Senior engineers who have spent 8+ years designing, shipping, and running backend systems for real businesses.
We are senior engineers who have spent 8+ years building Python backends that run real businesses — multi-tenant SaaS platforms, payment and billing systems, and data pipelines that hold up under real load.
That foundation is why our AI work behaves. We treat an LLM the way we treat any unpredictable third-party service: we isolate it, monitor it, control its cost, and keep it away from data it shouldn’t touch.
We don’t build demos. We build systems that don’t break — and we make AI behave like the rest of them.
Architectural decisions made for early traction that break under load — database bottlenecks, N+1 queries, and single points of failure.
Errors in jobs, pipelines, or integrations that don’t alert, don’t surface, and slowly corrupt data or drop requests.
Cloud usage grows without visibility or control, driven by inefficient queries, overprovisioned services, and poor resource boundaries.
Critical code paths become untouchable because knowledge is fragmented, ownership is unclear, and failure risk feels unpredictable.
Production Python backends from architecture to launch — APIs, data systems, async processing, payments, and multi-tenant platforms engineered for the scale you're heading toward, not just the one you're at.
We run what we build. Monitoring, incident response, backups and recovery, cost control — so the system stays healthy long after launch, and you're never guessing what broke.
AI built into real systems with the same discipline as the rest of the backend — data isolation, cost control, evaluation, and guardrails. RAG and agents that behave in production, not just in the demo.
8 independently deployable microservices — exam runtime, scoring, billing, identity — handling timed, concurrent exam sessions with Redis session state, SQS event dispatch, and observability built in from day one.
An LLM transaction-categorization layer for a budgeting platform, engineered so sensitive financial data is never sent to the model. Automatic AI categorization, zero privacy trade-off.
A conversational ERP agent that executes real operations — orders, invoices, purchase orders — through supervised routing with mandatory confirmation before any critical write. Designed, built, and live in 45 days.
A focused, paid diagnostic of your backend — architecture, risks, and a clear verdict on scale readiness. $1,500–$3,000, delivered in 1–2 weeks, no disruption to your team. If you want the findings fixed, we’re the team that can do that too.
We design, build, and operate production Python backend systems.
