AI Sandbox

A typical chatbot often stops at “conversation”, while enterprise deployment gets stuck on four questions: traceability, accountability, cloud data risk, and whether deployment means rebuilding systems / how to operate long-term. AI Sandbox turns those concerns into a governable delivery:

• Traceable: Cited answers and auditable access—know “who saw what, and did what”.
• Verifiable: Knowledge bases can be regression-tested and continuously improved; it is not just “chatting”, but an acceptance-ready delivery.
• Integratable: No rip-and-replace; OpenAPI/enterprise APIs help existing systems start “understanding human language”.
• Operable: Supports long-term operations (support/security updates) and appliance rebuild flows (export/import).

AI Sandbox is not “a better chat UI”—it is a delivery that makes security, compliance, and accountability explicit:

• Data boundary (data residency): Supports On‑Prem / Private deployment; data stays inside your intranet (offline-capable).
• Governance & audit: Reuse SSO/RBAC; configurable guardrails/policies; cited answers; traceable operations (who asked what, based on which sources).
• Enterprise knowledge base (RAG): Import internal PDFs/Word/Excel and build a maintainable knowledge base (indexing/permissions/regression tests) so teams share the same acceptance-ready answers.
• Contractable risk boundary: For production, define data boundary/retention/audit responsibilities via NDA/DPA and Enterprise Agreement (agreement governs).

In contrast, typical public cloud AI tools are often optimized for individual accounts and one-off chats; enterprises usually need to fill in governance, auditability, and compliance on their own.

Best for high-frequency, standardizable, content-heavy workflows where sensitive data must stay on-prem and access must be auditable—especially when you want to turn documents/knowledge into usable answers, processes, and reports.

Six high-frequency scenarios (presentation version)

• AI customer service: Precise answers, 24/7 automation; often 70–80% of common questions can be resolved automatically; fast go-live via 1‑Line Script, No Site Changes.
• AI hiring decisions (HR): Import job postings and resumes for “requirements × resume × salary” analysis; surface risks, map interview scoring to salary PR ranges, and generate compensation suggestions with decision summaries.
• AI legal assistant (litigation): After uploading case files, it organizes case law/points/elements; supports similar-case comparison tables and draft + risk checklist generation so lawyers can focus on final judgment.
• AI credit review / underwriting: Multisource cross-analysis and early warnings; combine financial statements and transaction context so risks surface early and decisions become consistent.
• AI enablement (existing systems): No rebuild; connect AI to existing systems by importing specs/SOP/permission tables, then execute tasks with natural language—on-prem and secure.
• AI ERP assistant (ERP AI): Import ERP module docs/SOP/specs; AI understands module/form/field relationships so users can ask in natural language and get reports—no system change, no consultant dependency.

It also fits finance analysis and risk review (“financial statements become accurate, traceable, and usable”), internal policy/SOP lookup, audit/compliance, and cross-team knowledge transfer.

The core difference is not “which model is stronger”, but data boundary, governance accountability, and auditability:

• Data boundary (data residency): Cloud AI often implies data egress and cross-border processing; on‑prem/private deployment keeps data inside your intranet (offline-capable), reducing leakage and compliance risk.
• Access control and audits: Cloud AI is often centered on personal accounts and one-off chats; on‑prem AI can reuse SSO/RBAC and keep audit trails (who asked what, when, and based on which sources).
• Verifiability and operability: Cloud tools optimize for “usefulness”; enterprise deployment requires “governable, regression-testable, continuously improvable”.
• Contractable responsibilities: On‑prem delivery makes it easier to define data retention, audit responsibilities, operations, and boundaries in NDA/DPA and Enterprise Agreements (agreement governs).

AI Sandbox is not just “running a model inside the intranet”—it is a governable, auditable, and acceptance-ready delivery (On‑Prem Tamed AI):

• Governable: Reuse SSO/RBAC; configurable guardrails/policies; key flows can be “confirm before execute”.
• Auditable: Cited answers; traceability (who asked what, based on which sources, and what was produced).
• Verifiable: Supports regression question sets and continuous improvement; quality becomes acceptance-ready delivery.
• Integratable: OpenAPI / enterprise APIs connect to existing systems—no rip-and-replace.
• Operable: Export/import rebuild flow (backup/replacement), support and security updates (agreement governs).