AI features that solve a real problem, not just show you have AI

AI integration means adding AI to your existing software through the APIs of model providers, not training your own model. The engineering is in giving the model the right context, handling its output reliably and building a screen people will use.

• AI API integration

  Your app connected to model providers, with error handling, rate limits and cost controls.

• Document processing

  Structured data pulled out of invoices, forms and emails.

• Summaries and sorting

  Long inputs made short, or sorted into the right categories.

• Search over your own data

  Answers drawn from your documents, with a retrieval layer for accuracy.

• Workflow automation

  AI as one step in an automated process, not a feature people have to remember to use.

• Testing and guardrails

  Accuracy measured, and output limited when it would do harm.

• Someone reads documents and types out what they find. Extraction is reliable and saves hours.
• Things are sorted by hand before anyone acts. Routing, triage and tagging are dependable uses.
• People search your own content. Retrieval over your documents beats asking a model from memory.
• Long inputs need short versions. Calls, reports and email threads.
• The work can live with the odd mistake. If a person reviews the output, AI fits well. If a mistake would cause harm, it needs tight limits or a different approach.

Model APIs from the major providers, retrieval pipelines over your data, vector storage where it is needed, background queues for long tasks, Node.js and Python to orchestrate it, validated structured output and accuracy tests before and after release. We do not train models. Existing ones are faster, cheaper and more accurate for most business problems.

1. Find the task worth automatingOften not the first one suggested. We look for repeat work with a clear right answer and a measurable cost.
2. Prototype on real dataA small version tested on your actual inputs, not tidy examples.
3. Agree what accurate meansWhat counts as correct, how often it must be right and what happens when it is wrong.
4. Build the integrationInside your existing app and workflow, with cost and failure controls.
5. Monitor in productionAccuracy and cost tracked over time, because models and data change.

• Accuracy is defined before the build. So you do not end up with an impressive tool nobody trusts.
• Cost controls from day one. Rate limits, caching and model choice keep running costs predictable.
• We say when a rule beats a model. Plenty of tasks called AI work better as ordinary code.
• We know how AI tools read the web. Our AI search optimization work runs on the same understanding.