What does an enterprise chatbot with RAG do differently?
A RAG (Retrieval-Augmented Generation) chatbot answers from your company's data — manuals, contracts, catalogs, tickets — and cites its sources. The technique combines embeddings and vector databases with hybrid search (vector + BM25), which drastically reduces hallucinations compared with a generic model.
We deliver the full cycle with context engineering — the evolution of prompt engineering that combines retrieval, memory and tool orchestration: document ingestion and cleaning, chunking strategy, vector indexing, reranking, agentic RAG and integration with WhatsApp, your website or internal systems. The result is customer service and support that scale without growing headcount.
What are AI agents, and when should you use LangGraph?
AI agents plan, decide and execute multi-step tasks using tools — querying an ERP, filling a form, opening a ticket. We orchestrate these flows with frameworks like LangGraph and multi-agent patterns, using tool calling, MCP (Model Context Protocol, now an open Linux Foundation standard) and A2A to connect agents to your systems safely — always with human-in-the-loop approval on critical steps.
Typical cases: email triage and reply, fiscal document analysis, data reconciliation between systems, internal copilots for sales and support teams. AI automation goes beyond traditional RPA: agents handle the exceptions that would break a fixed-rules robot.
How we guarantee quality: guardrails, evals and evaluation harnesses
AI in production without evaluation is risk. Every project ships with an evaluation harness (evals): test sets with expected answers and faithfulness/coverage metrics, run on every prompt, model or knowledge-base change — the equivalent of automated tests in traditional software.
Input and output guardrails filter off-topic requests, personal data and improper content, and fallback policies make sure the system hands over to a human when confidence is low.
Governance, security and privacy in AI projects
AI governance defines who can use what, with which data and with what traceability. We implement personal-data classification and masking, role-based access control, interaction audit logs and retention policies — aligned with Brazil's LGPD and international frameworks (NIST AI RMF, EU AI Act).
On the security layer: prompt-injection protection, credential isolation for agent tools, and a deliberate choice between commercial APIs (OpenAI, Anthropic, Google) and private models when data cannot leave your infrastructure.
Observability and telemetry: how do you know the AI is working?
LLMOps means operating AI like critical software. We instrument every solution with quality telemetry (correct-answer rate, human escalations), cost telemetry (tokens per conversation, cost per resolution) and performance telemetry (latency), with dashboards and alerts.
That closes the loop: production data feeds the evals, which drive continuous improvements to prompts, retrieval and models — with evidence, not guesswork.
Frequently asked questions
What is RAG and when is it worth using?
RAG (Retrieval-Augmented Generation) connects a language model to your company's documents and systems: the chatbot answers based on YOUR data, citing sources, instead of making things up. It pays off whenever answers must reflect internal knowledge — manuals, contracts, policies, catalogs — without training a custom model.
Do you build AI agents with LangGraph?
Yes. We use LangGraph and multi-agent orchestration for workflows that require steps, decisions and tool use (function calling, MCP): ticket triage, document analysis, ERP integrations. Every agent ships with guardrails, an evaluation harness and telemetry from day one.
How do you keep the chatbot from making up answers?
Four layers: RAG with hybrid search (vector + BM25) to ground answers in real sources; input and output guardrails; an evaluation harness (evals) run on every change; and LLM observability in production, with quality, cost and latency telemetry.
Is my data safe when using AI? What about privacy laws?
AI governance is part of every project: personal-data classification and masking, role-based access control, the choice between commercial APIs and private models, audit logging and retention policies aligned with Brazil's LGPD and international practice (NIST AI RMF, EU AI Act).
How much does an AI project cost?
Projects start with a lean proof of concept, typically under the On-Demand plan (from R$250/hour), with scope and success metrics defined up front. Evolution to production and ongoing operation fit the Care (R$1,125/month) and Expert (R$2,050/month) plans.
