--- title: "Hire AI Chatbot Developers | Custom Chatbot Agency | Vindler" description: "Hire senior chatbot developers for production AI chatbots with RAG, multi-turn conversation, and CRM integration. Custom chatbot development agency for customer support, sales, and internal knowledge bots." url: "https://vindler.solutions/hire/chatbot-developers" locale: "en" updatedAt: "2026-11-12" --- # AI Chatbots That Actually Solve Problems, Not Just Apologize We build production chatbots that understand context, retrieve accurate information, and know when to escalate. Not another widget that loops through “I apologize for the inconvenience” until the customer gives up. Tell Us About Your Project Technology Partners [AWS Partner Network](https://aws.amazon.com/partners/)[LangChain](https://www.langchain.com/) Recognized by Clutch [Top Clutch AI Consulting Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company São Paulo 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch Chatbot Company Brazil 2026](https://clutch.co/profile/vindler-solutions) Carlos Dutra, Founder of Vindler Who you work with ## Carlos Dutra, Founder Vindler is led by a senior engineer, not a sales team. Carlos has spent 15+ years building and leading data and AI systems, holds an MSc in Applied Mathematics (machine-learning research) from USP, and completed MIT Sloan Executive Education. He has led teams at Wildlife Studios and Trustly, ships production AI on AWS, and contributes to open source. A Toptal Verified Expert since 2020. MSc, Universidade de São PauloMIT Sloan Executive EducationToptal Verified Expert15+ years engineeringHugging Face Diffusers contributor [Quoted in VentureBeat on how enterprises should optimize for AI-referred traffic.](https://venturebeat.com/technology/llm-referred-traffic-converts-at-30-40-and-most-enterprises-arent-optimizing) Experience across Wildlife Studios Trustly Typeform Toptal Selected work ## Production systems, real outcomes A few engagements in depth, with the numbers that matter. AI-Powered Sales Assistant with RAG Automotive / Sales ### AI-Powered Sales Assistant with RAG A production RAG and analytics assistant for automotive dealers - Shipped to production on Kubernetes across CPU and GPU fleets with GitOps delivery - Hard multi-tenant isolation: dealer-scoped SQL validation blocks cross-dealer data access - Grounded RAG with an anti-hallucination fallback instead of fabricated answers AWS BedrockpgvectorBigQueryClaudeSegment Anything Named reference available on request. [Read the full case study](https://vindler.solutions/case-studies/ai-sales-assistant-rag) Multi-Agent Systems Architecture Enterprise AI ### Multi-Agent Systems Architecture A production supervisor-worker multi-agent assistant for a form-building platform - Production multi-agent assistant: six specialized agents and 90+ backend tools over MCP - Reusable agent framework (shared graph, A2A, MCP, human-in-the-loop) that scales by adding agents - Durable human-in-the-loop approvals that survive container restarts LangGraphAWS Bedrock AgentCoreA2A ProtocolMCPOpenAI GPT Named reference available on request. [Read the full case study](https://vindler.solutions/case-studies/multi-agent-systems-architecture) Voice AI Assistant for Medicare Patients Healthcare ### Voice AI Assistant for Medicare Patients A voice-first companion for elderly patients on bedside tablets - Runs in production on kiosk tablets with end-to-end observability on every call - True voice-and-touch multimodal interface designed for elderly accessibility - Six voice skills (medication, reminders, weather, news, trivia, word games) plus web search LiveKitAssemblyAIOpenAI GPT-4.1CartesiaSilero VAD Named reference available on request. [Read the full case study](https://vindler.solutions/case-studies/medicare-voice-assistant) [See all case studies](https://vindler.solutions/case-studies) ## What We Build with AI Chatbots From customer support to sales and internal knowledge, we deliver chatbot solutions that scale. ### Customer Support Chatbots AI chatbots that actually resolve customer issues. We build support bots with RAG-backed knowledge retrieval, multi-turn conversation handling, order and account lookups, and intelligent escalation that routes to the right human agent with full conversation context. ### Sales & Lead Qualification Bots Conversational AI that qualifies leads, answers product questions with real-time data, and books meetings. We integrate with your CRM so the chatbot has full customer context and every interaction is logged for your sales team. ### Internal Knowledge Bots AI assistants for your team that answer questions from internal documentation, policies, and procedures. We build chatbots that search your Confluence, Notion, Google Drive, and internal wikis, providing sourced answers with