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AI Assistants & Chatbots

Assistants that answer from your record, not from thin air.

A custom assistant grounded in your own content, so it answers from your documents instead of guessing. Two flavors: an internal knowledge assistant for your staff, and a customer-facing chatbot for support and sales. Every answer carries the sources it came from, the guardrails are written down, and anything it cannot or should not answer hands off cleanly to a person.

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GROUNDED IN YOUR CONTENT CITES ITS SOURCES HUMAN HANDOFF
The overview

An assistant that reads your record before it answers.

A generic chatbot answers from whatever the model absorbed in training, which is why it makes confident claims about a business it has never seen. The assistants we build work the other way around. Before answering anything, the assistant retrieves the relevant passages from your own content, then writes its answer from those passages and shows them. The pattern has a name, retrieval-augmented generation, but the idea is plain: the assistant is told to answer from your record, not from its memory.

That single change is what makes one of these trustworthy enough to put in front of staff or customers. It reads your help docs, your policies, your product information, and the knowledge scattered across your systems, and it answers in your voice with the sources attached so anyone can check the work. When it does not have the answer, it is built to say so rather than fill the gap with something that sounds right.

We will be candid about this up front: grounding the assistant in your content reduces wrong answers, it does not eliminate them. That honesty shapes how the whole thing is built. Citations let a person verify any answer, guardrails set what the assistant must not say, and a clean handoff passes anything hard or sensitive to a human. The trust does not come from hoping the model behaves; it comes from the machinery around it.

Two flavors, one machine

The retrieval pipeline is the same. What changes is the content it reads and how cautious it is.

  • Internal knowledge assistant for your staff
  • Customer-facing chatbot for support and sales
  • Grounded in your own content, not generic data
  • Citations back to the source passages
  • A clean handoff to a person, by design
What is included

More plumbing than prompt.

People imagine the work is mostly writing a clever prompt. It is not. The quality of one of these assistants lives in the layers underneath the model: how clean the source content is, how it is split into passages, and how well the right passage is retrieved for a given question.

These are the real components we build and the things we deliver for you to own. The model sits in the middle of this list, deliberately, because it is one swappable part of a larger system rather than the whole of it.

What's included

  • Clean source content, deduplicated and tidied
  • Chunking into passages sized to retrieve well
  • Embeddings so content is searchable by meaning
  • A retrieval index that finds the relevant passages
  • The model that writes the answer from them
  • Citations back to the exact source passages
  • Guardrails for what it must not say or claim
  • A clean handoff to a human, with context attached
  • Evaluation against a real test set before launch
  • The prompts and configuration delivered for you to own
How we build it

Ground it, test it, then let it answer.

We build the retrieval pipeline first, set the bar with a real test set, and only put the assistant in front of people once it clears that bar. The handoff and guardrails go in from the start, not as a patch after launch.

// answer from the record, cite the source, hand off when unsure

  1. Gather and clean the source contentWe start with the documents the assistant is allowed to read and get them into shape: deduplicated, current, and free of the stale pages that would otherwise produce stale answers. Most of an assistant's accuracy is decided here, before any model is involved, because it can only be as good as what it retrieves.
  2. Chunk, embed, and indexWe split the content into passages sized to retrieve cleanly, turn each into an embedding so it can be searched by meaning rather than exact keyword, and build the retrieval index. When precision matters we add a reranking step that re-scores the top results against the question, which is one of the highest-value improvements a retrieval system can have.
  3. Wire the model to answer only from the recordThe model is instructed to answer from the retrieved passages and to attach them as citations, not to fill gaps from its training. We keep it behind a clean interface so it stays a swappable part. If a better, cheaper, or more private model fits later, it can be changed without rebuilding the system.
  4. Set guardrails and the handoffWe write down what the assistant must not answer or claim, and exactly when it stops and passes the person to a human: questions outside its content, and anything consequential. The conversation so far travels with the handoff so nobody repeats themselves. This is the behavior that keeps it trustworthy.
  5. Evaluate, then watch itWe assemble a real test set of questions your team actually gets, with the answers you would accept, and measure accuracy, whether citations genuinely support the answer, and how often it correctly hands off. That same set becomes a regression check you re-run as content, prompts, or the model change, so quality is something you watch rather than assume.
Where this fits, and where it does not

Honest about what grounding can and cannot do.

A grounded assistant is a strong fit when the answers live in content you can point it at: documented help topics, policies, product information, and the internal knowledge people keep re-asking. It shines on the high-volume, answerable questions that eat a support queue or a colleague's afternoon.

