Cluster foundation
Networking, ingress, storage, node pools sized to the workload, and isolation so one hungry service cannot starve the rest.
What we cover
The complete system — because the expensive problems live between the pieces.
The cluster underneath, the architecture, the services that do the work, the data moving through them, the integrations outward, and the operations that keep it standing. Splitting it across vendors is where the expensive failures come from.
01 — Infrastructure
Cluster & platform
Kubernetes across as many hosts as the workload needs, wired into one cluster and described entirely in code. The fundamentals — metrics, logs, scheduling, isolation — come from the platform rather than being rebuilt per project.
Multi-host topology
Any number of machines — cloud, on-premise or mixed — joined into a single cluster that behaves as one system.
Templated rollout
The same deployment shape works for any product type, so a new system starts from a known-good base instead of a blank page.
Delivery by GitOps
Every environment lives in a repository and is reconciled continuously. A rollback is a revert; staging cannot silently drift.
Scaling & cost
Autoscaling tuned against real traffic, plus a straight answer about what the cluster costs and what to do about it.
Security posture
RBAC that follows real roles, secrets kept out of manifests, network policies, image provenance and an audit trail.
02 — Architecture
Microservice design
The part that decides whether your system stays changeable. Boundaries drawn along real seams in the business, contracts that are versioned and tested, and a shape that can be edited later without a rewrite.
What we design
- Service boundaries — along domain seams, not along org charts or database tables.
- Versioned contracts — verified in CI on the producer side and the consumer side both.
- Communication patterns — synchronous calls, queues or events, chosen per relationship rather than as a house style.
- Idempotency — because at-least-once delivery is the only kind you actually get.
- Failure behaviour — what each service does when its dependency is slow, gone, or answering wrongly.
What that gives you
- Add without fear — a new capability is a new small service, not surgery on an existing one.
- Remove without archaeology — deleting a part subtracts exactly what adding it cost.
- Deploy independently — one fix ships on its own instead of waiting for a release train.
- Onboard quickly — a new engineer learns one service, not the whole system, before being useful.
- Freedom of implementation — contracts hide what is inside, so each service uses the language and tools that fit it best.
03 — Data
Data flow & job pipelines
The main nodes of the data flow are the part that has to be understood up front. Everything else can be added later; a stream that stalls or silently loses records cannot be patched over.
Ingest
Connectors to sources, vendors and your existing systems, with backpressure, safe retries and detection of the gaps nobody would otherwise notice.
Storage choice
Relational, time-series, columnar, document — picked for this product and this data, since it is the decision most expensive to reverse.
Deployments & jobs
Continuous services for the steady stream, scheduled jobs for the periodic work, each with its own resources and failure handling.
Job pipelines
Multi-stage work chained so a failure stops at its stage, reports clearly, and resumes without redoing everything upstream.
Service communication
The paths between parts — queues, streams and direct calls — sized so throughput holds under the worst hour of the month.
Correctness
Schema validation that alarms on drift, replayable history where it matters, and a clear line between data you can recompute and data you cannot.
04 — AI
AI integrated into the tools you already use
A microservice is a natural home for a model: it has the data, the contract and the monitoring already. From there the useful move is usually not another dashboard — it is putting the answer where your people are already working.
Where it goes
- Own models on the live stream — custom ML developed as containers, trained continuously and predicting in real time on the data flowing through the system.
- Chat bots and assistants — in the company chat your team already lives in, answering from the system rather than from a general model's imagination.
- Inside your existing products — connected into the tools your customers or staff use today.
- As part of the flow — forecasting, classification and anomaly detection running as ordinary services in the pipeline.
- On the operational side — spotting broken feeds and unusual behaviour, often the highest-value and least glamorous model in the building.
How it is kept honest
- Grounded in your data — answers traceable to the records that produced them.
- Monitored like any service — inputs, outputs and drift as first-class metrics on the same alerting.
- Reversible — versioned, shadow-evaluated and switchable off in one command.
- Human in the loop by design — wherever being confidently wrong would be expensive.
05 — Product
The product itself, interface included
The services that do the actual work, plus whatever interface the product needs to be usable. We build the UI as a component of the system — and we do not take interface work detached from a system we are building.
Services that do the work
Each in the language that fits it — Go, Python, TypeScript or whatever the job calls for — with a small scope, an explicit contract, its own tests and metrics.
Interfaces
Dashboards, internal tools and operator screens, built to the shape of the data rather than the other way round.
Integration with your products
The systems you already run, connected in rather than replaced for the sake of it.
Quality testing
Contract tests at every seam, integration tests over the flows, load tests against the worst hour, and acceptance with your people before anything is called done.
06 — Operations
Monitoring, alerting and handover
You cannot operate what you cannot see. Observability is designed with the feature, not retrofitted after the first incident nobody could explain.
Telemetry
Metrics, logs and traces sharing correlation IDs across every service in the cluster.
Alerting
Alerts that mean something, map to a runbook, and reach a human before a customer does.
Handover
The cluster, the code, the documentation and the reasoning — delivered complete and yours.
Support
Ongoing operation and development if you want it. Never something we design you into needing.
Toolbox
What we reach for
Defaults, not dogma. The stack follows the product, and languages are chosen per service, not per company — containers make that free. If your team already runs something that works, we would rather learn it than replace it.
Languages
- Go
- Python
- TypeScript
- SQL
Runtime
- Kubernetes
- Docker
- Helm
- Argo CD
- Terraform
Messaging
- Kafka
- NATS
- Redis Streams
- gRPC
- REST / WebSocket
Data
- PostgreSQL
- ClickHouse
- TimescaleDB
- S3-compatible
- Parquet
AI / ML
- PyTorch
- scikit-learn
- XGBoost
- MLflow
- LLM APIs
Operations
- Prometheus
- Grafana
- OpenTelemetry
- Loki
- GitHub Actions
Engagement models
Three ways to work with us
Architecture review
A short, focused engagement. We read the system, the infrastructure and the incident history, then tell you in writing what will break first, what it costs to fix, and what to ignore.
Build the system
The full path — questions, stack, architecture, services, data flow, integrations, cluster and handover. We take responsibility for the result, not for a number of hours.
Run it with you
After handover: operations, monitoring and continued development of a cluster you own, so your team can stay on the things only they can do.
Not sure which part you need?
That is the normal starting position, and working it out is our job. Describe the business and the symptom — we will tell you which of these applies, including when the answer is “none of them”.