From fintech platforms to internal tools, our senior engineers build the systems your business runs on.
We use AI to accelerate development, with experienced developers directing the architecture, reviewing code and testing how it behaves.
Platforms · Internal tools · Existing systems
- COMPLEX SYSTEMS
Build complex systems.
Handle business logic, data and integrations, with reliability and access controls built in.
- INTERNAL SOFTWARE
Ship internal tools faster.
Turn operational needs into focused software your team can start using.
- EXISTING CODEBASES
Extend what you have.
Add capabilities, connect services and improve existing codebases, including technologies beyond our core stack.
- 01
Understand
Map your processes, data and integrations. Define requirements for performance, access and reliability.
- 02
Design
Choose an architecture and stack that fit the scope, expected load and team maintaining the system — services, serverless functions or a combination of both. Where the domain is complex, we work in hexagonal architecture, DDD and CQRS.
- 03
Build
Develop APIs, services and integrations. Use AI to accelerate implementation, with senior engineers reviewing and testing the work.
- 04
Operate
Deploy, monitor and improve. Track failures, performance and running costs as usage grows.
- APIs and integrations.
- Connect your applications, internal tools and external services.
- Reliable business logic.
- Implement workflows, validation and permissions, with tests for critical paths and edge cases.
- A maintainable codebase.
- Clear structure, documentation and automated checks that support future development.
- Visibility in production.
- Logging, monitoring and alerts to help your team identify and resolve issues.
- A modern stack, kept current.
- Up-to-date frameworks and runtimes, with an upgrade path that keeps the system from becoming legacy.
Data accuracyPermissionsReliable transactions
Our team has built fintech applications where data accuracy, permissions and reliable transactions are central to the product.
We bring that experience to large platforms and smaller internal systems, choosing the engineering each project needs.
AI helps us move faster through implementation and unfamiliar code. Our engineers remain responsible for the decisions and the result.
We start from your requirements — how the system will be used, what it integrates with and who maintains it — and choose technologies that fit that use case.
KotlinJavaPythonGolangNode.jsPHPThe wrong choice gets expensive later — in what the system costs to run, and in the effort every change takes once it’s in production.
Pick the shape the domain needs — microservices, a monolith or serverless functions — and keep business rules independent of frameworks and infrastructure: ports and adapters, domain-driven design, and CQRS where reads and writes grow apart.
Pick the contract the consumer needs — REST for most, GraphQL where clients ask for different shapes of the same data, gRPC between services, sockets and webhooks where something has to be pushed rather than polled — and document it so integrating against it doesn’t take a call with us.
Move work off the request path — a queue where a job just has to happen, an event log where several services need the same fact and a new consumer should be able to replay it, a light bus where latency matters more than durability.
Choose the language and runtime around the system, integrations and team maintaining it.
Structure application logic, APIs and integrations around established frameworks.
Choose data storage around your data model, access patterns and consistency requirements.
AI features are back-end work. They call your systems, read your data and run on your budget, so we build them under the same requirements as the rest of the platform — access control, reliability and cost.
LangChainLangGraphAgnoMCPOpenRouterModels and frameworks move fast, but the bigger cost sits elsewhere: an AI feature that works in a demo is where the engineering starts, not where it ends. Golden datasets, evals and traces built alongside the feature cost a fraction of the same work retrofitted after something has already gone wrong in production.
Run open-weight models on infrastructure you control where the data can’t leave, and reach the hosted providers through one routing layer — OpenRouter by default — where it can, so the model behind a feature stays a configuration rather than an architecture.
Build on established frameworks — a graph where the task has steps, branches and state worth persisting, a lighter runtime where it doesn’t.
Decide what runs as one agent and what splits into several, where a person approves before anything happens, and what a failed or repeated step does to the rest of the run.
Expose your systems to agents through MCP and typed tool calls, with A2A where agents from different teams or vendors have to work together — under the same permissions and audit trail as any other service.
Put your documents and records behind retrieval the model can actually use — vector search where similarity is enough, a graph where the answer depends on how entities connect, another pass where one wasn’t enough and the agent reformulates and looks again — filtered by what the person asking is allowed to see.
Engineer what goes into the context window, not just the wording — it decides how fast an answer comes back, what a run costs and what data left your systems to get it, so we keep prompts tight, cache what repeats and send only what the task needs.
Build the golden dataset and the eval suite with the feature rather than after it — a change to a prompt, a model or a retrieval step then shows up as a number, and a regression shows up before release instead of in support tickets.
We take a language-agnostic approach. We can assess, modify and extend codebases beyond our usual stack, using AI to help understand existing logic and tests to validate changes.
Bring a business problem, a process or an idea.
We’ll help you find the right next step.
A short intro call is often the fastest way
to see what’s worth pursuing.
