Solution

AI Chatbots Grounded in Your Own Content

A chatbot that cites its source or admits it does not know.

The chatbot market splits into two kinds of product: scripted decision trees that break the moment someone phrases a question unexpectedly, and general-purpose models that will answer anything, including things about your business that are not true. Neither is acceptable in a support context. The working design is retrieval-grounded generation with a hard refusal path.

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Why this is usually broken

  • Scripted bots break on any phrasing their author did not anticipate
  • General-purpose models will confidently invent a policy that does not exist
  • Most deployments have no way to tell you what the bot got wrong last week
  • Bots that cannot hand off cleanly damage the relationship more than a queue does

How we build it

Retrieval before generation

The model only sees content retrieved from your corpus. It cannot answer from its own training weights about your business, because it is never asked to.

A real refusal path

Where retrieval returns nothing relevant, the response is an escalation, not an improvised answer. This is enforced structurally rather than by asking the model nicely.

Answer logging and review

Every answer, its retrieved sources and its confidence are logged. Weekly review of low-confidence answers is what turns a launched bot into a good one.

Clean human handoff

Full conversation context transfers to the agent, so the customer never repeats themselves. This single detail drives most of the satisfaction difference.

What changes

Source-cited Answers link to the document behind them
Logged Every response reviewable after the fact
Full context Carried into human handoff
On-prem option For regulated deployments

Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.

Common questions

How long does a deployment take?

A grounded chatbot over existing documentation is usually live in two to four weeks. The variable is the state of your content, not the model work. If the documentation is out of date, the bot is out of date.

Does it need to be on-premise?

No. On-premise matters where the conversation itself is sensitive: healthcare, legal, financial services. For a public help center, a hosted model is usually a reasonable trade.

Where we deploy this

Retail & Consumer Goods

Predict what customers want before they ask, and stock exactly what you need.

Technology & Software

Ship faster, support better, and build AI-native products your competitors can't match.

Education

Institutional AI that satisfies FERPA before it touches a student record.

Healthcare & Life Sciences

Administrative relief and clinical intelligence, plus helpdesk, security and backup, from one provider that treats a BAA as the starting point.

The services behind it

Custom AI Agents & Automation

AI that doesn't just answer questions. It gets things done.

Hybrid & Cloud AI Solutions

Cloud AI power, routed intelligently through your security boundary.

Start with a conversation, not a proposal

Thirty minutes. We will tell you what we would change first, and whether you need us at all.

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The firm behind the firm