Build
Custom products, platforms, and APIs, engineered to be maintained rather than rewritten.
PRODUCT ENGINEERING · AI · DIGITAL SYSTEMS
We engineer what comes next.
Synthoriq builds digital products, intelligent systems, and modern software platforms for ambitious businesses.
Technologies we work with
- .NET
- Node.js
- React
- Next.js
- Python
- TypeScript
- PostgreSQL
- MySQL
- Microsoft SQL Server
- Azure
- AWS
- Docker
WHAT WE DO
From idea to intelligence.
Four ways we work, depending on where you are.
SERVICES
Engineering across the stack.
Eight areas of practice. Most projects use three or four.
PRODUCT
Product Engineering
From the conversation about why, through architecture and build, to the version that is still maintainable in year three.
AI
AI Engineering
LLM systems that survive contact with production — retrieval, tool use, evaluation, and the logging that lets you explain a decision afterwards.
FRONTEND
React & Next.js Development
Web applications and platforms that load fast enough to rank and stay fast as the codebase grows.
.NET
.NET Engineering
ASP.NET Core services and enterprise APIs, including the unglamorous work of getting a fifteen-year-old system to talk to something built this year.
NODE.JS
Node.js Engineering
Event-driven services and high-throughput APIs, built so that the thing that fails at 3am tells you why.
CLOUD
Cloud & Modernization
Moving a system that works to somewhere it costs less to run, in slices, without a cutover weekend.
HOW WE THINK
Four things we believe, and act on.
We build systems, not deliverables.
A deliverable is finished when it is handed over. A system is finished when somebody else can change it. The second one takes longer to build and costs less to own, and the difference shows up in the second year rather than the first.
Architecture decisions, written down with their trade-offs.
Every significant choice gets a record: what was chosen, why, and what was given up. A decision with no stated cost reads as marketing. Written down, it can be revisited deliberately instead of discovered by accident two teams later.
AI applied where it measurably pays back.
Some of the tasks brought to us are better served by a query, a rule, or a form, and we say so. Where a model genuinely fits, we build the evaluation set before the pipeline — so an improvement is a measurement rather than an impression.
Built to be maintained. Not rewritten in eighteen months.
We assume the team will change, the requirements will move, and we will not always be the ones making the changes. That assumption drives the architecture, the tests, the documentation, and how much cleverness we allow into the codebase.
We don't just ship features.
We build systems that create leverage.
Most agencies optimize for delivery — the date, the scope, the handover. We optimize for what the system costs you eighteen months later, because that is the number nobody quotes for and everybody eventually pays.
AI ENGINEERING
What an AI system actually looks like.
Five stages. The model is one of them.
Input
A request arrives — from a user, a queue, or another system. Structure it before anything else touches it.
Context
Retrieve only what is relevant. Most bad AI output is a retrieval problem wearing a model's clothes.
Reasoning
The model does the part that genuinely needs a model. Everything deterministic stays deterministic.
Tools
The system acts: queries a database, calls an API, writes a record. This is where reliability is won or lost.
Output
A result you can validate, log, and explain to whoever asks.
HOW WE BUILD
Technology is evidence, not the headline.
We are a new company with no published client work yet. Rather than fill this space with logos we have not earned, here is how the work is actually done.
We choose boring where boring works. PostgreSQL unless something specific argues otherwise. A container service before Kubernetes. A scheduled job before an orchestration platform you then have to operate. Every non-obvious choice gets a decision record naming what it cost, because a technology decision with no stated trade-off is a preference wearing a justification.
We build with AI-assisted tooling and say so openly. Cursor and Claude Code run inside the repository; a human reviews every change before it merges. It makes migrations and repetitive work considerably faster. It does not review architecture, and we do not let it decide anything a client is paying us to judge.
None of this is a claim about scale. It is a claim about method, and it is checkable — ask us to walk through a decision record, or to explain why a particular query is slow. That conversation tells you more than a logo strip would.
- Frontend
- ReactNext.jsTypeScript
- Backend
- .NETNode.jsPython
- Data
- PostgreSQLMySQLMicrosoft SQL ServerRedisVector search
- Cloud
- AzureAWSVercelDockerKubernetes
- AI
- OpenAIAnthropicLangChain
- Engineering workflow
- CursorClaude Code
HOW WE WORK
Six stages, no surprises.
The same sequence every time, so you always know what happens next and what you get at the end of it.
Discover
Understand the problem before proposing a shape for it.
You getA written problem statement, with what is out of scope named explicitly.
Define
Agree what version one is, and what it deliberately is not.
You getA scoped specification and a delivery sequence you have signed off.
Architect
Choose the shape of the system, and write down why.
You getArchitecture decision records, a data model, and a cost estimate for running it.
Build
Ship a thin working path first, then thicken it.
You getWorking software in a real environment, updated every cycle.
Validate
Prove it survives production, not just a demo.
You getTest results, a tested restore, and monitoring wired to a real destination.
Evolve
Hand over properly, then keep improving it.
You getDocumentation, recorded handover sessions, and an agreed support rhythm.
INDUSTRIES
Where we work.
Different sectors, mostly the same underlying problems: systems that grew faster than they were designed for.
- SaaS & TechnologyProduct companies past first traction, where the platform work now competes with the roadmap.
- Manufacturing & OperationsProduction environments where the system of record and the actual process stopped matching.
- EducationStudent, learning, and administrative systems where records must stay accurate across a whole academic cycle.
- FinanceSystems where a number has to be reproducible and every change has to be explainable.
- Real EstateProperty, tenancy, and transaction processes still coordinated across inboxes and portals.
- LogisticsMovement and fulfilment operations where visibility gaps become customer service work.
- Professional ServicesFirms whose margin depends on utilisation, billing accuracy, and how long a document takes to find.
- Not listed?The problems repeat across sectors more than the sectors differ. Tell us what your system does and we will tell you what it resembles.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.