Practical AI: where LLM features actually pay off

AI has gone from novelty to line item. Boards ask about it, competitors announce it, and vendors bundle it into everything. Yet many AI features quietly go unused. The ones that succeed share a pattern: they remove tedious work from a well-understood process.

Use cases with clear ROI

• Document understanding: extracting fields from IDs, invoices, permits, and statements
• Search and retrieval: answering questions grounded in your own knowledge base
• Drafting and summarization: first drafts of reports, replies, and case notes
• Classification and routing: triaging tickets, emails, and applications
• Forecasting: demand, cash flow, and capacity planning with classical ML

Start with the workflow

We map the existing human workflow before choosing a model. Where does time go? Where do errors happen? What does a good output look like, and who checks it? The answers define both the feature and its evaluation.

Evaluation is the product

An AI feature without an evaluation suite is a demo. We build task-specific test sets from real (anonymized) data, define pass criteria with domain experts, and run them on every change — exactly like unit tests.

If you can’t measure whether the AI got better, you can’t safely change it.

Keep humans in the loop

For high-stakes decisions — credit, health, compliance — AI should propose and humans should dispose. Confidence scores, highlighted sources, and one-click corrections make review fast and turn every correction into training signal.

Watch the unit economics

Model costs add up at scale. Caching, smaller task-specific models, batching, and careful prompt design routinely cut inference costs dramatically without hurting quality.

Respect the data

Use enterprise agreements that exclude training on your data, redact personal information where possible, and keep audit logs. Under the Data Privacy Act, you remain accountable for how personal data flows through AI systems.

The best AI features don’t feel like AI at all. They feel like the software finally understands the job.