Solutions · Build and secure
Secure AI Platform
Upmark Labs builds the layer around the AI you run: gateways and model routing, usage and cost monitoring, agent tracing and evaluation, security reviews and controls, and governance registers and policies. It suits technical, security, legal and risk teams taking AI into production. Every build has a written scope and fixed price: order online, or talk to an expert.
Which problems does a secure AI platform solve?
Most products in this area work on AI you already run or are about to launch. They route its traffic, watch what it does, test its answers, protect it, or keep a record of who approved it and why.
- Teams call AI providers with their own keys, with no shared limits or logs.
- AI spend grows and nobody can say which workflow, team or model is behind it.
- An agent fails or gives a wrong answer, and there is no trace of the steps and tool calls that led there.
- Prompt and model changes go live without a test set to show whether answers got better or worse.
- Agents can reach more data and tools than their job needs, and AI apps are open to prompt injection or sensitive data leaving through prompts.
- Nobody holds a current list of the AI tools and models in use, who owns each one, what risk it carries and which policy applies.
What do we build to run, secure and govern AI?
The work falls into infrastructure, security and governance. Infrastructure builds cover the platform your AI workflows and agents run on, such as a gateway, model routing, prompt management, memory and orchestration, plus tracing, monitoring and evaluation. Each reports usage, latency and cost per workflow. Security builds review, control and monitor your AI applications and agents. Governance builds give leadership, legal, risk and IT teams a central register of AI systems, owners and risk levels, with policy checks mapped to your internal rules.
Infrastructure builds use a vendor-neutral design and keep their configuration in version control. Security builds rank findings by severity with remediation guidance and run against your agreed scope only. Security and governance builds support your own review and compliance process; they do not certify security or compliance.
- AI Gateway: routes all AI requests through one gateway with keys, limits and logs.
- AI Agent Observability: traces every agent step, tool call and outcome for debugging.
- AI Evaluation System: builds test sets and automated evaluations for your AI features.
- AI Prompt Injection Defense: adds input and output defences against prompt injection in your AI apps.
- AI Agent Permission System: defines and enforces what each AI agent may access and do.
- AI Governance System: sets up AI policies, ownership, approvals and reviews across the organization.
How does a project in this area run?
Every product page shows its Scope Card before you pay: the systems or AI applications in scope, the controls, policies or monitors configured, the testing, the rounds of changes and the fixed delivery time shown on each product. Anything outside it is quoted in writing as a Change Quote, and that work starts only once you accept it.
Running costs are paid in your own accounts, not through us. For most builds here that means hosting, logging and monitoring tools, plus AI model usage where a build calls a model. Each product page gives an estimate.
- Scope: written on the product page before purchase.
- Intake: within 72 hours of the order you send the list of AI applications and systems in scope, your existing policies, cloud or log access as needed, written authorization for any testing, and a named IT or security owner.
- Build: we configure the workflow, prompts, guardrails, controls or monitors in your environment and connect the agreed systems.
- Test and launch: you receive a control test log or findings report, then two rounds of changes, an admin guide and a recorded handoff session.
- Support: 30 days of post-launch support, then an optional monthly maintenance plan.
How do you choose the right product?
Start from the gap you can name today. If you cannot see what your AI is doing or costing, start with visibility. If you can see it but cannot say whether it is safe, start with a security review. If leadership, legal or risk teams need a record of what is in use and who owns it, start with governance.
If two products look close, or your setup has more systems than a Scope Card covers, talk to an expert before you order.
- Spend you cannot explain by workflow or team: AI Cost Monitoring or AI Usage Monitoring Platform. Tasks sent to one model whatever the job: AI Model Router.
- Agents in production that fail or drift: AI Agent Monitoring Platform for health, failures and drift, with AI Agent Observability for step-by-step traces.
- Security questions before or after launch: AI Security Audit for applications and agents, AI Security Review Assistant for designs and pull requests.
- Sensitive data or over-broad access: AI Data Leakage Monitor, AI Access Control System or AI Agent Security Layer.
- No shared rules or register for AI: AI Usage Policy System, AI Model Inventory or AI Risk Assessment.
- Several of these at once: the AI Infrastructure Foundation and AI Security & Governance Stack bundles deliver related products as one project with one intake.
Secure AI Platform in the Store
AI Agent Security Layer
Adds policy enforcement, tool limits and logging around your AI agents.
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
AI Compliance Knowledge System
Organizes regulations and internal controls into a searchable knowledge base.
AI Security Monitoring
Centralizes security events from AI systems with alerting.
Browse the Store by category: AI Infrastructure AI Security AI Governance & Compliance
Questions buyers ask
Does a security or governance build certify that our AI is secure or compliant?
No. Security builds review and monitor the AI applications, agents and systems in your agreed scope, and rank findings by severity with remediation guidance. Governance builds support your own compliance process with a register, policy checks and records. Neither certifies security or regulatory compliance, and no guarantee is implied. The Compliance Documentation Pack can add written records of data flows, controls and processing for your own compliance reviews.
Will an infrastructure build tie us to one AI provider?
No. Infrastructure builds use a vendor-neutral design that works with multiple model providers, such as OpenAI, Google Gemini, AWS Bedrock and Azure OpenAI, and typically connect to tools such as Langfuse and Grafana. Configuration is kept in version control and every provider account stays in your name. AI Model Router can then send each task to the most suitable model by cost, speed and quality.
What do you need from us before any security testing starts?
Written authorization for any testing, the list of AI applications and systems in scope, your existing policies, cloud or log access as needed, a named IT or security owner, and one decision-maker for approvals. All of it is due within 72 hours of the order. Testing runs against the agreed scope only.
What do we pay each month after launch?
The build price is one-time. Running costs are paid in your own accounts, not through us: for most products here that means hosting, logging and monitoring tools, plus AI model usage where a build calls a model. AI usage during the build and testing is also billed to your accounts. Each product page shows an estimate, and a monthly maintenance plan is optional.
Can you add guardrails or logging to an agent we already run?
Yes. AI Agent Logging adds structured logging to existing AI workflows, and AI Agent Security Layer adds policy enforcement, tool limits and logging around your AI agents. If the agent is an Upmark Labs product, the Prompt Injection Guardrails Add-on adds input and output guardrails to it, and the Audit Logging Add-on adds tamper-evident logs of actions and changes.
What do the governance products give leadership, legal and risk teams?
A shared, current record of AI in the business. AI Model Inventory keeps a register of models, vendors, owners and uses. AI Risk Assessment runs a structured risk assessment for each AI use case. AI Audit Trail records AI inputs, outputs and decisions for later review. They connect to tools such as Google Workspace, Microsoft 365, Jira, Notion, Confluence and Slack.
Bring us the AI you run today and the question you cannot yet answer about it.
Describe the models, agents and tools in use and what your security or risk owner needs to see, and an expert will match you to a product or scope a custom build.