Governed enterprise AI platform
One platform boundary. Many specialist AI experiences.
Publish assistants, connect approved data and tools, and orchestrate workflows while identity, knowledge, policy, cost, and evidence remain governed.
Product system
Choose the outcome. Keep the governance model.
Each product path can evolve into its own specialist without copying AI logic into the website or rebuilding the presentation layer.
01 · Live
AI Knowledge Publisher
Turn approved documents and product knowledge into a versioned assistant with citations, sessions, history, and feedback.
Explore Publisher02 · Data
Structured Data Agents
Create bounded analysis experiences for spreadsheets, CSV files, and governed business data without hiding the source or method.
See the data path03 · Service
Customer Support
Give approved answers, draft useful responses, and escalate honestly when an action or answer is outside the available boundary.
See the support path04 · Embedded
AI Token Guard Support Page
Open the Django-published product support assistant inside any website page, keeping the customer on the site while Django owns the experience.
Open product support05 · Integration
Enterprise Data Gateway
Connect databases, APIs, and files through explicit read, permission, policy, and audit controls before AI can use them.
See enterprise data06 · Delivery
Codex Factory
Turn a request into a governed engineering workflow with planning, implementation, QA, security, evidence, and approval gates.
See the delivery path07 · Tools
Enterprise AI / MCP
Connect tools and knowledge with scoped permissions, human approval for consequential actions, and auditable outcomes.
See the MCP pathReference architecture
A visible boundary from experience to execution.
Experience layer
WordPress presents product stories, configuration, consent-aware analytics, and approved embedded experiences.
Publishing and access
Django publishes versioned assistants, manages public sessions, and exposes approved chat, history, feedback, and citation contracts.
Governed runtime
Knowledge, models, tools, identity, policies, limits, evaluation, monitoring, and human approvals remain behind the runtime boundary.
Built for production questions
Governance is part of the product, not a slide at the end.
Every implementation begins by defining what the system may know, what it may do, who may use it, how quality is measured, and how failure is handled.
- Identity and access boundaries
- Approved knowledge and citation behavior
- Tool permissions and human approval gates
- Quality, latency, usage, and cost observability
- Versioning, rollback, and audit evidence
- Operator runbooks and safe failure modes
Start with the boundary
Map your first governed AI workflow.
Bring one high-value workflow, its data sources, its users, and its constraints. We will turn it into a practical delivery plan.