We build software the way we would want ours built

That is a successful assessment, and it happens. You have spent two to four weeks instead of two quarters, and you usually leave with something more useful: which data to start capturing, or a rules-based approach that gets most of the value now. We would rather tell you early than bill you for a model that cannot work.

A chatbot is a conversation you go to; a copilot is help that comes to you. The copilot already knows the record you are on and what you are permitted to see, and it proposes actions you accept in place. Practically, the difference shows up in acceptance rate, copilots are used because they cost the user nothing to try.

AI Consulting covers the whole AI surface, machine learning, automation, integration. This one is narrower and deeper on generative AI specifically: prompt and model economics, disclosure and retention policy, hallucination risk, and the pilot design that settles whether to go further. Teams who already know they want GenAI start here.

Grounding plus citations. Answers are drawn from your own content with the source attached, and the assistant is built to say it does not know rather than to fill the gap. Anything unanswered is logged and reported, so the fix is a content change you can see the need for.

Usually, yes, and it is most of what integration work actually is. Where an API exists we use it; where one does not we build a thin service in front rather than letting the AI reach into a database it does not understand. The audit tells you which of those you are dealing with before anyone commits to a timeline.

Often, and it is a common starting point. We begin with an audit of tenancy, permissions and data model, because those three decide whether the codebase can be extended or should be replaced in slices. You get that answer in writing before committing to a build.

Sometimes, and the audit answers it honestly. The deciding factors are usually whether the data model is sound and whether authorisation can be centralised, if both are salvageable, incremental refactoring is almost always cheaper and far less risky, because you keep shipping. If they are not, we will say replace, and scope it in slices rather than as a big-bang rewrite.

It depends on what the app has to do, and we argue it from your requirements rather than a default. Cross-platform wins for most business and content apps and roughly halves the cost. Native wins where you need deep platform integration, heavy graphics, background processing or the newest OS features on day one. We put the trade-off in writing during discovery so the choice is yours.

The retrieval layer enforces access using the permissions your document system already holds, so it never surfaces a document a user cannot open. We test matter separation and ethical walls explicitly before launch, and the system logs every query for audit.

In most cases yes. We integrate with ServiceNow, Jira Service Management, Freshservice, Zendesk, and similar platforms through their APIs, so your platform stays the system of record. Where an API is limited we work alongside it with an event queue rather than forcing a migration.