RAG chatbot development for your own business data
We connect your existing software to an AI model so your team can ask a question in plain English and get an answer from live data. No dashboards to learn, no reports to run.
The problem
You have data spread across your CRM, accounting software, project tools, and internal databases. When someone asks "how did we perform last quarter?" or "which clients haven't been invoiced yet?", someone has to log into three systems, export CSVs, and build a summary.
That takes hours and the answer is already outdated by the time it's ready.
What is a RAG chatbot?
RAG stands for retrieval-augmented generation. Rather than relying on what a model absorbed during training, the system retrieves the relevant facts at the moment the question is asked and puts them in front of the model before it answers.
The part worth asking a vendor about is where that retrieval goes. In our builds it is usually a live query against your systems, Salesforce, QuickBooks, your support desk, rather than only a search over an index of documents. The difference is not academic: a live query returns today's invoice balance, and an index returns whatever was last crawled. Documents and contracts index well, transactional records do not, and plenty of builds need both.
Questions your team could ask
Once we connect your systems, your team types questions in plain English and gets answers pulled from live data:
"What's our revenue from enterprise clients this quarter compared to last?"
Pulled from your CRM and accounting system in real time. No exports needed.
"Which invoices are overdue by more than 30 days?"
Queried from your billing system with client names, amounts, and contact details.
"Show me all urgent support tickets from last week that haven't been resolved"
Pulled from your support desk with ticket details and assigned agents.
"What's the average time from first contact to closed deal this month?"
Calculated from your CRM pipeline data, compared to previous months.
How this differs from ChatGPT
ChatGPT can reach some business systems now. Salesforce documents ChatGPT as a supported MCP client, and HubSpot ships a connector inside ChatGPT. (Verified July 2026, against Salesforce's hosted MCP server documentation and HubSpot's connector documentation.) What those connectors give you is what the vendor chose to expose, scoped the vendor's way, one system at a time. They do not reach your internal database or a bespoke billing table, and they do not join three systems and compute an answer. That last part is the job.
Access is scoped, authenticated, and logged, so the assistant only reads what you allow, and only the data needed to answer a question is sent to the model.
Hosted model or fully on-premise
These are two different products and it is worth being exact about which one you are buying. The default connects a commercial model over an API with scoped access: strongest reasoning, and only the data needed for a given question leaves your network. Fully on-premise runs an open-weight model on hardware you control, so nothing leaves at all.
The second option is real, and it costs you something. The open-weight models you can self-host generally sit a step behind the frontier commercial ones on hard reasoning, and you carry the GPU bill. If a regulator or a client contract requires it, that trade is worth making. If it does not, scoped API access is usually the better build, and we will say which one we think your case needs.
What you get
A chat interface your team can access from their browser or Slack, connected to the systems that matter. We set up the connections, handle the access rules, and tune the assistant to your business terminology. That work and the handover are part of the project.
Ongoing monitoring, changes, and new data sources are not bundled into the build price. They run on a monthly retainer, which is step 05 of how we work, and it is a separate decision once the assistant is live.
A first assistant connected to two or three systems is typically scoped at 2-4 weeks. We commit to a date in the proposal rather than quoting an average.
Under the hood: We build API connectors to your internal systems and expose them over the Model Context Protocol (MCP), the open standard for connecting AI applications to external data sources and tools. (Verified July 2026.) All access is authenticated, logged, and scoped to what you approve. The same connectors are what our API integration work produces, so an assistant and a sync can share them.
Tell us what questions your team keeps asking
We'll show you what an AI answer looks like with your actual data. Our example scenarios show the shape of the work.
hello@telora.dev