Client work / Enterprise commerce
One company-owned AI platform. Five departments.
Drizzle designed and implemented a governed AI platform in the client's AWS environment, giving around ten agent workloads one route to approved models, enterprise knowledge, and production operations.
The constraint
The company had AI demand. It did not yet have an AI operating system.
Multiple teams were building agents, but every workload created a new infrastructure and governance problem. The platform had to reduce repetition without limiting product choice.
Every team rebuilt the same foundation
New agent workloads repeatedly reopened identity, model access, data connection, deployment, observability, and cost questions before product work could begin.
The managed path changed the economics
A managed platform lowered initial engineering effort, but its projected operating cost and long-term dependency did not fit the client's portfolio strategy.
Experiments lacked a production contract
Teams needed one governed route for approved models, enterprise knowledge, usage controls, audit evidence, and reliable operation in the client's environment.
The architecture decision
Buy convenience, or build a durable company capability.
Drizzle evaluated the platform as a portfolio decision. The selected path had to work across operating cost, control, and long-term extension rather than optimize only for the first workload.
- Economics
Recurring platform premium grew with portfolio adoption.
- Control
Core operating choices lived behind a provider boundary.
- Extension
New workloads inherited one vendor's product surface.
Compared with the managed-service direction
- Economics
Shared infrastructure and open components improved the projected cost model.
- Control
Cloud resources, platform code, policy, and telemetry stayed with the client.
- Extension
Teams joined stable internal contracts while models and components remained replaceable.
The system
One governed control plane connected teams to replaceable AI services.
The design separated the interface used by product teams from the models, data services, and infrastructure underneath it. Shared policy lived in the platform instead of every agent.
Identity, approved access, routing, quota, usage visibility, and audit evidence for the complete workload portfolio.
Delivery sequence
Architecture and implementation moved as one engagement.
The platform was proven with real workloads in the client's environment, then expanded as a shared production capability rather than delivered as a static blueprint.
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Model the portfolio, not one demo
Drizzle aligned product, technology, security, and platform stakeholders around the first workloads, shared constraints, ownership boundary, and operating model.
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Choose the company-owned path
We designed a secure AWS foundation, governed AI control plane, enterprise knowledge path, and production operations layer as one system.
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Prove the complete path with real agents
Infrastructure automation, gateway controls, data access, observability, and initial workloads were implemented in the client's environment and repositories.
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Turn the platform into a shared capability
New departments and agent workloads could join the same governed foundation while Drizzle supported reliability, optimization, and client ownership transfer.
What Drizzle delivered
The shared gateway was one layer of a complete production platform.
Every delivery stream reduced work that product teams would otherwise repeat for each new agent.
Automated AWS foundation
Infrastructure as code and delivery automation created a repeatable path for networking, compute, storage, identity, secrets, policy, and platform changes.
Unified model gateway
Client-built agents reached approved models through one governed interface for identity, routing, quota, usage, and audit controls.
Enterprise knowledge path
Controlled interfaces connected workloads to approved internal information without surrendering ownership of the data boundary.
Production observability
Metrics, logs, traces, dashboards, cost visibility, and operational procedures were included in the platform rather than added after adoption.
Workload onboarding contract
Stable platform interfaces let teams bring new agents into production without creating another isolated infrastructure stack.
Client ownership transfer
Cloud resources, repositories, controls, documentation, and operating practices remained inside the client's engineering boundary.
Evidence ledger
Economics, adoption, implementation, and ownership stay labeled.
The result is strongest when each number keeps its context. Projected economics are not presented as realized savings, and deployment speed is scoped to the agreed foundation.
Automated deployment time for the agreed production infrastructure foundation after architecture and environment requirements were established.
Projected operating cost compared with the managed service direction initially considered. This is a planning comparison, not realized savings.
Agent workloads using the shared model access and governance layer rather than separate platform stacks.
Product, technology, customer success, sales, and solution architecture using one company-owned production path.
Platform infrastructure, source, policy, telemetry, and operating controls stayed in the client's cloud and repository boundary.
One shared control point for approved model access, routing, quota, usage visibility, and auditability across the portfolio.
Cost scope. The 70 percent comparison belongs to the planning model used to choose between architecture directions. It does not claim a realized or universal savings rate.
Organization adoption
Five departments joined one production path.
Adoption did not require five platform stacks. Each department used the same governed boundary while product context remained with the workload.
Product
One path from an agent idea to a governed production workload.
Technology
Shared platform contracts instead of repeated infrastructure plumbing.
Customer success
Approved AI capabilities on the same operated foundation.
Sales
Access to governed workloads without an isolated stack.
Solution architecture
A reusable platform boundary for customer-facing solution work.
What changed
AI experiments became an organization-level capability.
Teams could focus on their workflows while the platform supplied the common production contract underneath the portfolio.
One production path
- Before
- Separate platform decisions for every new agent
- After
- A shared route for deployment, model access, enterprise data, policy, and operations.
Company-owned economics
- Before
- A managed-service direction with a growing projected premium
- After
- Open components and shared AWS infrastructure with a projected 70 percent lower operating cost.
Portfolio-level governance
- Before
- Identity, routing, quota, and audit logic inside each workload
- After
- Around ten agents using one governed gateway and one operating model.
Operations from day one
- Before
- Monitoring and support added after a successful prototype
- After
- Metrics, logs, traces, cost visibility, procedures, and ongoing onboarding built into the platform.
Ownership at handoff
The client gained a platform it could change without asking permission.
Drizzle remained close to production while designing the system for durable client control over infrastructure, source, policy, cost, and future platform choices.
- 01 Cloud infrastructure
The platform ran in the client's AWS environment under the client's identity, network, policy, and cost boundaries.
- 02 Platform source and delivery
Infrastructure code, platform services, configuration, and delivery automation remained in client-controlled repositories.
- 03 Models and components
A stable access layer kept model providers and infrastructure components replaceable as needs and economics changed.
- 04 Operating capability
Documentation, observability, procedures, and continued onboarding developed the client's ability to operate and extend the platform.
Your AI portfolio
Stop making every agent solve the platform again.
Bring the workloads, the current cost model, and the ownership boundary. We will identify the shared platform decision worth proving first.
Talk to an engineer