Custom AI Assistant Development

An assistant works alongside a person: it answers, drafts, summarises and proposes, and the user decides what happens next. An agent is given a goal and plans and executes several steps on its own. The two share the same building blocks - a model, retrieval and tools - but they carry very different risk, cost and testing burdens.

Most organisations get value fastest from an assistant that can also take a small number of well-defined actions with a confirmation step, such as creating a ticket or drafting a CRM update for the user to approve. Moving to more autonomy makes sense once the assistant has a measured track record on those actions and the workflow is predictable enough to describe as rules. We help you decide where on that spectrum each use case belongs rather than defaulting to the most autonomous option.

A useful assistant answers from your sources, not from what the model happened to learn in training. We connect it to the systems where knowledge actually lives - document libraries, wikis, ticket history, product catalogues, policy manuals and databases - and keep the index in sync as content changes, so retired policies stop being quoted.

Grounding quality depends less on the model than on the content pipeline: how documents are split, how tables and PDFs are parsed, how stale or duplicate pages are filtered, and how structured data is queried directly instead of being flattened into text. Answers cite the passages they used, and the assistant is instructed to say it does not know when the sources are silent, which is what makes staff willing to trust it.

The most common failure in internal assistants is not a wrong answer but a leaked one - an HR document or a salary sheet surfacing for someone who could never open it directly. We enforce access at retrieval time by carrying the source system permissions into the index and filtering every search by the signed-in user identity through your existing single sign-on.

Actions follow the same rule. When the assistant calls a tool, it does so on behalf of the user with that user scope, never with a broad service account that bypasses your controls. Sensitive operations can require explicit confirmation, and every retrieval and action is logged so security and compliance teams can review what was accessed and why.

Adoption depends on meeting people in their existing tools. We deploy the same assistant core into Slack, Microsoft Teams, a web widget, your intranet or your own app, so behaviour, permissions and knowledge stay consistent across channels. Each channel has its own realities - threads and mentions in Slack, tenant approval and app policies in Teams, authentication and rate limiting on a public website - and we handle those details rather than leaving them to launch week.

Before launch we build an evaluation set from real questions your teams ask, with expected answers and sources, and score accuracy, citation correctness and refusals on every change to prompts, models or content. After launch, feedback buttons, unanswered-question reports and sampled conversation reviews show where the knowledge base has gaps, so improvement is driven by evidence rather than anecdotes.