SCOPE
Open-model deployment
Model packaging, serving configuration and hardware planning around memory, context and concurrency. Validate the choice against your actual workload.
FROM MODEL TO RUNNING SERVICE
Make AI part of a working system.
We deploy open models, integrate hosted model APIs and build the services around them. Start with your workload and data requirements, then choose the hosting, interfaces and operating controls that fit.
Discuss your projectSiavash Labs · Open-model infrastructure and application integration
Open modelYour deployment
Hosted APIProvider service
Backend servicesAccess · Requests · Jobs
Application APIWeb · Mobile · Internal tools
Two deployment choices; scoped for the workload.
AI INFRASTRUCTURE & INTEGRATION
A model is one component. The service around it needs engineering.
SCOPE
Hosted model integration
Connect provider APIs through your backend, with credentials, usage limits, request handling and provider-specific behaviour kept out of client applications.
SCOPE
APIs & service infrastructure
Authentication, job queues, streaming or asynchronous responses, data boundaries and application integrations. Design failure and cancellation paths alongside the happy path.
SCOPE
Evaluation & operation
Agree task-specific checks for response quality, latency and cost. Define monitoring, release, rollback and ongoing operational responsibilities.
WHAT THE HANDOVER CAN INCLUDE
A result your team
can work with.
Deliverables and acceptance criteria are agreed for your system before implementation begins.
- A deployment or provider-integration baseline
- Backend API and application integration
- Workload-specific evaluation and operating limits
- Deployment, monitoring and handover documentation
RELEVANT EXPERIENCE
See the responsibility in practice.
OUR INFRASTRUCTURE · SIAVASH LABS
Open-model infrastructure
Our infrastructure project for deploying open AI models and connecting them to applications and development workflows.
ApplicationWeb · Mobile · Development tools
Backend APIApplication integration
Model-serving infrastructureDeployed open model
Siavash Labs · Our own infrastructure project.
Own infrastructure projectView project
BEFORE WE START
Useful questions.
Direct answers.
Can you help without an embedded or automotive project?
Yes. AI infrastructure is a standalone service. It can support a business workflow, internal tool, web product, mobile app or an existing backend.
Should we self-host a model or use a provider API?
That depends on data requirements, model capability, usage, latency and who can operate the service. We compare both paths against a representative workload before recommending one.
Can the system run in our own environment?
We can scope deployment in a customer-controlled environment. Hardware, network access, model licensing and operational ownership need to be established first.
What does the initial pilot prove?
The pilot exercises an agreed workflow and integration boundary. Its acceptance criteria can include output quality, response time, failure handling and cost. It does not imply a universal model benchmark.
A CLOSER LOOK
Self-hosted model or provider API? Start with the workload.
Read the engineering guideLET’S GET SPECIFIC
Let’s define the next step.
Bring one workflow, representative inputs, data constraints and an expected usage pattern. We agree an integration pilot and what would make it ready for a wider rollout.