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.
A chat bot can pass every technical check and still embarrass a business in its first real conversation. Wiring and buttons are one thing. A live conversation is another. The gap between them gets closed by testing conversations themselves, alongside the integrations sitting behind the screen.
The obvious path is easy to write and easy to skip past. A tester types the expected question, gets the expected answer, and moves on. Real visitors do not read the script. They phrase the same question five different ways, misspell a product name, or ask two things inside one message. A useful test set includes the awkward phrasing alongside the clean version, because the clean version was never the risk.
A frustrated tone deserves its own tests. Someone typing short, angry sentences after a failed order needs a different response than someone asking a casual question before a purchase. Writing a handful of conversations that start annoyed, then checking whether the bot escalates to a person at a sensible point rather than offering another cheerful menu, catches a failure mode calm testing never finds.
Context across several messages breaks more often than a single question ever does. A test conversation should ask a follow-up that only makes sense if the bot remembers the first message. Then change the subject entirely. Then return to the original topic. Bots that handle one message well often lose the thread by the fourth line, and that is exactly where a real visitor gives up.
Ambiguous and incomplete input belongs in the set as well. A typo in a product name. A question missing a key detail. A request phrased as a complaint rather than a question. Each one tests whether the bot asks a sensible clarifying question instead of guessing or simply repeating itself back.
A person, not a script, should read the transcripts of these tests before launch. Automated checks can confirm that a reply contains an expected phrase. Only a reader notices a tone that sounds cold, an answer that is technically correct yet unhelpful, or a joke that lands wrong for the audience reading it.
Every change to a prompt, a flow, or the underlying knowledge deserves a rerun of the whole test set, including the cases that have nothing to do with the change just made. A fix aimed at one failure regularly breaks a conversation that used to work. The only way to catch that quickly is to already have the earlier tests written down and ready to repeat.
Treating the test set as a living document, growing every time a real conversation exposes a gap, turns testing from a one-time task before launch into an ongoing habit that keeps pace with how visitors actually talk to the bot.
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We build chatbots across categories: customer support chatbots, sales qualification chatbots, internal knowledge chatbots, industry-specific assistants, conversational interfaces embedded in products. With modern LLM capabilities, chatbots have evolved dramatically beyond the frustrating rule-based bots of the 2010s — Menlo Park companies increasingly deploy genuinely useful AI conversation.
For most projects, we build on top of foundation LLMs (GPT-4/GPT-4o from OpenAI, Claude from Anthropic, Llama from Meta, Gemini from Google) rather than training custom language models — foundation model capabilities exceed what most custom training budgets can achieve. We build custom value through domain-specific prompting and system instructions, retrieval-augmented generation (RAG) over client-specific content, custom tools and function calling, conversation state management, integration with client business systems. If you prefer open-source approaches, Llama models from Meta are an option.
RAG architectures enable chatbots to answer questions using client-specific content rather than only training data. We build RAG systems with vector database infrastructure (Pinecone, Weaviate, Qdrant, PostgreSQL with pgvector), embedding generation pipelines, chunking strategies for different content types, retrieval tuning, hybrid search combining vector and keyword approaches, citation generation. For enterprise chatbots, RAG quality often determines usefulness.
Useful chatbots take action. We integrate with CRMs (logging interactions, creating leads and cases), ticketing systems, internal APIs, calendar systems, enterprise systems specific to client operations. For B2B chatbots, integration with Salesforce, HubSpot, or Zendesk is common — chatbots that can take action produce significantly more value than chatbots that only converse.
Chatbot safety is business-critical — inappropriate responses, hallucinations, or off-topic conversations damage brand and customer trust. We implement system prompt engineering establishing appropriate scope and tone, content moderation for user inputs and AI responses, retrieval grounding reducing hallucination risk, escalation patterns handing off to human agents for sensitive issues, logging and monitoring enabling ongoing quality review, response guardrails. For regulated industries, safety investment is especially important.
Yes — voice AI is increasingly practical with modern speech-to-text and text-to-speech capabilities. We build voice applications: phone-based voice assistants, voice-enabled web applications, smart speaker integrations, in-vehicle or embedded voice interfaces. Siri itself originated at SRI International in Menlo Park before Apple acquired it — the Peninsula has deep voice AI heritage.
Chatbot metrics combine usage, quality, and business outcomes. Usage: conversation volume, message counts, session lengths. Quality: response relevance, hallucination rates, user satisfaction ratings, conversation completion rates, escalation rates. Business outcomes: support ticket deflection rates, lead qualification rates, conversion from chatbot interactions, support cost reduction. We emphasize business outcome metrics.
