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 NetworkNVIDIA Inception ProgramLangChain

    Recognized by Clutch

    Top Clutch AI Consulting Company Brazil 2026Top Clutch AI Company Brazil 2026Top Clutch AI Company São Paulo 2026Top Clutch Chatbot Company Brazil 2026
    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.

    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
    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
    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

    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.

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