Real enterprise use cases and specialized architectures with security, governance, and native integration.
Design patterns, context engineering, and modern protocols applied to artificial intelligence solutions.
Folder-based architecture (.ai & .cursor) for contextualized wikis (.ai/wiki), ADR decision records, and persona engineering with defined boundaries.
Native MCP support as client and server (JSON-RPC 2.0 specialist sub-agent) integrable into Claude Desktop and partner platforms.
Fernet (AES-128) encryption for client-specific API keys, local vector store in ChromaDB, and robust guardrails against data leaks and prompt injection.
Computer vision support, real-time audio transcription with Whisper model, Server-Sent Events (SSE) streaming, and Document-in-the-Loop ephemeral RAG.
Explore our specialized topics and see how Megamil applies artificial intelligence in practice.
Vision model support for processing base64/URL images up to 5MB (screenshots, document photos) for text analysis, data extraction, and visual interpretation.
AI photo processing to evaluate wear, defects, or dirtiness on returned equipment, automatically suggesting destination (maintenance, cleaning, reuse, or disposal).
Automated photographic validation of Point-of-Sale installations, damaged material collection, and anti-fraud digital receipt audits.
Semantic search in local vector DB (ChromaDB) querying cosine similarity across segmented document chunks (global, corporate, project).
Direct attachment of PDFs, Excel spreadsheets, text, JSON, and Markdown in chat. In-memory chunking and real-time semantic search with automatic session cleanup.
RAG connected to relational DBs and legacy systems for direct natural language queries to retrieve stock positions and history without SQL.
Industry-standard MCP specification enabling AI to invoke external REST APIs (ERPs/CRMs), structured SQL queries, or real-time web searches.
Exposing internal project intelligence via JSON-RPC 2.0 standardized interface as an external specialist sub-agent.
Optimized conversation history: short temporary cache sessions in Redis with TTL, and persistent corporate sessions in relational DB.
Real-time response delivery via Server-Sent Events (SSE), streaming text incrementally while emitting internal AI tool execution events.
Multichannel virtual assistant (Web, WhatsApp, Apps) for self-service, real-time order tracking, availability checks, and natural language requests.
Orchestration of AI personas with strict role boundaries (Architecture, Dev, Business Analysis, QA, Release, Migration).
Embedded wiki (.ai/wiki) centralizing architecture docs, data schemas, business rules, and ADR decision records for LLMs.
Modular rules ecosystem (.cursor/skills & .cursor/rules) extending AI capabilities for code governance, permission audits, and Git workflows.
Centralized integration across OpenAI, Gemini, Anthropic, and local Ollama models with runtime switching without redeployment.
Hierarchical prompt stacking across 5 levels (global rules, org rules, project rules, session params, instant message context).
Multi-tenancy security with encrypted API keys (AES-128 Fernet) per client/project and strict logical session isolation.
LLM request centralization with Prompt Injection defense, sensitive data privacy validation, and semantic embedding cache for cost/latency optimization.
Whisper model integration for multi-format audio transcription (WebM, MP3, WAV), injecting text into LLM context prior to processing.
Natural language query conversion into dynamic tables, KPIs, and charts with predictive summarization and bottleneck detection.
NLP semantic reading of support tickets and customer feedback, identifying detractors, urgency level, and SLA breach risks.
Intelligence engine reading active UI context to trigger anomaly alerts (nearing expiration dates, atypical variations) and suggest actions.
Private assistant trained on org guidelines to support daily staff operations and software engineering productivity.
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Megamil Serviços de Informática LTDA