Service 01
Design AI architecture for the environment you actually have.
AI platforms rarely live in isolation. They sit between users, applications, identity, sensitive data, model providers, local infrastructure, developer tools, observability, and security controls. Deadlights helps turn those dependencies into an architecture that can be implemented and operated.
Engagement areas
Where the work happens.
- AI and LLM use-case discovery
- Trust-boundary and data-flow design
- Local, private, cloud and hybrid model patterns
- Secure AI gateway architecture
- Workload-profile design
- Model-provider and model-routing strategy
- Identity and authorization integration
- Data protection and DLP integration
- Logging, observability and audit architecture
- Resilience and failure-path planning
- Proof-of-concept design
- Production-readiness planning
Typical engagement outputs
What you leave the engagement with.
01Reference architecture. A clear target-state view showing workloads, control points, model destinations, enterprise integrations, trust boundaries, and operational data flows.
02Decision record. Document why major choices were made, including tradeoffs, alternatives and assumptions.
03Workload profiles. Define how web chat, coding, agentic workloads, applications, or other AI traffic should differ in policy, routing, logging, model access, and controls.
04Control map. Connect governance requirements to technical enforcement points such as identity, gateway policy, DLP, model allowlists, logging, approval and exception handling.
05Implementation backlog. Break the target state into small, testable increments with prerequisites, success criteria and ownership.
Fit
When this service is a good fit.
- Your AI proof of concept works, but production architecture is unclear.
- Different teams are independently adopting AI tools and model providers.
- Security controls exist but are not connected to AI usage.
- You need to evaluate local, private and external model paths.
- You want an architecture before buying more tools.
- You need a practical sequence for implementation.
Architecture should reduce uncertainty
Not create more documentation.
The goal is a design your technical teams can build from, test, operate, and revise—not a diagram that ends the engagement.