Pre-seed, seed and Series A teams come to us when shipping fast is no longer the only metric. We build the production-ready foundation that lets you ship features for the next two years without an architectural rewrite.
Most of our SaaS engagements fall into one of four shapes:
We're a fit when:
Not a fit if you're pre-funding, validating an idea, or building a feature on top of an existing platform.
We've shipped enough post-seed rebuilds to know which decisions cost teams six months and a Series A delay. These are the five we see most often.
Four phases, predictable cadence, weekly demos. Nothing is hidden behind a 'big reveal' — you see deploys every week from week three.
We map the existing system, decide the tenancy model, lock the data model and write a one-page RFC your team and ours both sign off.
API contracts, auth and billing boundaries, deployment topology. The output is a buildable spec — reviewed by your CTO before any production code is written.
Vertical slices, weekly deploys behind feature flags. No big-bang launch — your team can ship product features alongside us throughout.
Load test, SLO baseline, runbooks, on-call documentation. We hand the system to your engineers, not to a maintenance contract.
These aren't logos on a wall. Each row is one engagement: what was broken when we walked in, what we changed, and what shipped.
Startup EngineeringA Telegram-native gaming platform expecting a viral launch — the existing backend couldn't survive a single peak hour.
Stateless Java workers, queue-based fan-out, predictable horizontal scaling on a managed Postgres tier.
Shipped through launch week without a scaling incident; engineering team kept building product, not patching infra.
Startup EngineeringAn AI-driven B2B growth platform with the inference path tangled into the application code — every model change risked the product.
Split inference behind a stable API, designed a GDPR-aligned data flow with EU-only processing, separated billing events from product events.
Onboarded EU customers without a compliance rework; the data team can iterate models without coordinating product releases.
Startup EngineeringA multi-role marketplace where creators, brands and agency operators all needed isolated workspaces, dashboards and approval flows in a single product surface.
Role-based product logic, per-workspace data isolation, structured campaign and content workflows engineered into the system from day one rather than retrofitted.
Three operator personas use the same platform without conflict; new role types can be added without rewriting the access model.
Startup EngineeringA SaaS site builder where every customer's published site shared the same render layer — a single noisy site degraded everyone else.
Per-tenant render layer, dedicated cache topology, isolated build pipelines so each customer's deploy stays independent.
Scaled to paying customers without per-customer ops work; new customer sign-up does not add to the on-call load.
The decisions we expect to argue about with your team in the architecture sprint — and the questions investors and acquirers will ask later.
Silo, pool or bridge — each model trades isolation against operational cost. The right choice depends on regulated data, expected tenant size, and whether you'll ever need a single-tenant deployment.
Users belong to organisations, not the other way around. Workspace switching, role hierarchies and (later) SCIM provisioning all depend on getting the org/user/role boundary right on day one.
Plan changes mid-cycle, usage-based pricing, free-trial conversions and prorated upgrades all live or die on the metering boundary. Treat billing as a separate domain, not a feature flag.
Outbound webhooks fail. Inbound integrations rate-limit. Idempotency, retries and replay are infrastructure, not application concerns — the SaaS that gets this right won't lose customer data on a partner outage.
EU hosting is the easy part. The harder part is data subject access requests, sub-processor disclosure, deletion paths and contractual DPAs your customer's procurement team will read line by line.
Per-tenant metrics matter more than aggregate ones. A 99.9% global SLO can hide a single customer who's seeing 10% errors — and that customer churns first.
If your question isn't here, it's almost certainly the first thing we'll discuss in a 30-minute call.
Different situation? We answer the actual question, not the brochure version.
Ask directly→From the architecture sprint to a stable, production-running system: typically 12–20 weeks for a seed-stage product, depending on what's already shipped and what has to keep running. The first deploy of new code usually happens in week three. We don't quote a re-architecture without the architecture sprint output — quoting blind is how teams get burned.
Yes — that's the explicit constraint we plan around. Every implementation phase produces vertical slices behind feature flags, deployed weekly. Your product team works in the same repo, reviews the same PRs, and ships features alongside the rebuild. There is no production freeze, ever.
We work alongside it. We embed in your repository, follow your code review, attend your standups when it helps. Our job is to make your engineers stronger, not to make them dependent on us. By the end of stabilisation, the system runs on your team's on-call rotation, not ours.
Modular monolith, almost always, until you have at least three product squads or a clear operational reason to split. Microservices at seed stage trade a real problem (codebase coupling) for a worse one (operational complexity). The exceptions: anything that has to scale independently, like AI inference or per-tenant rendering.
We default to a bridge model — per-tenant schema in a shared cluster — because it's the easiest to migrate from later. Silo and pool are both reachable from bridge with bounded effort; the reverse migrations are punishing. The decision is made in the architecture sprint based on your largest expected tenant and your regulatory exposure, not on a default.
GDPR is a baseline, not a feature. Every system we ship has EU-only processing, a documented sub-processor list, per-tenant deletion that survives backups, and a DPA template your enterprise customers can sign. We'd rather over-engineer this on day one than defend a half-measure to your customer's procurement team six months later.
