API latency
Reduced tail latency on a B2B order API
Query plan and cache behaviour
method
Query plan rewrite, composite indexes, and response caching with tag invalidation.
//benchmarks
Engineering outcomes, with the method attached
Representative outcomes from delivered engagements. Each entry describes the kind of system and intervention involved without presenting unsupported numerical claims.
Client names are withheld under NDA. We do not publish numerical benchmark claims here because the underlying reports and raw results are not destination-accessible. These entries are qualitative descriptions of the work performed, not guarantees about what a different system will achieve. Any future numerical claim will include the public evidence needed to evaluate it independently.
Sustained throughput
Increased capacity for a read-heavy catalogue service
Connection and read-path efficiency
method
Connection pooling, read replicas, payload compaction, and an asynchronous write path.
Infrastructure cost
Reduced infrastructure spend for a Kubernetes platform
Resource and storage efficiency
method
Right-sized requests and limits from usage data, spot node pools for stateless workloads, and storage-class review.
Deployment lead time
Shortened releases for a monolith with manual steps
Pipeline and migration safety
method
Pipeline parallelisation, test-suite partitioning, an automated migration gate, and blue/green cutover.
Recovery operations
Improved recovery work for a payment integration platform
Observability and restore readiness
method
Structured logging with trace correlation, actionable alerts, documented runbooks, and restore drills.
Peak-load resilience
Reduced failure pressure during seasonal retail traffic
Backpressure and graceful degradation
method
Queue-based load levelling, idempotent retries, and graceful degradation of non-critical reads.
//verification
How we would prove it on your system
The same procedure every performance engagement starts with. It is deliberately boring and entirely reproducible.
- 01
Capture a representative traffic profile from production — endpoint mix, payload sizes, concurrency, cache state.
- 02
Build a k6 or JMeter harness that replays it against a staging environment sized like production, and record the baseline.
- 03
Profile under that load. Fix what the profile shows, in order of measured cost, one change per commit.
- 04
Re-run the identical harness. Publish both runs, including the changes that did not help.