Agentic AI Inside Your Infrastructure

Security and data sovereignty are foundational to Kaji's design. First, Kaji and the Shakudo OS deploy directly within your own cloud infrastructure (such as an AWS VPC or Azure). Your data does not leave your environment unless you explicitly proxy it to an external LLM.

When you do use external models (like OpenAI or Anthropic), it is done with strict zero-retention and zero-training contractual guarantees. Additionally, Shakudo's AI Gateway includes deterministic heuristics that can detect and strip PII or PHI before a payload ever reaches an external LLM. It blocks the request, logs it in an audit trail, and alerts the team, ensuring that sensitive data is protected even if a user accidentally includes it in a prompt.

It is completely valid to be wary of autonomous agents running unchecked in an enterprise environment. Kaji mitigates this through a blend of Role-Based Access Control (RBAC), context-driven development, and agentic memory:

• Approval Workflows (RBAC): Kaji ingests your organizational chart. If a user asks Kaji to execute a high-risk action—like restarting a production Neo4j database—Kaji knows if that user lacks the appropriate permissions and will automatically route an approval request to the authorized administrator.
• Context-Driven Development: Before writing code, Kaji generates a comprehensive specification document detailing requirements, tasks, and success criteria. It uses this spec as a deterministic anchor to stay on track.
• Graph-Based Memory: Kaji utilizes a time-series memory framework to remember established company workflows, past successes, and previous errors, severely reducing the likelihood of repeating mistakes or hallucinating incorrect infrastructure commands.

Kaji addresses token bloat through cost visibility and intelligent model orchestration. Token costs are a direct pass-through with zero markup, and Kaji provides real-time visibility into the exact token count and dollar cost of every action it takes, eliminating surprise bills.

More importantly, Kaji utilizes an orchestrator with sub-agents to optimize costs. While it uses expensive frontier models (like Claude 3 Opus) for complex reasoning and planning, it automatically delegates simpler execution tasks—like front-end changes or reading basic logs—to highly efficient, low-cost models (like Claude Haiku). Shakudo is also introducing local hosting for powerful open-source mega-models (like GLM5 and Qwen), which will further drive down token costs while maintaining data privacy.

Kaji does not require you to adopt a new communication silo. It integrates directly via adapters into your existing messaging platforms, including Microsoft Teams, Slack, and Mattermost.

On the infrastructure side, Kaji sits on top of the Shakudo OS, which comes pre-integrated with over 250 industry-standard tools and databases (e.g., Postgres, Neo4j, vector databases, Kafka). Because it operates within your environment, Kaji is already aware of your internal stack. It can spin up microservices, configure database connections, and orchestrate components without needing you to manually string together individual API endpoints or MCP servers.

Kaji acts as a highly intelligent, full-stack engineer and operations assistant. Teams use it for:

• Rapid Application Prototyping & Build-out: Generating fully functional internal dashboards, complete with synthetic or live data connections, in a matter of hours.
• Intelligent RPA & Automations: Setting up backend workflows, such as connecting OCR tools to extract data from financial 10-Ks, or triggering automated Twilio phone calls and Mailgun emails based on logic (e.g., contacting a patient who submitted a low NPS score).
• Business Operations: Assisting with contract reviews, creating internal document management systems with automated approval routing (e.g., via SharePoint), and executing routine IT or HR tasks.