Fixed-scope, fixed-price reviews of your hardest systems problems — streaming, reliability, AI inference, data, cloud. You see exactly what you get, what it costs, and how long it takes. No discovery call to find that out.
Each is a self-contained engagement. Same transparency every time: a public scope, a flat price, a fixed clock, and a written teardown you keep.
Partitioning, consumer-group design, exactly-once, backpressure, and lag — found before they page you.
Vendor-neutral. Model routing, caching, batching, token economics, fallback tiers, self-host vs API.
Single points of failure, retries, idempotency, circuit breakers, and blast-radius under partial outage.
Before a launch, a raise, or a first enterprise contract: capacity model, bottlenecks, an honest go / no-go.
EKS, MSK, right-sizing, spot strategy, egress, and the networking choices quietly inflating your bill.
Retrieval quality, chunking, an eval harness that means something, hallucination controls, cost and latency.
Query plans, indexing, partitioning, connection pooling, and a replica strategy that survives growth.
Tracing, metrics, logs, Grafana dashboards, and SLOs — so you stop debugging production blind.
Over-split diagnosis, service boundaries, monolith-vs-services, and the comms patterns causing the pain.
Monolith→services, database moves, on-prem→cloud: sequencing, rollback, and a dual-write plan that holds.
The whole point is that you never wonder what you're paying for. Four steps, no surprises.
Scope and price are public. Choose the one that fits — or send your problem and I'll tell you which it is.
One 45-minute sync to agree the exact question, plus read-only access to the systems in scope.
I go through it the way I'd go through my own production stack. No status-meeting theatre.
A written report — findings, severity, fixes, sequencing — and a working call to walk it through.
The honest-verdict clause. If two weeks inside your system turns up nothing worth fixing, the teardown says exactly that — a clean bill, what was checked, and the watchlist I'd keep. You're paying for the verdict, not for manufactured findings.
Not a slide deck of platitudes. Specific failure modes, the evidence, and the fix — at the depth that prevents the 3am page. This one is a representative sample — the format is exactly what you receive.
Consumers use the default session.timeout.ms with eager assignment, so each rolling deploy triggers a full group rebalance. Under your 14-partition topic and a 6-replica service, that's ~40s of stop-the-world per deploy — and it compounds during the autoscaling events that follow traffic spikes.
Move to CooperativeStickyAssignor for incremental rebalancing, raise the session timeout past your deploy window, and adopt static group membership (group.instance.id) so rolling restarts don't reshuffle ownership. Expected: rebalance impact drops from ~38s to sub-3s, lag peak roughly 10×.
Distributed systems at Amazon Alexa. Head of Engineering at a health-tech startup. Java / Spring / Kafka, Go, AWS, and applied AI in production. You work directly with the person doing the work, start to finish — which is also why scope is fixed: focus is the product.
Book an audit directly, or describe what's keeping you up and I'll point you to the right one — or tell you honestly if you don't need one.
A few details — I'll personally reply within one business day with which audit fits, or an honest "you don't need one."
I'll personally read this and reply within one business day, from priyanshu@zephyrcode.live. Talk soon.