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AI & machine learning development.
We integrate AI and machine learning into software products — delivering practical automation, NLP pipelines, computer vision, and recommendation engines that ship directly into real production applications.
What we build.
We integrate AI and machine learning into software products that solve business problems — automation that reduces manual work, analytics that surface patterns humans miss, and features that improve user experience. Our AI work ships inside production applications, not as experiments.
From models to product.
- LLM integration and custom AI assistants (OpenAI, Anthropic, open-source models)
- Retrieval-Augmented Generation (RAG) systems for enterprise knowledge bases
- Natural language processing — text classification, entity extraction, semantic search
- Computer vision — object detection, image recognition, video analysis
- Recommendation engines for content, products, and personalization
- Predictive analytics and forecasting models
- Intelligent automation — document processing, data extraction, workflow optimization
- ML model deployment, monitoring, and retraining pipelines
- AI-powered features embedded into existing web and mobile products
Who we work with.
Product teams adding AI features to an existing product
You have a working product and want to add intelligence — smart search, recommendations, content generation, or automated workflows. You need engineers who understand both ML and production software, not a research team that delivers a notebook.
Companies automating manual, repetitive processes
Your team spends hours on document processing, data entry, classification, or review tasks that follow clear patterns. You need an automation layer that handles the volume and escalates the edge cases to humans.
Startups building AI-first products
Your product idea is built around an AI capability — a chatbot, a content engine, an analytics tool. You need a team that can build the full stack around the model: the frontend, the API, the data pipeline, and the deployment infrastructure.
Enterprises exploring LLM and GenAI adoption
You want to use large language models internally — for knowledge retrieval, document summarization, customer support automation, or code generation. You need a team that can build secure, private RAG systems connected to your internal data.
How we work.
From idea to production.
01.We validate before we build
AI projects fail most often because the problem was wrong, not because the model was bad. We start every engagement with a feasibility assessment — reviewing your data, defining success metrics, and confirming that ML is the right approach. If a rule-based solution solves the problem, we say so.
02.Built for production, not demos
A model that works in a notebook is not a product. We build the full pipeline — data ingestion, model serving, API layer, monitoring, and retraining. Your AI feature runs in production with the same reliability as any other part of your software.
03.Built into your product
Our AI engineers work alongside your frontend and backend team. ML features ship inside your existing application — same codebase, same deployment pipeline, same sprint cycle. No separate "AI silo" that produces artifacts your engineering team can't maintain.
Technology we use.
- ML frameworks
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face Transformers
- LLM & GenAI
- OpenAI API
- Anthropic Claude API
- LangChain
- LlamaIndex
- Ollama
- vLLM
- NLP
- spaCy
- NLTK
- Sentence Transformers
- PEFT / LoRA
- Computer vision
- OpenCV
- YOLO
- MediaPipe
- Data & pipelines
- Pandas
- Apache Airflow
- dbt
- Data stores
- PostgreSQL
- MongoDB
- Redis
- Vector databases
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Infrastructure
- AWS SageMaker
- GCP Vertex AI
- Docker
- Kubernetes
- FastAPI
- MLflow
- Monitoring
- Weights & Biases
- Prometheus
- Grafana
FAQ.
Before you ask.
It depends on the use case. For LLM-based features (chatbots, summarization, RAG), your existing documents, knowledge bases, and support tickets are usually enough. For custom ML models (predictions, classification, recommendations), you need labeled training data — we help you assess what you have, identify gaps, and design a data collection strategy before any modeling begins.
Yes — this is one of our most common engagements. We embed ML-powered features into existing web and mobile applications: smart search, content recommendations, automated classification, document processing, and AI assistants. Our engineers integrate directly into your codebase and deployment pipeline so the AI feature ships like any other product update.
Both, depending on what the problem requires. For many NLP and generative tasks, fine-tuned LLMs or API-based models (OpenAI, Anthropic) are the fastest and most cost-effective path. For domain-specific problems — specialized image recognition, proprietary classification, or edge deployment — we train custom models on your data. We recommend the approach during the feasibility phase.
We support on-premise and private cloud deployments for sensitive data. For LLM projects, we can use self-hosted open-source models (Llama, Mistral) instead of third-party APIs when data cannot leave your infrastructure. All data processing pipelines are designed with access controls, encryption, and audit logging from the start.
A focused integration of an existing LLM or pre-trained model into your product takes 4–8 weeks. A custom ML feature with data preparation, model training, and production deployment takes 8–16 weeks. Ongoing ML systems with retraining, monitoring, and iteration are scoped as dedicated team engagements. We define the timeline during the feasibility assessment.
Start with clarity.
Not sure where AI fits in your product?
Start with a feasibility assessment. We'll review your use case, evaluate your data, and tell you honestly whether AI is the right approach — before any commitment to a full engagement.