Designing for Scale

We build backends designed to evolve, not to be refactored when they cannot. Every system starts from a service boundary map: we identify what owns data, what transforms it, and what consumes it. We apply hexagonal architecture so business logic is never coupled to a database driver, queue library, or cloud vendor.

For startup-scale projects we use .NET 9 with a thin CQRS pattern. Commands and queries are separated from day one so reads can be optimised independently of writes. We use PostgreSQL or SQL Server for transactional data, EF Core for complex queries, and Dapper for high-read paths where latency matters. All external integrations — payments, webhooks, AI inference — are wrapped behind interface contracts, making them swappable without touching domain logic.

Security is structural, not layered on after. Role-based claims are enforced at the handler level. Sensitive fields are encrypted at rest using envelope encryption. All cross-service communication uses signed tokens, and we enforce mTLS in service meshes where the threat model requires it.

Scale is an architectural commitment, not a feature added later. We separate scalability concerns into three layers: compute, data, and network — each with its own strategy.

At the compute layer, services are stateless from the beginning. Session state lives in Redis, not process memory. This means horizontal auto-scaling via Kubernetes HPA works without coordination overhead. For AI workloads, inference requests are offloaded to an isolated worker pool backed by a queue (RabbitMQ or Azure Service Bus), so processing spikes never degrade main API response times.

At the data layer, we apply read/write splitting early. Write traffic goes to the primary, read traffic goes to replicas. For high-cardinality queries — dashboards, reporting — we use materialised views refreshed asynchronously. We do not run ad-hoc GROUP BY queries against a live transactional database.

At the network layer, all static and semi-static assets are served through a CDN. API responses safe to cache are served via output caching with Redis-backed invalidation. We monitor p95 and p99 latencies, not averages — because averages hide the outliers your heaviest users experience.

We do not bolt AI onto existing systems. We define what the AI must decide, then design backwards from the output contract. Before any model selection, we answer three questions: What decision should the AI automate? What is the cost of a wrong answer? How will humans override it?

For most business AI problems, the right solution is a well-structured prompt pipeline over a foundation model (GPT-4o, Claude, Gemini) with deterministic guardrails and typed output parsing — not a custom-trained model. We use LangChain or raw SDK depending on orchestration complexity. AI responses are always parsed, typed, and validated before touching any downstream system.

When a problem genuinely requires a custom model — classification, anomaly detection, time-series forecasting — we build a clean inference service: a Python FastAPI wrapper around the model, deployed as a containerised microservice. The model is versioned separately from the service, loaded from object storage at startup, so model updates do not require a full deployment cycle.

We instrument every AI call: input tokens, output tokens, latency, and confidence scores are logged and monitored. This gives you visibility into cost trends and quality degradation before your users notice.