We integrate artificial intelligence and machine learning into real products — not as a buzzword, but as a tool that solves specific business problems. From AI-powered content generation to predictive analytics, we build systems that deliver measurable value from day one.
What We Build
- LLM integrations — connect Claude, GPT, or open-source models to your applications for content generation, summarization, classification, and conversational interfaces.
- AI-powered automation — intelligent workflows that handle document processing, data extraction, customer support triage, and content moderation.
- Predictive analytics — models that forecast demand, detect anomalies, score leads, or recommend products based on your historical data.
- Custom AI pipelines — end-to-end systems for data ingestion, model training, evaluation, and deployment with monitoring and feedback loops.
- RAG systems — retrieval-augmented generation that lets AI answer questions grounded in your company’s documents, knowledge base, or product catalog.
Our Stack
- LLM providers: Anthropic Claude, OpenAI, open-source models
- Frameworks: LangChain, LlamaIndex, Hugging Face
- Vector databases: Pinecone, Weaviate, pgvector
- ML tools: Python, scikit-learn, TensorFlow, PyTorch
- Infrastructure: AWS SageMaker, GPU instances, serverless inference
How We Work
AI projects start with a focused proof of concept. We identify one high-impact use case, build a working prototype in 2-3 weeks, and measure results against clear success metrics. Only after validating the approach do we invest in production hardening, scaling, and integration with your existing systems.
What You Get
- Production AI features integrated into your application or workflow.
- API endpoints for AI capabilities your team can build on.
- Prompt engineering and model selection optimized for your use case.
- Cost monitoring and optimization for API usage.
- Documentation and training for your team to maintain and extend the system.