Every service, broken down.

Structured fields out of invoices, forms, and scanned records.

Routing tickets, applications, or claims to the right queue automatically.

A defined sequence of actions an LLM can carry out on its own, within stated limits.

Answering questions from your own documents, with every answer traceable to a source.

When in-house capacity can't keep up with what's already scoped and prioritized.

Continuous delivery on an existing product, alongside your own team.

Keeping a system nobody in-house wants to own running, documented, and gradually improved.

Engineering changes with a hard external deadline — a regulatory requirement, an audit finding, a vendor migration.

Systematic identification of exploitable weaknesses, then controlled attempts to exploit them safely.

Testing with no internal knowledge of the system — a real external attacker's vantage point.

Partial system knowledge — the middle ground between an outsider's view and a full code audit.

A full walkthrough of the source code and architecture alongside the test.

What your systems actually collect, where consent is properly recorded, and what closing the gap requires.

Closing the specific gaps a test surfaces — not a generic checklist applied regardless of findings.

Connecting a system that was never built to talk to anything else to the rest of your stack.

Moving data from where it's created to where it's needed, with validation at each step.

Cleaning, structuring, and labeling data so it's actually usable.

The layer between two systems that don't speak the same language.

Getting data out of hardware and industrial protocols and into a system that can use it.

The interface your team or your customers actually use — built to the same delivery model as everything else.

An audit of what you're actually running today, before any AI or integration work starts.

Where AI genuinely fits in your systems and where it doesn't, made explicit before anything gets built.

Monitoring and maintaining an AI system after it ships, instead of letting it degrade quietly.

A defined piece of software, scoped and quoted after discovery, delivered at a fixed cost.

Rebuilding the parts of a prototype that don't survive contact with real data, load, or users.

Written documentation of what was built and why, so your team can extend it without calling back.