive unstructured and need manual processingAI extracts, structures and summarizes in seconds
AI solutions & assistants
- Clients get no response outside business hoursAI assistant answers 24/7 with your data and your tone
- Employees waste time searching internal knowledge basesAI finds the answer instantly from your documents
- Your team writes the same content types repeatedlyAI generates drafts that match your brand voice
Visual AI
- Moderators can't manually review thousands of imagesAI flags problematic visual content automatically
- Products need manual tagging before they go liveAI classifies and tags images at scale
- You need to analyse mages in your content flowAI identifies images

Our AI Tech Stack

Battle-tested frameworks and tools we actually use in production — organized by AI specialization, not a random soup of logos.

The foundations — for training models across any ML task.

PyTorch
TensorFlow
Scikit-learn
XGBoost

Backend & Infrastructure for AI Projects

AI projects deploy on the same battle-tested WDS backend and infrastructure stack — not a separate AI infrastructure, but the same proven tools we use in Web Development.

Languages & backend

Python
FastAPI
Django
Django REST Framework
Flask

Infrastructure & deployment

Docker
Kubernetes
GitHub Actions
GitLab CI/CD
On-Premise

Cloud & deployment options

AWS
On-Premise
RunPod

AI That Puts Privacy First

Most AI services today rely on third-party APIs like OpenAI or Anthropic. That works — until your legal team reads the data processing agreement. We build the alternative: custom models running on your infrastructure, trained on anonymized data, with no external calls.

Local LLMs, not external APIs
We deploy open-weight models (LLaMA, Mistral and others) on your infrastructure. Your data never leaves your environment. No ChatGPT API calls, no data sent to OpenAI or Anthropic for training.
Anonymized training data
Every dataset we work with is anonymized before training. PII removed, sensitive fields masked, synthetic data used where appropriate. Compliance-ready from day one.
Content safety built In
Content analysis and classification. Especially important for RAG systems, where incorrect outputs can misinform users or expose confidential data.
RAG security, done right
For retrieval-augmented systems: document-level permissions, vector database encryption, metadata filtering, and source attribution on every answer. Users only retrieve what they’re authorized to see.

Who this is for

Especially relevant for: financial companies, healthcare, legal firms, any team under GDPR / HIPAA / SOC 2 requirements, and products handling confidential user data.

Want to discuss a privacy-sensitive AI project?

How we turn AI ideas into reality

Every AI project follows the same framework. Each phase has clear deliverables.

Problem definition & discovery
We work with you to translate a business problem into a technical problem. What does success look like? What metrics will we track? Is ML even the right approach? This phase can save months of work later.
Data collection & preparation
The quality of any AI system is the quality of its data. We audit, clean, anonymize and structure your data, identify gaps and — when needed — generate synthetic data to fill them.
Model selection & training
We choose the right approach for your problem: fine-tuning an open-weight LLM, training a custom model, or combining both. We don't over-engineer — simpler models often win on production metrics.
Validation & MVP
A working prototype evaluated against the metrics defined in phase 1. You get a demo, benchmarks and honest feedback on what's working and what needs more iteration.
Production deployment
Moving from MVP to commercial-grade solution: additional training, edge case handling, performance optimization, Docker containerization, CI/CD, monitoring setup.
Monitoring & continuous improvement
AI models degrade over time as data shifts. We set up monitoring dashboards, alert thresholds and retraining pipelines so your AI stays sharp six months and two years post-launch.