Developers are adopting AI models, agents, and services faster than any organisation can track them. Draftt gives every engineering leader a real-time inventory of every AI model across every cloud, with automated policy enforcement, deprecation alerting, and cost visibility.
Engineering teams adopt AI models and services without central oversight, creating ungoverned AI footprints across multiple cloud providers. Deprecated models break production without warning. Cloud providers charge extended support fees for AI models past end of life. And agentic AI is creating a new category of technical debt that traditional governance frameworks were not built to address.
AI models, agents, and services in a typical enterprise stack today. Most adopted without central oversight.
typical AI model lifecycle before provider deprecation. Faster than any other infrastructure technology.
central visibility into which teams are using which models. The default state of AI governance today.
AI discovery and inventory.
Discover and catalog every AI model, agent, and service across your entire estate: PaaS platforms (AWS Bedrock, GCP Vertex AI, Azure OpenAI and Foundry, Anthropic Claude), SaaS AI applications, OpenAI API integrations, and custom deployments. Draftt maintains a continuously updated, real-time inventory showing every model in use, who is using it, which version is running, and what its lifecycle status is.
Policy compliance, cost exposure, and deprecation risk.
Every AI model finding is enriched with business context: which team uses it, what the compliance policy status is, whether the model is approaching or past deprecation, and what extended support costs are accumulating. Draftt surfaces shadow AI usage, duplicate deployments across environments, and API key attribution so every model usage is traceable to a team and application.
Agentic governance workflows
When a model is deprecated, approaching end of life, or violating an organisational policy, Draftt’s AI Agents Hub automatically routes the finding to the right team, creates a ticket with migration guidance, and sends alerts via Slack. For supported AI providers, Draftt generates the migration path to the approved replacement model. Policy is set once, organisation-wide. Engineering teams execute within their own application context.
See every lifecycle event up to 18 months out.
Draftt scans your entire tech stack: cloud infrastructure, runtimes, containers, IaC, open source packages, certificates, managed services, and AI models. The lifecycle timeline plots every end-of-life event, end-of-support date, and breaking change even 18 months ahead, with filters by team, technology, and tag.
Know Exactly What to Fix First, and Why
Draftt’s Prioritization Agent scores every lifecycle event against change analysis, business impact, and engineering impact: CVEs, breaking changes, extended support fees, and the services that depend on the affected component. Engineering teams act on what compounds, not on whatever the vendor flagged most recently.
Agentic Remediation
Draftt’s AI Agents Hub routes lifecycle findings to the owning team with the remediation plan already attached. For supported technologies, autonomous upgrade agents execute Kubernetes cluster upgrades, database version migrations, and runtime updates on your behalf, without manual intervention.
Continuously updated catalog of every model, agent, and AI service across AWS Bedrock, GCP Vertex AI, Azure OpenAI, OpenAI, Anthropic Claude, and custom deployments. Shows version, usage volume, estimated cost, last activity, and lifecycle status.
One inventory for every model your teams are running.
Identifies AI models and services adopted by engineering teams without organisational approval. Surfaces unauthorised model usage before it creates compliance exposure, cost surprises, or data governance risk.
Shadow AI surfaced before it hits an audit.
Flags deprecated models before they impact production. Tracks every AI model against provider deprecation schedules and surfaces migrations to approved replacement versions with full context and cost impact.
Deprecation surprises eliminated. Migrations planned ahead, not in response to incidents.
Cloud providers now charge extended support fees for deprecated AI model versions. Draftt surfaces these emerging costs and projects the financial impact of not migrating, making it easy to build the business case for action.
An emerging FinOps line item for AI. Surfaced before it shows up on the bill.
Define any AI governance policy: approved model lists, prohibited models, version requirements, or data handling rules. Every model in the inventory is evaluated against active policies and compliance status is visible per team and per application.
Policy lives in one place. Compliance status visible per team, per application, across the organisation.
As organisations build in-house AI agents and multi-model orchestration pipelines, a new category of AI technical debt is emerging. Draftt extends governance to cover custom agents, agentic workflows, and multi-model pipelines so engineering teams can govern the AI they build, not just the models they call.
The AI you build, governed alongside the AI you call. One layer, one policy engine.
Craik Pyke
VP of Infrastructure and Security Engineering
Book a 30-minute walkthrough. We'll connect your cloud environment and show you every AI model running in your stack, who owns it, and whether it is compliant with your policies.