Conversational AI & Chatbots
Conversational AI Development - Assistants Grounded in Your Knowledge
We build conversational AI and enterprise chatbots grounded in your own content using retrieval-augmented generation (RAG), so answers come from your documentation and policies - with citations, guardrails, seamless human handover, and analytics on what your content fails to answer.
What We Build
The generation of chatbots that trained everyone to type "agent" immediately failed for one reason: they didn't know anything. Rigid intent trees couldn't answer real questions, so they deflected them. Modern conversational AI can genuinely resolve a large share of routine queries - but only when it's grounded in your actual knowledge and honest about its limits. An assistant that invents an answer is worse than one that says "I don't know, here's a human."
Customer Support Assistants
Resolving routine queries across web, app, and messaging channels, with clean escalation for everything else.
Employee Assistants
HR, IT, and policy self-service, ending the internal-wiki scavenger hunt. Often the fastest ROI available, because internal content is easier to govern than public content.
Product and Sales Assistants
Guiding prospects through configuration, specification, and pricing questions with accuracy your sales team can trust.
Voice and Phone Integration
Where your customers still call, connected to the same knowledge and escalation paths.
The Engineering That Makes It Trustworthy
Retrieval-augmented generation (RAG). Answers are generated from retrieved passages of your documents, not from the model's general training. Done well, this means answers cite their source and your team can trace exactly why the assistant said what it said.
Guardrails and scope control. Topic boundaries, tone and brand alignment, refusal behavior for out-of-scope or sensitive requests, and prompt-injection resistance validated by our security team.
Human handover with context. Full conversation history passed to the agent, so the customer doesn't repeat themselves. Nothing destroys trust faster than a handover that resets the conversation.
Evaluation before launch. A golden test set of real questions, scored for accuracy, groundedness, and safety - plus regression testing on every change, because prompt and model updates can silently alter behavior.
Analytics that improve your content. The questions your assistant can't answer well are a content roadmap. Every deployment we run surfaces documentation gaps the business didn't know it had.
What Good Performance Actually Looks Like
We agree targets before build - containment rate, accuracy on the golden set, escalation quality, CSAT - and report against them. We deliberately don't quote industry-average containment percentages as a promise: performance depends almost entirely on the quality and coverage of your knowledge base, which we assess honestly during discovery rather than after signature.
Curious How Well an Assistant Would Perform on Your Content?
We'll build a working assistant on a slice of your real knowledge base and show you measured accuracy before you commit to production.
Your Content Is the Product
The uncomfortable finding of nearly every conversational AI project: the assistant's quality ceiling is set by the knowledge base, not by the model. Teams arrive expecting a model problem and discover a content problem. What we assess before building:
Coverage. Do documents actually exist for the questions people ask? We analyze real query logs - support tickets, search terms, chat transcripts - against your content inventory. The gap is usually larger than expected.
Currency. How much content is outdated? Stale documents don't fail loudly; they produce confident, wrong answers, which is worse than no answer at all.
Contradiction. Where do documents disagree? Retrieval will surface both, and the model has no way to adjudicate. Contradictions must be resolved in the source, not patched in the prompt.
Structure. Long, unstructured documents chunk badly and retrieve poorly. Content with clear headings and self-contained sections dramatically outperforms the same information in a wall of text.
Access. Which content is permitted for which audiences? Retrieval must respect existing permissions, or the assistant becomes a very efficient over-sharing mechanism - the same failure mode we discuss on Copilot readiness.
Retrieval Engineering, Briefly
Naive retrieval - embed everything, return the top five chunks - underperforms in ways that show up as unpredictable answer quality. What we actually build:
Chunking strategies matched to document type, respecting structure rather than splitting on arbitrary character counts
Hybrid search combining semantic similarity with keyword matching, because exact terms like product codes and policy numbers matter and embeddings handle them poorly
Metadata filtering so retrieval is scoped by product, region, audience, or recency before ranking
Reranking to improve precision on the passages that reach the model
Query understanding for follow-up questions, where the actual question depends on the previous turn
Each of these is measurable in evaluation, which is how we decide which are worth their complexity for your corpus rather than applying all of them by default.
Rollout That Protects Trust
First impressions determine adoption. An assistant that fails visibly in week one gets abandoned even after it improves.
Our staged approach: internal-only first, with staff testing against real questions; narrow scope at launch - the topics your content covers well, with everything else routed to a human immediately; visible expansion as coverage improves, so users experience the assistant getting better rather than getting worse; and always-available human escape, because the fastest way to destroy trust is to trap someone with a system that can't help them.
Frequently Asked Questions
Usually because of the content, not the model: gaps where no document covers the question, outdated documents producing confident wrong answers, contradictions between sources, or poorly structured content that chunks and retrieves badly. Content assessment before build is the highest-leverage quality work available.
Internally first, then externally with a deliberately narrow scope covering only well-documented topics, expanding as coverage improves - with an always-available route to a human. Users forgive a limited assistant; they don't forgive one that traps them.
RAG retrieves relevant passages from your own content and gives them to a language model as the basis for its answer. It's the standard way to make an AI assistant accurate about company-specific information - and to make answers citable and traceable.
By grounding answers in retrieved company sources with citations, restricting scope, instructing and testing refusal behavior, running pre-launch evaluation on real questions, monitoring continuously, and escalating low-confidence cases to humans. No credible vendor promises zero errors; the discipline is detection and containment.
A production-ready assistant typically takes six to twelve weeks: content assessment, retrieval pipeline, guardrails, integration, evaluation, and a staged rollout. Content quality is usually the critical path.
Yes - Zendesk, Intercom, Salesforce, ServiceNow, HubSpot, and custom systems. Integration is what makes handover, ticket creation, and personalization possible; without it you have a search box with better manners.
Then the assistant will expose that, which is uncomfortable but valuable. We assess content coverage first and either scope content remediation with our content design team or narrow the assistant's initial scope to well-covered topics.
No. Deployments are architected with no-training contractual terms, private cloud, or self-hosted models so your data stays inside your compliance boundary.