Architecture that survives growth — real-time data, cache strategy and infrastructure you can reason about at 3am.
Scale is not a bigger server. It is knowing which part breaks first, measuring it before customers do, and having already decided what happens when it does.
A written read of your current system: where it breaks under load, what it costs to fix, and what can safely be left alone.
Reactive backends and subscription-driven clients, so state stays consistent across sessions without polling loops holding it together.
Cache layers, rate limiting and read paths designed against measured traffic — not against a guess about what will be hot.
Infrastructure as code, preview environments, zero-downtime deploys and cost visibility per service rather than one monthly surprise.
Projects we shipped in scalable systems. Every one links to running software or the source it was built from — check any of them.
2026Convex
Business-critical invoicing system where two users must never see divergent records.
Built on Convex's reactive backend — live queries push updates to every connected client, with authentication and access control at the data layer rather than in the UI.
2026Astra DB
Multi-tier data layer serving vector search, caching and relational persistence together.
DataStax Astra DB for vector retrieval, Upstash Redis for distributed rate limiting, Vercel Postgres for durable state — three stores, each doing the one job it's good at.
2026Python
Python tooling for provisioning and operating AI workloads.
Infrastructure scaffolding for model-serving workloads — environment definition, dependency isolation and repeatable deployment.
The open-source projects this work stands on. Built and maintained by their own teams, not by us — listed here because knowing what we build with tells you more than a logo wall would.
Load profile, query plans and real traffic first. Optimising before measuring is how teams speed up the wrong thing.
Identify the actual bottleneck — usually a data access pattern, rarely the language or the host.
Changes land incrementally behind flags, each one verified under load before the next.
Dashboards, alerts and capacity headroom documented, so the next limit is spotted early instead of hit at speed.
What clients ask before starting scalable systems work with us.
At design time for anything structural — data model, access patterns, tenancy — because those are expensive to change later. Not at design time for throughput. Most systems never reach the load their architecture was over-built for, and the cost of that over-building is paid every sprint in complexity.
We read the codebase, the schema and the production metrics, then deliver a written assessment: current bottlenecks, the order they'll be hit in, what each fix costs, and what can be safely ignored. It typically runs one to two weeks and is useful on its own, whether or not we do the implementation.
Usually, through dual writes, backfill and staged cutover with a rollback path at every step. Some migrations genuinely need a maintenance window, and when that's the case we say so up front rather than promising zero downtime and discovering otherwise on the night.
Almost certainly not yet. Microservices trade a code problem for a distributed-systems problem, and most teams asking for them have a modelling issue a well-structured monolith would solve for a fraction of the cost. We'll tell you when the split is actually justified — usually it's about team boundaries, not load.
Per-service cost visibility from the start, right-sized resources based on measured usage, and caching where it removes billed work rather than just adding a layer. Runaway cloud bills are nearly always an architecture symptom, not a pricing problem.
Tell us what you're building. We'll come back with a read on scope, timeline and whether scalable systems is genuinely the right call.