We build chat bots that handle thousands of customer interactions at once
— answering FAQs, guiding purchases, and routing complex cases to the right person. Available 24/7, with consistent answers.
Support queues
become too much.
Bots handle FAQs. Agents focus on complex issues.
Customers drop off
outside working hours.
24/7 availability.
Conversations continue after hours.
Bots feel robotic
and frustrate users.
Conversational design.
Natural language flows.
Integrations are nowhere
to be found.
CRM, e-commerce, and support tools connected.
The first few messages a bot sends decide whether a visitor keeps typing. A wall of text at the start reads like a form. So does a greeting with too many options. The opening turn should do one thing. Tell the visitor what the bot can help with, in words a person would actually say.
A short greeting beats a complete menu. Name three or four things the bot handles well. Then stop. That gives a visitor enough to react to, without forcing a choice from a long list. Say what the bot cannot do too, briefly, so nobody wastes three turns finding the edge of what is possible.
Buttons and free text ask different things. A button narrows the reply to something the bot already recognizes, which cuts the odds of a dead end. Free text invites a broader question, one the bot may or may not be ready for. Neither choice is wrong by itself. Trouble starts when a flow opens with free text, then stumbles the moment someone answers in a way nobody scripted.
Three or four button choices per turn is a practical ceiling. Past that, a visitor starts reading instead of choosing. That defeats the point of a button. A longer list belongs behind a catch-all option such as something else, leading into a shorter follow-up question rather than a second wall of buttons.
Once a request narrows to something specific, the bot can drop the buttons and gather details one at a time. Asking for a name, an order number and a reason for contact in one message overwhelms a visitor who only wanted to answer the first part. One question per turn feels like a conversation. Not a form in disguise.
Message length inside a single bubble matters as much as the number of turns. A short line lands faster than a dense block. Easier to scan on a phone too. A longer explanation reads better split across two or three short messages sent in sequence than crammed into one block a visitor has to reread.
Every turn should end somewhere, a question, a button set or a clear next step, rather than trailing off and leaving a visitor unsure whether to type again. That does not mean pushing toward a sale in every reply. A turn can end by offering to continue, to stop, or to talk to a person. No answer should feel preferred over another.
The first version of an opening flow is a draft, not a finished script. Real transcripts usually show one or two turns where people stop replying more than the rest. Fix that specific turn. Not the whole flow. The rest already works.
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We build chatbots across categories: customer support chatbots, sales qualification chatbots, internal knowledge chatbots, industry-specific assistants. For Westwood medical practices, patient-facing chatbots (appointment scheduling, symptom checking with appropriate disclaimers, routing to care) are particularly valuable. For education projects, academic assistant chatbots have strong use cases.
For most projects, we build on top of foundation LLMs rather than training custom language models.
RAG architectures enable chatbots to answer questions using client-specific content.
Useful chatbots take actions in your systems as well as answer questions.
Chatbot safety is business-critical. For Westwood medical chatbots, safety requires particular attention — appropriate disclaimers, escalation patterns for urgent clinical situations, HIPAA compliance for any patient interaction.
Yes — voice AI is increasingly practical.
Chatbot metrics combine usage, quality, and business outcomes.
