What does an enterprise chatbot with RAG do differently?
A chatbot with RAG (Retrieval-Augmented Generation) answers from your company's data (manuals, contracts, catalogues, 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, website or internal systems. The result is customer service and support that scale without growing the team.
What are AI agents, and when should you use LangGraph?
AI agents are systems that plan, decide and execute multi-step tasks using tools: querying an ERP, filling a form, opening a ticket. We orchestrate those flows with frameworks such as LangGraph and multi-agent patterns, using tool calling, MCP (Model Context Protocol, now an open standard under the Linux Foundation) and A2A to connect the agent to your systems safely, always with human approval (human-in-the-loop) on critical steps.
Typical cases: email triage and reply, tax-document analysis, data reconciliation between systems, internal copilots for sales and support teams. AI automation goes beyond traditional RPA: the agent handles exceptions that would break a fixed-rule bot.
AI to understand and document legacy systems
The AI use that saves the most money in Brazilian companies is not the chatbot: it is understanding old code. Language models read PHP, Java, .NET, classic ASP and COBOL and produce documentation, module maps, dependencies and implicit business rules, the knowledge that left the company with the original developer.
JBKR combines the AI with the experience of someone who has maintained legacy systems since 2006: every model output is reviewed, tested against the system's real behaviour and recorded. That is human-in-the-loop in practice, and it is part of the legacy modernization assessment.
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, faithfulness and 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 inappropriate content, and fallback policies make sure the system hands off to a human when confidence is low.
Governance, security and privacy in AI projects
AI governance defines who may use what, with which data and with what traceability. We implement classification and masking of personal data, role-based access control, interaction audit logs and retention policies, aligned with Brazil's LGPD, the AI bill under discussion in Brazil (PL 2338/2023) and international practice (NIST AI RMF, EU AI Act). Our principles are published on the Responsible AI page.
On the security layer: protection against prompt injection, isolation of the credentials used by agent tools, and a deliberate choice between commercial APIs (OpenAI, Anthropic, Google) and private models when data cannot leave the premises.
Observability and telemetry: how do you know the AI is working?
LLMOps means operating AI the way you operate critical software. We instrument every solution with quality telemetry (correct-answer rate, escalations to humans), cost (tokens per conversation, cost per resolution) and performance (latency), with dashboards and alerts.
That closes the loop: production data feeds the evals, which guide continuous improvements to prompts, retrieval and models, with evidence rather than 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 from YOUR data, citing sources, instead of making things up. It is worth it whenever answers must reflect internal knowledge (manuals, contracts, policies, catalogues) without training your own model.
Do you build AI agents with LangGraph?
Yes. We use LangGraph and multi-agent orchestration for flows 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 make sure the chatbot does not invent answers?
With four layers: RAG with hybrid search (vector + BM25) to anchor 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.
What about privacy law? Is my data safe with AI?
AI governance is part of the project: classification and masking of personal data, role-based access control, choice between commercial APIs and private models, audit logs and retention policies aligned with Brazil's LGPD. Client data never trains models, as stated on our Responsible AI page.
Can AI help understand a legacy system?
Yes, and it is one of the most useful applications: language models read PHP, Java, .NET or COBOL code and produce documentation, module maps and business rules, always reviewed by someone who understands the system. JBKR offers this as part of the legacy-system assessment.
How much does an AI project cost?
Projects start with a lean proof of concept, typically under the On-Demand plan (from BRL 250/hour), with scope and success metrics defined up front. Evolution to production and ongoing support fit the Care (BRL 1,125/month) and Expert (BRL 2,050/month) plans.
