What we build and run
Four practices for the systems under demanding products.
We work in the parts of the stack where correctness and scale decide whether a product survives contact with real traffic. Every practice is led by engineers who have run these systems on call, and any of them can be combined into a single engagement with one team accountable for the result.
The four practices
Deep in a few areas, not thin across everything.
Each practice below lists what a typical engagement actually includes. Follow any card through to its detail page for the reference architectures, the tools we reach for, and the numbers we hold ourselves to.
Platform & distributed systems
High-throughput APIs, event-driven services, and the resilience work that keeps them standing. We design for the failure modes first: backpressure, partial outages, idempotent retries, and clean degradation instead of a cascade.
- Service and API designgRPC and HTTP services with explicit contracts, versioning, and load budgets you can defend.
- Event-driven coresExactly-once semantics where it matters, outbox and saga patterns, and replayable state.
- Resilience engineeringCircuit breakers, rate limits, and load tests that prove the p99 holds under stress.
- Ledgers and correctnessDouble-entry accounting, consistency guarantees, and audit trails that reconcile.
Cloud & infrastructure
Kubernetes platforms, infrastructure as code, and the observability and SRE practice that makes them operable. We build a repeatable path from a commit to production, then instrument it so on-call can act on signal rather than guess.
- Internal platformsSelf-service deploys, golden paths, and multi-tenant clusters with real governance.
- Infrastructure as codeTerraform modules and GitOps pipelines so environments are reproducible, not hand-tuned.
- ObservabilityMetrics, traces, and logs wired to SLOs and error budgets, not vanity dashboards.
- Cost and reliabilityRight-sizing, autoscaling, and capacity plans that keep the bill honest.
Data & streaming
Real-time pipelines, event streaming, and analytics platforms that move data from source to decision in seconds. We handle the hard parts of streaming: ordering, late arrivals, exactly-once sinks, and schemas that evolve without breaking downstream consumers.
- Streaming pipelinesKafka and stream processors sized for millions of events per second, with backpressure handled.
- Ingestion and CDCChange-data-capture off operational stores without slowing the systems of record.
- Analytics storesColumnar warehouses and time-series stores tuned for sub-second operational queries.
- Data contractsSchema registries and quality checks so bad data fails fast instead of spreading.
Applied AI systems
Retrieval systems, model serving, and evaluation pipelines that put language models into production behind guardrails you can measure. We treat a model like any other dependency: instrumented, load-tested, and bounded, with a fallback when it is wrong.
- Retrieval and RAGGrounded assistants over your own data, with citations and controls against drift.
- Model servingLow-latency inference on Triton and friends, batched and autoscaled under a latency budget.
- Evaluation harnessesOffline and online evals so quality changes are caught before users feel them.
- GuardrailsInput and output checks, rate limits, and human review paths for high-stakes decisions.
How we engage
Three ways to work with us.
Most engagements start as one of these three shapes and move between them as the system matures. The team stays the same throughout, so context is never lost in a handover.
Fixed-scope build
We design and ship a system to production against a written scope with milestones and a fixed discovery fee. You see a working path to production early and real behavior under load from week one.
- Architecture and milestone plan up front
- Vertical slices shipped to production early
- Runbooks and a clean handover at the end
Managed operations
We run the system we built, or one you already have, against defined SLOs. You get 24/7 on-call, an error-budget policy, and the same engineers who know the internals holding the pager.
- SLOs and error budgets agreed in writing
- 24/7 on-call with named engineers
- Incident reviews and steady hardening
Review and embedded staff
Architecture reviews, production audits, and senior engineers embedded alongside your team. Useful when you have the people but want a second, accountable opinion before a bet gets expensive.
- Architecture and readiness reviews
- Reliability, cost, and security audits
- Embedded senior engineers by the sprint
By the numbers
A track record measured in uptime.
Start
Not sure which practice you need?
Describe the system and where it hurts. We will tell you which of these fits, or say plainly if we are not the right team for it.