What “practical AI” looks like
The best AI projects start with a bottleneck: time spent reading, classifying, rewriting, extracting, or routing information. Then we build a system that’s observable, testable, and safe—often with human review at the right step.
Typical workflows
Extract key fields, summarize changes, route for approval, and keep an audit trail.
Classify incoming messages, suggest replies, and route to the right owner with confidence signals.
Read invoices, structure line items, and produce human-friendly summaries for finance workflows.
Find answers in docs and systems using safe retrieval patterns and permissions.
Reliability and safety principles
- Design for review: add checkpoints where human judgement matters
- Measure quality: samples, eval sets, and clear acceptance criteria
- Build observability: logs, metrics, and error paths you can debug
- Respect permissions: retrieve only what the user is allowed to see
- Prefer small wins: ship value fast, then widen scope safely
FAQ
Often no. Many problems can be solved with good prompting, retrieval, and workflow design. Training comes later if it’s clearly justified.
Constrain the task, retrieve trusted sources, require citations where possible, and add human review for high-risk decisions.
Yes—AI is most valuable when connected to your APIs, documents, and internal workflows.