These blueprints show how our team approaches problems we know well, drawing on our engineers’ hands-on experience in fintech, cloud and reliability. They describe our approach, not specific client engagements.
AI claims triage Cloud cost with Graviton Outage preventionClaims arrive as emails, PDFs, scans and photos. Teams read each one by hand to decide what it is, whether it’s complete and who should handle it, so simple claims wait in the same queue as complex ones.
Time from claim received to first assignment
Share of claims routed without manual reading
Agreement with expert reviewers on a held-out sample
Cost per claim processed
Step 1
Sample real claims, agree the categories, routing rules and the cases that must always go to a person.
Step 2
Pull key fields from documents, classify claim type and urgency, and flag missing information automatically.
Step 3
Low-confidence or high-value claims go to an adjuster with the AI’s reasoning attached; every decision is logged for audit.
Step 4
Deploy on your cloud with monitoring for accuracy, drift and cost, and retrain on adjuster corrections.
Typical path: 2-week discovery, 4–6 week pilot on a single claim type, then scale.
Compute is often the largest line on the AWS bill. Arm-based Graviton instances can lower it, but teams worry about compatibility, performance and a risky cut-over.
Cost per request or per transaction
CPU utilisation and cluster density
p95 latency before and after
Zero customer-facing incidents during cut-over
Step 1
List workloads, dependencies and container images, and identify what already runs on Arm.
Step 2
Produce multi-architecture images in CI/CD so every service can run on x86 and Arm.
Step 3
Add Graviton node groups on EKS, shift a small share of traffic and benchmark against the current fleet.
Step 4
Migrate service by service with automatic rollback, then right-size instances on the new baseline.
Typical path: 1–2 week assessment, then a phased migration by service.
A single mistyped value in infrastructure code, such as a load-balancer timeout, can take a whole application down. Most teams find out from their users, not their alerts.
Time to detect and time to recover
Change failure rate
Share of incidents caught by alerts before users
Follow-up actions closed on time
Step 1
Policy checks in the pipeline catch risky infrastructure changes before they are applied.
Step 2
Changes reach a small slice of traffic first, with automatic rollback when health checks fail.
Step 3
Dynatrace, Prometheus or Datadog alerts tuned to early warning signs, not just hard failures.
Step 4
Every incident ends with fixes to systems and tooling, tracked to completion.
Typical path: 2-week reliability review, then guardrails and alerting rolled out in sprints.
Tell us about it. We’ll reply within one business day with how we’d approach it.
Contact us Email sales@devqore.io