We are fluent in modern tools, but fluency is not the point. We choose the smallest stack that improves decision quality, reduces delivery risk, and leaves your team able to own the system after launch.
The most expensive technology decision is the one made because of fashion. We default to tools your team can operate, your hiring market knows, and your auditors have seen before — then introduce newer options only where the measurable upside justifies the operational burden.
Each entry below shows where we commonly reach for the tool and whether we consider it Core, Proven, or Emerging in our delivery playbook. If your current stack is not listed, we can still work in it; it simply has not become a default recommendation.
The right answer is rarely universal. We compare each candidate against ownership, people, risk, fit, and cost so the final choice is explainable to engineering, finance, and the board.
Can your team run, observe, patch, and recover it at 03:00?
Will you find engineers and partners who know it well?
Is the ecosystem boring enough for production risk?
Does it simplify the actual workload rather than impress a deck?
What are the licence, cloud, migration, and maintenance costs?
Good fit
Product-led web apps, content-rich journeys, and teams that benefit from shared TypeScript between UI and API boundaries.
Poor fit
Internal CRUD where server-rendered simplicity beats a rich client, or teams without JavaScript ownership.
Good fit
Transactional systems, SaaS control planes, reporting-adjacent data, and vector search when pgvector is enough.
Poor fit
Petabyte analytics, unbounded event streams, or single-digit millisecond global key-value access.
Good fit
Multiple services, clear platform ownership, portability requirements, and teams ready to invest in cluster operations.
Poor fit
A handful of services that can run more safely on managed containers, serverless, or platform-as-a-service.
Good fit
Knowledge assistants, workflow copilots, and retrieval-heavy use cases where answer quality can be evaluated.
Poor fit
Deterministic business rules, sensitive data without guardrails, or places where latency and cost cannot be bounded.
Retrieval, evaluation, permissions, and observability around LLMs so answers stay grounded in your own knowledge.
Kafka, queues, and outbox patterns for decoupled workflows that need replayability and failure isolation.
Tenant isolation, billing boundaries, feature flags, and data models that support enterprise growth.
Accounts, networks, identity, policy, and observability foundations before product teams scale delivery.
Component model behind the majority of the web apps we ship.
App Router, server components, and edge-rendered routes for production sites.
Strict TS across the front end — fewer bugs, faster onboarding.
Token-driven utility CSS with design-system primitives.
Content-led sites where shipping less JavaScript is the win.
Pragmatic alternative for teams already invested in the Vue ecosystem.
High-throughput services and BFFs on a JS runtime your front-end already knows.
Opinionated TypeScript framework for non-trivial backends and modular APIs.
FastAPI services, data tooling, and the glue for ML pipelines.
Latency-sensitive APIs and platform components where the runtime matters.
Enterprise-grade services where Spring is the standard your estate expects.
Long-lived line-of-business systems on a runtime your team already operates.
Server-rendered PHP apps shipped quickly without sacrificing testability.
Cross-platform apps that share the React mental model with the web.
Native iOS where the platform features and polish are the product.
Native Android with first-class Jetpack Compose UIs.
A pixel-perfect single codebase across iOS, Android, and the web.
Hosted LLMs for assistants, copilots, and retrieval-augmented search.
Claude family for long-context reasoning and tool use.
Private-endpoint LLM access for regulated or contract-constrained workloads.
Composable LLM orchestration where the abstractions earn their place.
RAG retrievers, ingestion pipelines, and structured data adapters.
Model training and fine-tuning for the rare cases that justify it.
Vector search alongside your existing Postgres data — without a new datastore.
Declarative SQL transformations with tests, lineage, and CI.
Battle-tested orchestration for batch and dependency-heavy pipelines.
Durable, replayable event streaming for systems that talk in events.
Cloud warehouse with elastic compute and clean separation of storage.
Serverless warehousing where GCP is the rest of the estate.
Lakehouse workloads, especially where ML and analytics share the same data.
Our default operational database — predictable, capable, and well understood.
Document workloads where the access pattern genuinely fits.
Caching, queues, and ephemeral state at low latency.
Full-text and analytics search where Postgres no longer suffices.
Single-table designs for workloads that need predictable AWS-native scale.
Our most-shipped cloud — well-architected landing zones and serverless.
Enterprise estates with M365 and Entra at the centre.
Data-heavy and ML-heavy workloads on BigQuery and Vertex.
Edge runtime, WAF, and zero-trust access at the perimeter.
Production hosting for Next.js apps with first-class previews.
Container orchestration where the operational profile genuinely warrants it.
Reproducible build and runtime images across every environment.
Infrastructure as code for the bulk of our cloud platform work.
IaC in the same languages as the application code, where that fits the team.
Default CI/CD pipeline — fast, deterministic, and reviewable.
GitOps continuous delivery into Kubernetes clusters.
Logs, metrics, traces, and SLOs unified for production operations.
Vendor-neutral instrumentation we standardise on by default.
Stack questions?
A senior engineer will follow up within one business day with an opinionated take on the stack — and the trade-offs that matter for your context.
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