When operating your live system takes more and more time.
01Deployments are difficult and diagnosing failures takes too long.
02Backup, rollback or capacity limits are unclear.
03You want to expose an AI model to your application through an API.
Project scope
Define delivery and acceptance
from the start.
We review the deployment setup, logs and metrics, operational responsibilities and target environment together.
Project package
Platform Reliability Improvement
A concrete improvement to a priority deployment, visibility or operational issue.
Deliverables
Baseline and priority assessment
Required CI/CD and infrastructure changes
Logging, metrics and error visibility
Deployment, rollback and operations guide
How do we accept the work?
The baseline is recorded. Changes are verified through diagnosis and rollback scenarios; performance targets are measured under comparable conditions.
Project package
AI Model Service Setup
A selected model running as an access-controlled API in the target environment.
Deliverables
Model and runtime compatibility checks
API, container and deployment setup
Resource, latency and capacity measurements
Access control and operational notes
How do we accept the work?
Compatibility, resource use, capacity limits and authorization are verified for the selected model and environment.
Scope, responsibilities, price and schedule are clarified in a written proposal after the initial conversation.
Technical approach
Tools that fit
your current system.
We choose technology based on requirements, the existing architecture and operating conditions. Changes are scoped alongside the deliverables.
Docker
Kubernetes
AWS / GCP
Terraform
GitHub Actions
OpenTelemetry
Related client engagement
BeaconFSA
ML/LLM-assisted document verification and an end-to-end loan origination platform, with technical ownership from development through production operations.