AI Development
AI that ships as a working system, not a demo
We build AI agents, automation workflows, generative AI features, and the data infrastructure that makes them reliable in production — not a proof-of-concept that stalls after the first demo.
What’s included
- AI agents — task-specific agents integrated into existing workflows and systems.
- Automation — replacing manual, repetitive processes with AI-driven or rules-based automation.
- Generative AI features — embedded directly into products, not bolted on as a chatbot widget.
- Data pipelines & infrastructure — the ingestion, storage, and processing layer that AI features actually depend on.
- Model integration — working with existing LLM providers and APIs rather than building models from scratch, where that’s the right call.
How we approach it
- 01
Identify the actual use case
Where AI removes real manual effort or unlocks a capability, not where it’s simply fashionable.
- 02
Design the data flow
AI is only as reliable as the data feeding it; this gets designed, not assumed.
- 03
Build and integrate
Agents and features shipped into your actual product or workflow.
- 04
Monitor and refine
Production AI systems need ongoing evaluation, not a one-time launch.
Who this is for
Teams that want AI to solve a specific operational or product problem — not teams looking for an "AI strategy" slide deck with nothing built behind it.
Core technologies
Our AI work combines deep expertise in system integration, data engineering, and process automation with current AI tooling: LangGraph, n8n, dbt, Apache Airflow, MuleSoft, and Python.
FAQ
- AI agents, workflow automation, generative AI product features, and the underlying data pipelines and infrastructure that support them.
- Not necessarily — data pipeline design is often part of the project itself. It’s assessed during scoping.
- Yes — most AI development work integrates into systems that already exist, rather than starting from a blank slate.