Releases that are routine. Production that is watched.
Every system we ship goes out through the same repeatable pipeline, with a rollback path that has been rehearsed rather than assumed — and once it is live, monitoring watches production and raises the alarm before your customers do.
- Release path
- One pipeline, one artefact, staging to production
- Delivery stack
- Docker · Kubernetes · Terraform · GitHub Actions
- Watching production
- Prometheus · Grafana · ELK · Alertmanager
- After go-live
- Monitored, with runbooks and an on-call handover
What sits behind the claim
Reliability is built in during delivery, not added after the first outage.
- Containerised by default. Every service ships as a Docker image, so the artefact that passed QA is the exact artefact that reaches production.
- Pipelines as code. Build, test, scan and deploy defined in GitHub Actions beside the application, so the path from a merged pull request to production is the same path every time.
- Infrastructure as code. Every environment described in Terraform, so staging and production cannot quietly drift apart and rebuilding one is a pipeline run rather than a recovery project.
- Safe rollouts. Blue-green and canary releases with automated rollback, so a bad deploy is a non-event instead of an outage.
- Tests inside the pipeline. Critical user journeys automated and run on every pull request, so a regression is caught before merge instead of by a customer.
- Load tested before launch. Throughput and latency measured against a stated target on a production-like environment, with the breaking point identified rather than discovered.
- Service level objectives. Availability and latency targets per service, with error budgets that make "good enough" a number instead of an argument.
- Alerts that mean something. Related alerts grouped and routed to the team that can act, and tuned down until every page is one a human should genuinely wake up for.
How it works in an engagement
From the first deployable increment to the years after launch.
A working environment from the first increment
Build runs in two-week increments against a deployed environment from the first increment onwards, so you review running software rather than status reports. Every project ships with its infrastructure defined as code, its pipeline running and its tests already inside that pipeline.
Observability is a build-time decision
Services emit metrics, structured logs and traces correlated by request, so the question "why is this slow" has an answer that does not require guessing. Dashboards are provisioned as code and answer specific questions — is it healthy, is it within its SLO, what changed.
A baseline before anything clever
For systems you already run, we instrument what exists and establish a baseline from your own traffic first, because anomaly detection on an unmeasured system is theatre. SLOs are then agreed with the people who carry the pager.
Runbooks that execute
Documented failure modes, alert thresholds and escalation paths, and the repeat work of an on-call rotation — restarting a known-bad process, expanding a volume, draining a node — turned into reviewed runbooks that are logged and reversible. Incidents get a timeline built from telemetry, not memory.
Deploy on demand, a rollback path that has been rehearsed, and fewer pages — the ones that remain mean something.
Everything runs in your own cloud accounts and stays yours, including the dashboards and the telemetry. BazaarBandhu made observability across its decoupled catalog, order and payment services an explicit objective. The detail is on the DevOps and AIOps pages.
Questions we are asked
Do we need to replace our current monitoring stack?
Usually not. We build on Prometheus, Grafana, Loki and the ELK stack, and most estates already run part of that. The first phase is normally filling instrumentation gaps and consolidating alerting, not a migration — you should end up owning your own telemetry.
How do you test performance before launch?
Against a production-like environment, with a traffic model drawn from your real peaks rather than a round number. The output points at the specific query, lock or external call responsible, plus a repeatable script so the same measurement can be taken again after the fix.
Do you provide ongoing support after go-live?
Yes. Managed services cover operations, updates, performance, security and scale after launch. Or we hand the platform over completely, with runbooks, and step back.