04 — AI & Automation
Put intelligence where the work actually happens.
AI becomes useful when it removes effort from an existing workflow—not when the product is rearranged around a chatbot.
We identify high-leverage decisions and repetitive operations, then build intelligent capabilities into the systems people already need to use.
Start with leverage, not novelty.
Not every product needs AI, and saying so is part of the job. Where it does belong, the interesting engineering is in the surrounding experience: confidence, provenance, human review, failure states, permissions and observability.
Capabilities
- AI workflows
- LLM integrations
- Retrieval systems
- Agents
- Document intelligence
- Business automation
Ideal when
- people are re-keying information between systems
- a decision depends on reading a lot of unstructured text
- search returns documents when it should return answers
- a process is well understood but tedious
- review capacity, not model quality, is the bottleneck
Relevant work
Fictional demo engagements
Related reading
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Architecture decisions your users eventually feel
Database boundaries and caching strategy sound like implementation details until they become loading states and support tickets.
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Real-time UX is a state-management problem before it's a networking problem
Opening the socket takes an afternoon. Deciding what the screen means when messages arrive out of order takes the rest of the project.