AI Agents and Assistants Connected to Your Business Data

A general chatbot answers from whatever it learned in training. An agent works from your material: price lists, policies, past proposals, support tickets, spreadsheets and your CRM (where customer records live). It looks up the relevant pieces, writes an answer from them and shows where the answer came from.

The method is called retrieval, often shortened to RAG. In plain words, the assistant searches your content first and writes from what it found. That keeps its answers tied to your facts.

Some agents also do tasks: create a ticket, update a record, draft an email or book a slot, using only the tools you allow. To connect those tools we often use MCP, the Model Context Protocol, a standard way to give an assistant access to a tool under rules you set.

A job that repeats, uses information you already have, and is easy for a person to check. For example:

• An internal assistant that answers staff questions from policy documents, with a link to the source page.
• A helpdesk assistant that handles the common questions and passes the rest to your team, conversation attached.
• An assistant on your website that answers from the site's own content and captures a lead when the visitor is ready. Our own product, Site Bite, does this.
• An email assistant that reads incoming mail, tags it and drafts a reply for a person to approve.

We study the job as it is done today, collect the documents and look at the real questions people ask. Then we pick one job and write it down: the sources, the tools, the limits, and the moments it must hand over. Anything risky stays with a person.

We build the search over your content, the connections and the hand-over, and you try working versions with real questions early. Launch starts with a small group, then widens. Your team gets documentation and a recorded walkthrough.

• Secure by default: the assistant can reach only the sources and tools it is given, staff see only what their role allows, and providers are chosen so your content is not used for training.
• Built to scale: once one job works, the same foundations carry the next job, without starting again.
• Tested before it ships: a written test set of real questions, plus the awkward cases: questions outside its job, documents that contradict each other, and users asking it to ignore its rules. You get the results in a short report.
• Looked after after launch: documents change and so do the models behind the assistant, so an answer that was right in spring can be wrong by autumn. Regular reviews catch that, and they can sit inside a Care Retainer.
• Small changes handled, not invoiced: updating a source document or adjusting a rule after a review gets done, not quoted.
• Honest advice: if a fixed set of steps would do the job, a plain workflow automation is simpler and cheaper to run, and we will say so.

If the information you want it to use is not written down anywhere, there is nothing to search, and the first job is organising your knowledge. If its output would drive a legal or safety decision with nobody checking, we will tell you a person must review it. And if you are not sure AI fits at all, the AI Opportunity Audit comes first.

An assistant is only as good as what it can reach. If the job lives mostly on WhatsApp, see WhatsApp AI reception. Tasks that move data between systems are workflow automation. Customer personal data means consent and retention under the DPDP Act, where we implement and partner counsel advises on interpretation. The same team builds the assistant, the connections and the safeguards around them.

Tell us the job you want done, and we will say plainly whether an assistant suits it. You can also read about our wider AI Systems & Automation work.