Languages, frameworks, databases, and cloud platforms in real use across our projects — plus the reasoning we use to pick one over another, which matters rather more than the list.
Discuss your stack{ 01 } — The stack
Everything below runs in live projects. We would rather list less and mean it than pad the page with logos nobody here has shipped to production.
Interfaces that stay fast on a mid-range Android phone on a patchy connection — because that is what most of your users are actually holding.
Frameworks
Styling & UI
State & data
Server-side systems designed for the load you will have in two years, not the load a demo needs today — with the boring parts (auth, jobs, migrations) done properly.
Runtimes & languages
API styles
Async & jobs
The schema decision made in week one is the one you live with for years. We model it before we build on it, and we design for the reporting you will want later.
Relational
Non-relational & cache
Analytics
Cross-platform where it saves you money and native where it does not — a decision we make from your feature list, not from what we prefer to write.
Cross-platform
Native
Mobile services
Infrastructure you can hand to another team without a briefing: defined in code, deployed by pipeline, and observable when something goes wrong at 2am.
Platforms
Delivery
Operations
Applied to problems where it beats a rule — and deliberately not applied where a rule is cheaper, more predictable, and easier to audit.
Models & APIs
Retrieval & agents
Workflow automation
Testing and hardening treated as part of the build rather than a phase at the end, because both cost an order of magnitude more once you are live.
Testing
Security
Compliance-minded work
The measurement layer, installed properly. Most growth problems we are asked to fix turn out to be tracking problems first.
Measurement
Acquisition
Lifecycle
{ 02 } — How we choose
Every agency can publish the same logos. What separates one build from another is the reasoning behind each choice — so here is ours, in the order we apply it.
Core data and money paths run on technology with a decade of production history and a large hiring pool. Novelty is reserved for places where failure is cheap and recoverable.
If your team already runs .NET on Azure, we do not propose a rewrite so we can use a stack we like better. The right choice is the one your people can still maintain in three years.
We avoid architectures that only work on one vendor's proprietary services. Standard databases, containerized workloads, and infrastructure as code mean migrating is expensive, not impossible.
Every added service is another thing to monitor, patch, and pay for. A tool earns its place by removing more complexity than it introduces — otherwise it does not go in.
These tools show up across every service we run — custom platforms, mobile apps, data and AI work, and the growth systems pointed at them. The combination is deliberate: one team that can change the product and the campaign is faster than two that have to negotiate.
If you already know what you need built, start with the service page. If you are still weighing build-versus-buy or a migration, start with a conversation — that is a consulting question, not a stack question.
Book a free consultation call — a senior team member replies within one business day with real thoughts, not a sales script.
From your constraints, in this order: what your team can maintain after handover, what the workload actually demands, what integrates with systems you already run, and only then what we would prefer to write. A stack that is technically elegant but unmaintainable by your team is the wrong stack, however well it benchmarks.
Usually yes, and it is generally the cheaper path. We start by reading the codebase and infrastructure before recommending anything. Where a rewrite genuinely is the right call, we say so with the reasoning and the cost — and we scope it as an incremental migration rather than a big-bang cutover wherever the architecture allows.
Where it beats a deterministic rule — document extraction, search over unstructured content, drafting, classification, support triage. Where a rule is cheaper, more predictable, and easier to audit, we use the rule. We also build the evaluation harness alongside any AI feature, because an AI feature nobody measures is one nobody can improve.
You do, from day one. Repositories, cloud accounts, domains, and CI pipelines are created under your ownership with us granted access. Everything is documented so another team could take over — that is a deliberate design goal, not a courtesy at the end.
It lists what we work with in live projects, not everything anyone on the team has ever touched. If a tool is not here and you need it, ask — the honest answer may be that we would bring in a specialist rather than claim depth we do not have.