We build chat bots that handle thousands of customer interactions at once
— answering FAQs, guiding purchases, and routing complex cases to the right person. Always on, always consistent.
Support queues
become too much.
Bots handle FAQs. Agents focus on complex issues.
Customers drop off
outside working hours.
24/7 availability.
Conversations never stop.
Bots feel robotic
and frustrate users.
Conversational design.
Natural language flows.
Integrations are nowhere
to be found.
CRM, e-commerce, and support tools connected.
Didn’t find what you were looking for? Drop us a line at info@toimi.pro.
We build chatbots across categories: customer support chatbots (deflecting common questions, routing complex issues to human agents), sales qualification chatbots (engaging website visitors and qualifying leads), internal knowledge chatbots (helping employees find information in enterprise knowledge bases), industry-specific assistants (healthcare, legal, financial), and conversational interfaces embedded in products. With modern LLM capabilities, chatbots have evolved dramatically beyond the frustrating rule-based bots of the 2010s — Redwood City companies increasingly deploy genuinely useful AI conversation.
For most Redwood City clients, we build on top of foundation LLMs (GPT-4/GPT-4o from OpenAI, Claude from Anthropic, Gemini from Google) rather than training custom language models from scratch — 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, and integration with client business systems. The custom value is in the application architecture, not in competing with foundation model training.
RAG architectures enable chatbots to answer questions using client-specific content (knowledge base articles, documentation, policies, product information) 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 (balancing precision and recall), hybrid search combining vector and keyword approaches, and citation generation so users can verify AI responses against source content. For Redwood City enterprise clients, RAG quality often determines chatbot usefulness.
Useful chatbots do things, not just answer questions. We integrate with: CRMs (logging interactions, creating leads and cases), ticketing systems (creating and updating support tickets), internal APIs (querying product data, order status, account information), calendar systems (scheduling meetings), and enterprise systems specific to client operations. For Redwood City B2B clients, chatbot integration with Salesforce, HubSpot, or Zendesk is especially common — chatbots that can take action produce significantly more value than chatbots that only converse.
Chatbot safety is business-critical — inappropriate responses, hallucinations (confidently stating incorrect information), 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, and response guardrails preventing harmful or off-brand outputs. For Redwood City regulated-industry clients, 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 (routing calls, handling simple inquiries, gathering information before human handoff), voice-enabled web applications, smart speaker integrations (Amazon Alexa, Google Assistant), and in-vehicle or embedded voice interfaces. For Redwood City clients, voice adds convenience in specific contexts — we're honest when text-based interaction would serve clients better than forced voice interfaces.
Chatbot metrics combine usage, quality, and business outcomes. Usage: conversation volume, message counts, session lengths. Quality: response relevance (evaluated through sampling and review), hallucination rates, user satisfaction ratings, conversation completion rates, escalation rates to human agents. Business outcomes: support ticket deflection rates, lead qualification rates, conversion from chatbot interactions, support cost reduction. For Redwood City clients, we emphasize business outcome metrics — chatbots that chat but don't produce business value don't justify their investment.
