Services 03
AI is more useful when it can actually do something.
A model on its own answers questions. A model connected to your documents, your database, your tools, and your workflow can take work off someone's desk.
I build that connective layer: retrieval, tool calling, structured outputs, validation, and the human checkpoints that make it safe to rely on.
The model is one step. The steps around it are what make the output usable.
What this covers
Not every project uses all of it. Most use a handful.
- LLM Integrations
- AI Agents
- Tool Calling
- Structured Outputs
- Workflow Orchestration
- RAG
- Document Processing
- OCR
- Data Extraction
- Classification
- Summarization
- AI Assistants
- AI-Enabled Internal Tools
- API-Enabled Agents
- Prompt Engineering
- Human-in-the-Loop AI
- Local AI
AI stack
- OpenAI
- Anthropic / Claude
- Mistral
- Hugging Face
- Ollama
- LangChain
- PyTorch
- TensorFlow
- AI Agents
- LLM Integrations
- RAG
- Prompt Engineering
- Tool Calling
- Structured Outputs
- OCR
- Claude Code
- OpenAI Codex
The goal isn't to add AI everywhere.
It's to find where AI actually removes a bottleneck.