It is a poor fit, or needs a person firmly in the loop, when the answer is not written down anywhere, when it requires judgment rather than recall, or when getting it wrong carries real cost. Grounding reduces wrong answers; it does not make them impossible. Retrieval can miss the right passage, and the model can phrase something the source does not quite support. We design around that limit rather than pretend it away.

// a confident wrong answer is worse than a clean "let me get a person"

Good fit, and where to keep a human close

  • Repetitive questions answered from real documents
  • Support and sales FAQs your team repeats daily
  • Internal knowledge scattered across systems
  • Anything where a cited source can settle the answer
  • Answers that are not written down anywhere yet
  • Questions that need judgment, not recall
  • Billing, legal, or health matters: hand off to a person
  • Anything where a wrong answer carries real cost
Questions, answered plainly

Frequently asked questions

A generic chatbot answers from whatever the model learned in training, which means it will happily make things up about your business because it has never seen your business. An assistant grounded in your content works the other way around: before it answers, it retrieves the relevant passages from your own documents, then writes the answer from those passages and attaches them as citations. The pattern is called retrieval-augmented generation. It does not make the model smarter; it makes the model answer from your record instead of its memory, which is exactly what you want for anything specific to your company.

They are two flavors of the same machine, and many clients end up with both. An internal knowledge assistant points at your policies, runbooks, product information, and the answers buried across your systems, so staff can find something without interrupting three colleagues. A customer-facing chatbot points at your help docs, pricing, and policies, and handles the support or sales questions your team answers over and over. The retrieval pipeline underneath is the same. What changes is the source content it is allowed to read, the tone it answers in, and how cautious the guardrails are, since a customer-facing bot is held to a stricter standard than an internal one.

Less often, but we will not promise never. Grounding the assistant in your content and instructing it to answer only from what it retrieved cuts wrong answers sharply, because the model is working from real passages instead of guessing. It does not eliminate the problem. Retrieval can miss the right passage, or the model can phrase something in a way the source does not quite support. That is precisely why citations and a human handoff exist: every answer shows the passages it came from so a person can check it, and the assistant is built to say it does not know rather than invent. We would rather it admit the edge of its knowledge than sound confident and be wrong.

More plumbing than prompt. The real components are clean source content (the documents the assistant is allowed to read, deduplicated and tidied), chunking (splitting that content into passages the right size to retrieve), embeddings (turning each passage into a vector so it can be searched by meaning), a retrieval index that finds the passages most relevant to a question, the model that writes the answer from them, citations back to the source passages, guardrails for what it must not say, and a handoff path to a person. The model is the part people focus on, but most of the quality comes from the content and retrieval layers underneath it.

We evaluate it against a real test set rather than eyeballing a few demo questions. Before launch we assemble a set of questions your team actually gets, with the answers you would consider correct, and we measure the assistant on how accurate it is, whether its citations genuinely support what it said, and how often it correctly hands off instead of guessing. That gives you a real bar instead of a vibe. The same test set becomes a regression check you can re-run whenever the content, the prompts, or the underlying model changes, so quality is something you watch rather than assume.

It hands off, and that is the most important behavior in the whole system. When a question falls outside the content it was given, or when it touches something consequential like a billing dispute, a legal matter, or a health question, the assistant says so plainly and passes the person to a human, with the conversation so far attached so nobody has to repeat themselves. You decide where that line sits. A confident wrong answer is worse than a clean "let me get a person for this," and the handoff is what keeps the assistant trustworthy rather than merely impressive.

No. We build the assistant so the model is a swappable part behind a clean interface, which means the content, the retrieval index, the prompts, and the guardrails do not depend on any one provider. If a better or cheaper model comes along, or if a privacy requirement points you toward a different one, it can be changed without rebuilding the system around it. The prompts, the configuration, and the data are yours, in your accounts, documented for your team. You own the assistant, not a subscription to our version of it.
The rest of the codex

Related services

Assistants sit alongside the rest of our AI Systems & Automation work. For a deeper read on the thinking behind grounding an assistant in your own content, the Insights piece below is the long-form version of this page.

THE HUB AI Systems & Automation Overview of AI Systems & Automation services
Workflow & Document Automation AI on intake, routing, document extraction, classification, and reporting
AI Agents Agentic systems that take multi-step actions, with guardrails and human-in-the-loop
AI Integration Add AI features inside your existing site, app, or product
AI Strategy & Readiness Where AI pays off, data and tooling readiness, governance, and rollout
FROM THE INSIGHTS An AI assistant on your own content Why grounding in your record beats a generic bot, and where it still needs a human.

Have a stack of documents people keep asking about?

Tell us the questions your team or your customers ask over and over, and where the answers live. We will give you a straight read on whether a grounded assistant fits, what it would take to ground it well, and where a person still needs to stay in the loop.

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