Service Chatbots & Assistants

Chatbots that know your business — and admit what they don’t.

Everyone's met the chatbot that confidently invents a refund policy. That's what happens when a model gets a company logo but not company knowledge. I build enterprise chatbots and internal assistants grounded in your actual documents, policies, and data — every answer cited to its source, every unknown escalated to a human, every conversation on a scoreboard.

Source-cited Every answer, receipts included

Escalates Unknowns go to humans, not fiction

Deflection % The KPI, tracked from day one

The chatbot graveyard is full of two species: the rigid decision-tree bot users learned to bypass with "agent, agent, agent," and the ungrounded LLM bot that improvises policy until legal shuts it down. Courts have already held companies liable for what their chatbots invented. The fix isn't less AI — it's grounding, containment, and measurement.

Mine are built on the same retrieval architecture as my RAG systems: answers constrained to what your documents actually say, cited so agents and auditors can check them, with permission-aware retrieval so the bot only knows what that user is allowed to know. When confidence drops, it says so and hands off to a human with full context — the transcript, the sources, the attempted answer. That handoff is a feature, not a failure.

Where these earn their keep

Customer support deflection — tier-one questions answered from your knowledge base, measured on deflection rate and CSAT, not message volume.
Internal assistants — HR, IT, policy, and ops questions answered from your own documentation, with citations, inside your access controls.
Sales and onboarding — product questions answered accurately at 2 a.m., escalated warmly at 9 a.m., logged either way.

A chatbot's job isn't to always have an answer. It's to be right when it answers and honest when it can't — in that order.

Four things a production chatbot must do.

Built in from the first sprint — because retrofitting trust doesn’t work.

01 / Ground

Answer from your sources

Retrieval over your documents, policies, and data — hybrid search plus reranking, the same architecture as my RAG builds.

02 / Contain

Stay on the rails

Scope guardrails, tone control, and prompt-injection defense so the bot can’t be talked into discounts, policies, or opinions you never wrote.

03 / Escalate

Hand off like a professional

Low-confidence and high-stakes conversations routed to humans with full context — transcript, sources, and what the bot almost said.

04 / Measure

Report like a team member

Deflection rate, resolution accuracy, CSAT, and cost per conversation — reviewed like you’d review any hire.

RAG-groundedCitationsHuman handoffInjection defenseMulti-channelAnalytics

Questions, answered.

The ones every buyer asks first.

How is this different from an off-the-shelf chatbot platform?

Platforms give you the chassis; the hard part is grounding it in your knowledge with your permissions and measuring whether it is right. I build that layer — sometimes on a platform you already own, sometimes custom. The differentiator is answer quality you can audit, not the widget.

Can the chatbot be prevented from making things up?

Substantially, yes. Grounded generation constrains answers to retrieved sources, citations make every claim checkable, and confidence thresholds route uncertain cases to humans. The honest engineering answer is that hallucination is minimized and contained, not magically abolished — which is exactly why the escalation path and eval suite matter.

What does an enterprise chatbot cost to run?

Well-architected, typically cents per conversation — model routing sends easy questions to cheap models and hard ones to frontier models. I quote against your volume and report cost per conversation next to deflection rate, so the ROI math stays public.

Which channels can it live on?

Web, Slack, Teams, SMS, and behind your support desk (Zendesk, Intercom, ServiceNow) — same brain, same grounding, every channel. Internal assistants usually start in Slack or Teams; customer-facing ones on web chat with support-desk handoff.

Build the bot your customers don’t apologize for.

If your team keeps answering the same hundred questions, that's the corpus. Let's put it to work — cited, contained, and counted.

Let's talk

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Remote-first across the United States and internationally — including these markets.

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