citations so employees trust the responses. ### Omnichannel Chatbots Deploy one chatbot across web, Slack, WhatsApp, SMS, and email. We build the conversation engine once and connect it to every channel your customers use, maintaining conversation history across channels for seamless handoffs. ### Chatbot Analytics & Optimization Comprehensive dashboards showing resolution rates, escalation reasons, user satisfaction, conversation length, and cost per resolution. We use LangFuse to trace every conversation and identify where your chatbot fails so we can fix it systematically. ### Multilingual Chatbots Chatbots that work in your customers' languages. We build multilingual systems that detect language automatically, retrieve from language-specific knowledge bases, and maintain conversation quality across English, Spanish, Portuguese, and other languages your market requires. Built by Senior Engineers ## Why Most AI Chatbots Fail (And How We Build Ones That Work) The market is flooded with chatbot builders that promise AI-powered customer support in five minutes. They work for the demo. Then real customers start typing messy queries with typos, asking follow-up questions that require context from three messages ago, requesting actions that need API calls to your backend, and switching topics mid-conversation. The five-minute chatbot apologizes and suggests contacting support, which defeats the entire purpose. Production chatbots are distributed systems. The conversation engine needs to maintain state across sessions, the retrieval pipeline needs to find the right answer from thousands of documents, the action layer needs to execute operations (refunds, order changes, account updates) with proper authentication and validation, and the escalation logic needs to route to the right human team with full context. Getting any one of these wrong means a bad customer experience. We have built chatbots that handle thousands of conversations daily across e-commerce, enterprise SaaS, and healthcare. We know which LLM to use for different conversation types, how to structure knowledge bases for accurate retrieval, and how to build escalation logic that makes human agents more productive instead of just dumping them into a conversation with no context. ## Our Tech Stack We work across the AI chatbot ecosystem and integrate with the tools your team already uses. LangChain LangGraph Python TypeScript Next.js FastAPI OpenAI Anthropic Claude AWS Bedrock Pinecone Qdrant LangFuse Slack API WhatsApp Business API Twilio Intercom Zendesk ## How We Work A straightforward process from first call to production deployment. Step 1 ### Discovery Call We start with a 30-minute technical conversation to understand your data, your users, and your constraints. No sales pitch. We dig into what you have tried, what failed, and what success looks like. Step 2 ### Architecture Proposal Within a week, we deliver a detailed technical proposal: system architecture, technology choices with rationale, estimated timeline, and cost breakdown. You will know exactly what we plan to build and why. Step 3 ### Build & Ship We build iteratively with weekly demos. You see working software from week one, not slide decks. Every PR is reviewed, every decision is documented, and we transfer knowledge continuously so your team can maintain what we build. ## Frequently Asked Questions ### ### ### ### ### ### ### ## Ready to Build a Chatbot That Actually Works? Tell us about your chatbot project and we will respond within 24 hours with an initial assessment. Whether you need customer support, sales, or internal knowledge, we build chatbots that deliver. Free 30-minute discovery call Chatbot architecture proposal within one week Working prototype in the first sprint ### Get a Free Assessment Describe your chatbot needs and we'll assess how AI can improve your customer experience. ## Structured data ```json {"@context":"https://schema.org","@type":"Organization","@id":"https://vindler.solutions/#organization","name":"Vindler Solutions","alternateName":"Vindler","legalName":"Vindler Desenvolvimento de Sistemas de IA Ltda - EPP","url":"https://vindler.solutions","logo":"https://vindler.solutions/logo.png","description":"Vindler is a global AI engineering consultancy specializing in custom AI solutions, LLM integration, and enterprise-grade AI systems.","foundingDate":"2025-06-02","foundingLocation":{"@type":"Place","name":"São Paulo, Brazil"},"taxID":"61.102.920/0001-05","email":"contact@vindler.solutions","telephone":"+1-646-846-5339","numberOfEmployees":{"@type":"QuantitativeValue","minValue":1,"maxValue":10},"areaServed":[{"@type":"Country","name":"United States"},{"@type":"Place","name":"Europe"},{"@type":"Country","name":"Brazil"}],"knowsAbout":["Multi-agent systems","Retrieval-augmented generation","Voice AI