Enterprise AI Gateway
Unified Multi-LLM Management Platform
An enterprise platform that unifies OpenAI, Anthropic, Google and more behind one gateway, bringing scattered department-level AI usage under central control. Admins allocate budget once instead of managing each model's billing separately, and users draw on that allocation across models without signing up or paying individually. Designed by SUVsoft, a security company, it ships with PII and credential detection, audit logs, and cost savings from repeat-query caching as defaults, not add-ons.
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Every team ends up on a different AI, and nobody can control it
Once generative AI spreads department by department, teams end up on different tools within months — engineering on Claude, marketing on a ChatGPT Plus subscription billed to a company card, another team on Gemini. The company has no way to see who is spending what on which model, or what information was shared. There is nothing to stop personal data or internal credentials from being pasted into a prompt, and if something goes wrong, there is no audit trail to trace it.
Route every model through one gateway and you can govern it
Instead of letting each team use LLMs independently, the fix is routing every request through a single gateway. Once every request passes through one point, that point can detect personal data, cap usage and cost, and log who asked what and when. Instead of paying for and managing each model's billing separately, admins allocate budget once for the whole organization. Users simply use whatever model they need within their allocation, with nothing to sign up for or track; the organization gets central control.
SUVsoft's Enterprise AI Gateway
SUVsoft has been building security products since 2011 — mobile security (AV-TEST certified in Germany) and email security (Mail AI). The PII and credential detection in this gateway isn't new engineering; it's the same detection logic carried over from Mail AI. Unlike generic AI gateways that start with routing and cost control and bolt security on afterward, we started with security and built AI routing on top of it. Chat history and audit logs stay on your infrastructure.
Key Features
One Budget, Zero Admin Overhead
Admins allocate a budget once, and users draw on it across every model without signing up or paying for each one individually. No more juggling separate billing and limits per provider.
Multi-LLM Integration
Choose from OpenAI, Anthropic, Google, Perplexity and more in a single console, and compare up to three models side by side. Existing developer tools like Claude Code and the openai SDK connect as-is via an OpenAI-compatible API.
48% Cost Cut, Measured
Repeated instructions are reused via provider-side caching, cutting actual token consumption. In a measured test, calling the same long prompt a second time hit the cache and cut cost by 48%. Savings are shown directly on the dashboard.
Detection Built by a Security Company
Detects not just personal data like resident registration and account numbers, but credentials such as AWS keys and documents with passwords written in them. Files that can't be partially masked, like Excel or CSV, are blocked outright.
Searchable Audit Log + Approved Connectors
Every request is full-text searchable by who asked what and when. External MCP tools only reach company-wide chat after admin approval.
Specifications
Model integration
- Supported providers
- OpenAI, Anthropic, Google, Perplexity and more, expanding regularly
- Integration method
- OpenAI-compatible API — existing tools like Claude Code and the openai SDK connect as-is
- Side-by-side comparison
- Compare responses from up to 3 models at once
- Cost savings
- Repeated instructions reused via provider caching (48% cut in a measured case)
Governance & cost
- Org structure
- Company > Department > User, three-tier permissions
- Usage limits
- Per-minute and daily request caps, daily/monthly budgets, per-IP quotas
- App-specific keys
- Per-key allowed model, monthly budget, allowed IP, and expiry; violations are blocked instantly
- Credits
- Admin-issued personal credits cover usage past budget (direct card billing in development)
Security & audit
- Detection policy
- Detects PII (resident ID, account, phone), credentials (AWS keys, passwords written in text), and confidential-tier documents, then shows/masks/blocks; table files are blocked outright
- Key storage
- Provider API keys and connection info are stored encrypted
- Audit log
- Full-text searchable, including block reasons
- Access control
- 2FA (standard OTP-app compatible), login IP allowlist, session expiry; external MCP tools reach chat only after admin approval
How Deployment Works
- 1
Requirements review
Identify which departments use which models, how much, and what PII policy is needed.
- 2
Infrastructure setup
Install the gateway on your own servers — on-premise or a private cloud.
- 3
Model & policy integration
Register the LLM provider keys you'll use, and set per-department budgets and PII policies.
- 4
PoC
Verify cost, accuracy, and PII detection behave as intended against real business queries.
- 5
Company-wide rollout
Issue accounts and move to steady-state operation using the audit log and cost dashboard.
In Practice
“Call multiple model APIs safely from internal scripts”
Keep using existing tools like Claude Code and the openai SDK, while an app-specific key limits allowed models, budget, and IP. Violating requests are rejected instantly.
“Control department AI spend with budgets”
Set a monthly budget per department; overage falls to personal credits instead of unrestrained spending.
“Block personal data and credentials accidentally pasted into a prompt”
Not just resident IDs or account numbers — strings that look like AWS keys are also blocked or masked automatically before reaching an external model.
“Use AI usage history in incident investigations”
Full-text search who asked which model what and when, including blocked requests and the reason, to trace root cause when something goes wrong.
FAQ
On-premise sLLM is a single dedicated model running in a network isolated from the internet. Enterprise AI Gateway is a management layer above that — it connects and governs multiple models together, external APIs and on-premise models alike. You can register your sLLM as one of the models inside the gateway and use both together.
If the selected model is an external API, that request is sent to that provider. Before it leaves, it passes through the PII detection policy, so identifiers like resident registration or account numbers can be blocked or masked. Work that must never leave the network can be routed to an on-premise model instead.
We built mobile security (AV-TEST certified) and email security (Mail AI) before this. The PII and credential detection here is that same experience carried over — a different build order from products that add a privacy filter on top of a routing tool after the fact.
The demo page isn't ready yet. Contact us and we'll walk you through it in the meantime, and we plan to connect a live demo link on this page once it's ready.
Repeated system instructions and project directives are reused via provider caching. In a measured test, a second call of the same long prompt hit the cache and cut cost by 48%. That figure isn't guaranteed every time, but organizations with more repeat queries see bigger savings.
Korean-specific patterns — resident registration numbers, card numbers, account numbers, phone numbers, emails, addresses — are detected in both prompts and attachments. Credentials such as AWS keys or documents with passwords written in them, and confidential-tier documents, are also classified. On detection, the organization's policy applies: block, mask, or allow — and files that can't be partially masked, like Excel or CSV, are blocked outright.
Yes. The gateway exposes an OpenAI-compatible API, so existing tools such as Claude Code or the openai SDK connect by simply changing the address and key. Requests through this path follow the same usage limits, PII policy, and audit logging as chat.
It's deployed directly on your own infrastructure — on-premise or a private cloud. It's container-based, so migration and backup are straightforward, and chat history and audit logs stay in your environment.
An admin can issue personal credits so work continues. Direct card billing is in development.
No. Once an admin allocates a budget, users can draw on any model — OpenAI, Anthropic, Google and more — without signing up or paying individually. Everyone uses their allocation freely, and admins see total usage and cost from a single dashboard.
It depends on infrastructure readiness and how many models and policies need to be connected. We recommend starting with a small PoC in one department, confirming usage, cost, and PII detection behave as intended, then expanding in stages.
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