Operational intelligence for technical teams
Sperry Tecnologia — July 2026
Modern IT operations depend on servers, networks, identity, applications, observability, security, cloud services and automation. The knowledge required to operate this environment is scattered across people, tickets, logs, runbooks, documents and tools that rarely share the same context.
debuga.ai was designed to reduce this fragmentation. The platform provides a specialized conversational interface connected to configurable AI providers, technical capabilities, persistent history and administrative controls. The goal is not to be another generic chatbot, but to organize operational reasoning, preserve useful metadata and support investigation and documentation workflows.
The reference architecture combines cloud providers with optional local inference, containerized deployment, PostgreSQL, S3/MinIO storage, real-time streaming, billing and white-label configuration.
A single incident may require correlation across DNS, certificates, HTTP, logs, firewalls, applications, identity and monitoring. Constant tool switching increases diagnostic time and weakens documentation quality.
Procedures are distributed across documents, messages and individual memory. Teams repeat investigations and become dependent on a small number of specialists.
General-purpose models do not automatically understand the organization's topology, policies, plan limits or evidence. They also do not provide centralized provider, cost, branding, history and audit controls by default.
Some organizations prefer cloud models; others need local processing; many require a hybrid strategy. A rigid architecture limits these choices.
debuga.ai combines:
- technical specialization for infrastructure, security and DevOps;
- conversational interaction with streaming and history;
- policy-controlled technical capabilities;
- multi-provider routing and fallback;
- optional local inference;
- provider and usage observability;
- white-label identity and deployment;
- PostgreSQL, S3-compatible storage and containerized operation.
The product line includes chat, conversation continuity, configurable providers, administration, plans, billing, multimodal workflows, technical sources and evolving knowledge reuse. Availability depends on plan, provider, feature flags and deployment.
flowchart TB
U[User]
N[NGINX / HTTPS]
A[React + Express + tRPC]
P[(PostgreSQL)]
S[(MinIO / S3)]
O[Optional Ollama]
C[Cloud providers]
U --> N --> A
A --> P
A --> S
A --> O
A -. fallback .-> C
The stable line uses React, TypeScript, Express, tRPC, Drizzle ORM, PostgreSQL, MinIO/S3, NGINX, Docker Compose and optional Ollama. Stripe may provide billing when enabled.
The platform is provider-agnostic. Operators configure eligible providers, models, priorities, budgets and fallback behavior. Local inference can keep selected processing inside the deployment; cloud providers offer external capabilities and scale.
Model names shown in screenshots are examples, not permanent architecture.
- managed service providers;
- internal IT teams;
- infrastructure, DevOps and SRE;
- SOC/NOC and security operations;
- IT consultancies;
- public-sector and registry-office projects;
- telecom and internet service providers.
The platform complements, rather than replaces, monitoring, SIEM, CMDB and automation systems.
Public plan categories are Free, Starter, Pro and Enterprise. Current prices, limits and payment conditions are published by the application. Enterprise deployments are defined by scope and contract.
White-label projects may configure application and agent identity, visual assets, legal information, support channels, providers, knowledge sources, domain and infrastructure. Not every integration is included by default.
Public principles include externalized secrets, HTTPS/TLS, authentication, authorization, configurable abuse protection, isolated data services, input validation and sanitized operational logging. Retention, at-rest encryption, subprocessors and provider policies depend on the deployment and contract.
The administration console can expose provider, model, task type, status, duration, token volume, routing path, estimated cost and authorized administrative events. Retention is a deployment policy.
| Dimension | debuga.ai approach |
|---|---|
| Focus | Operational and technical workflows |
| Flexibility | Cloud providers and optional local inference |
| Traceability | Provider, model, status and usage metadata |
| Continuity | Persistent conversations and reusable context |
| Deployment | Containers, dedicated database and object storage |
| Branding | White-label identity |
| Governance | Plans, limits, roles and audit metadata |
The roadmap separates stable, configurable, controlled-evolution and planned work. Current directions include routing improvements, contextual knowledge, monitoring and automation connectors, SSO/SAML, MSP capabilities and multimodal expansion.
AI output can be wrong and must be reviewed. Technical tools may only be used against authorized systems. Destructive changes require human approval and rollback planning. Availability depends on infrastructure and third-party providers.
debuga.ai turns distributed technical knowledge into a more organized, traceable and adaptable operational experience. Its hybrid architecture combines cloud providers, optional local inference, technical capabilities, persistence, billing and white label without exposing the proprietary production core.
Yao et al. ReAct: Synergizing Reasoning and Acting in Language Models (2022).