agents","LangChain","LangGraph","LiveKit","AWS Bedrock","Model Context Protocol","LLM observability and evaluation"],"founder":{"@type":"Person","@id":"https://vindler.solutions/about#carlos-dutra","name":"Carlos Dutra","jobTitle":"Founder and CEO","url":"https://vindler.solutions/about","sameAs":["https://www.linkedin.com/in/carlosdutra","https://github.com/cdutr"]},"contactPoint":{"@type":"ContactPoint","contactType":"Customer Support","email":"contact@vindler.solutions","telephone":"+1-646-846-5339","url":"https://vindler.solutions/contact","availableLanguage":["English","Portuguese","Spanish"]},"address":[{"@type":"PostalAddress","addressLocality":"São Paulo","addressRegion":"SP","addressCountry":"BR","streetAddress":"Av. 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They break whenever a user phrases something differently than expected. AI chatbots use large language models to understand intent, maintain conversation context, and generate natural responses. Combined with RAG (retrieval-augmented generation), they can answer questions from your knowledge base without being explicitly programmed for every possible query. The result is a chatbot that handles the long tail of customer questions, not just the top 20 FAQs."}},{"@type":"Question","name":"Can you integrate a chatbot with our existing CRM and support tools?","acceptedAnswer":{"@type":"Answer","text":"Yes. We integrate with Salesforce, HubSpot, Zendesk, Intercom, Freshdesk, and custom CRMs. The chatbot can look up customer information, create and update tickets, log conversations, and trigger workflows in your existing tools. We use your CRM's API and handle authentication, rate limiting, and error cases so the chatbot operates as a natural extension of your support stack."}},{"@type":"Question","name":"How do you handle chatbot escalation to human agents?","acceptedAnswer":{"@type":"Answer","text":"We implement intelligent escalation based on multiple signals: conversation sentiment (the user is getting frustrated), query complexity (the question requires judgment, not just information), confidence score (the chatbot is not sure about its answer), and explicit request (the user asks for a human). When escalating, we pass the full conversation history and a summary of what the chatbot has already tried, so the human agent picks up seamlessly without asking the customer to repeat themselves."}},{"@type":"Question","name":"What does chatbot accuracy look like? Can we expect 100%?","acceptedAnswer":{"@type":"Answer","text":"No AI system achieves 100% accuracy, and anyone who promises that is lying. Our production chatbots typically achieve 85-95% accuracy on in-domain questions, measured against human-annotated ground truth. The key is building systems that know what they do not know: a chatbot that confidently gives a wrong answer is worse than one that says 'I am not sure, let me connect you with a team member.' We implement confidence scoring and graceful fallbacks."}},{"@type":"Question","name":"How do you keep the chatbot's knowledge up to date?","acceptedAnswer":{"@type":"Answer","text":"We build automated ingestion pipelines that sync with your knowledge sources. When a support article is updated in Zendesk, a product page changes, or a new policy document is published, the chatbot's knowledge base updates automatically. We implement change detection so only modified documents are re-processed, keeping ingestion fast and cost-efficient. We also monitor retrieval quality over time and alert when the chatbot starts answering questions with outdated information."}},{"@type":"Question","name":"Can the chatbot take actions like processing refunds or updating accounts?","acceptedAnswer":{"@type":"Answer","text":"Yes. We build chatbots with tool use capabilities that can execute actions through your APIs. The chatbot can process refunds, update shipping addresses, cancel subscriptions, reset passwords, and any other action your API supports. Every action requires proper authentication and validation. We implement confirmation steps for destructive actions (the chatbot asks 'Are you sure you want to cancel?' before executing) and audit logging for compliance."}},{"@type":"Question","name":"How much does a custom AI chatbot cost to build and run?","acceptedAnswer":{"@type":"Answer","text":"Build cost depends on complexity: a knowledge-base chatbot with RAG takes 4-6 weeks, while a full omnichannel support bot with CRM integration and action capabilities takes 8-12 weeks. Running costs depend on conversation volume and model selection. We optimize costs through model routing (simple queries use cheaper models), caching (repeated questions skip LLM inference), and efficient retrieval (minimizing the amount of context sent to the LLM). Typical running costs range from $0.02-0.10 per conversation."}}]} ```