Software for operations that standard tools can't handle.

Purpose-built systems for non-standard workflows, complex state machines, and business logic that existing software was never designed to model.

// The problem

Standard software assumes standard operations.

Most tools are built for the common case. Non-standard workflows get forced into rigid schemas, losing the logic that makes them work.

This is why companies end up with 14 spreadsheets and a prayer.

Manual processes don’t survive scale.

Spreadsheets and handoffs compound into fragile pipelines. Every manual step is an unmonitored failure point with no rollback.

At 10x volume, every workaround becomes a production incident.

Configurable is not the same as custom-built.

Toggles and dropdowns can’t model domain-specific state machines. When the business logic is the product, the software has to match it exactly.

Configuration space grows polynomially. Domain logic doesn’t.

spiflare -system overview
booking_engineactive
workflow_systemrunning
payment_syncconsistent
exception_handlingautomated
manual_operationseliminated

interactive. try typing a command.

// Our approach

01

Map the operation

Workflows are traced end-to-end: inputs, decision points, failure modes. What matters is how data actually flows, not how the org chart says it should.

Output: system spec with state diagrams, data models, and failure scenarios documented before architecture begins.

02

Architect the system

Software is designed around real constraints, not product templates. Schema, state machines, edge cases, all scoped before a line of code ships.

Output: technical design doc covering data flow, consistency boundaries, scaling strategy, and rollback plans.

03

Ship and sustain

Working software goes to production, then stays healthy. Monitoring, incident response, iterative improvements. Systems are maintained, not abandoned.

Output: production system with observability, runbooks, SLOs, and a team that answers the pager.

// How we think

Domain-first design

The problem domain is modeled before the stack is chosen. Data models, state transitions, and invariants are defined upfront. Technology decisions follow from requirements, not the other way around.

Proven tools, minimal surface area

Mature, well-understood technology over novel stacks. Fewer dependencies, smaller attack surface, easier debugging. Every library added is a liability maintained.

Resilience as a requirement

Partial failure is treated as the normal operating mode. Every external call gets a timeout, a retry policy, a fallback, and a circuit breaker. The other side is assumed down until proven otherwise.

Observability built in

Everything is instrumented. Structured logs with correlation IDs, distributed traces across service boundaries, alerts on symptoms not causes. If a system can't explain its own behavior, it doesn't ship.

Continuous delivery

Small changesets, automated testing, zero-downtime deploys. Rollback in under two minutes. The risk of a release is a function of its diff size, nothing else.

Build and operate

The engineers who write the code own the on-call rotation. No handoffs between build teams and operations teams. The same people who ship it are the ones woken up by it.

// What a system can look like

scroll horizontally to explore

INGRESSCOREMESSAGINGML PIPELINEDATAOBSERVABILITYload_balancerwafapi_gatewayauth_servicerate_limiterorchestratorschedulerevent_processorsaga_coordinatorcqrs_projectormessage_busdead_letter_queueevent_streamchange_data_capturefeature_ingestionfeature_storetraining_pipelinemodel_registryinference_servicedrift_detectordb_primarydb_replicacache_clusterobject_storetimeseries_dbsearch_indexlog_aggregatordistributed_tracemetrics_pipelinealert_engineincident_router

Have a problem worth solving?

Tell us what's broken. We'll tell you if we can fix it.

info@spiflare.com