Production-grade engineering across three core disciplines. From backend platforms to intelligent AI workflows.
Most software companies reach a point where off-the-shelf tools stop fitting. Your data model is too complex, your throughput is too high, or your integration requirements are too specific. That's when you need a purpose-built platform.
We design and build backend systems from the ground up — event-driven microservices, domain-driven API layers, data pipelines, and the infrastructure to run them reliably at production load. We don't prototype. We ship systems that work under real conditions.
Our approach emphasises clear architecture boundaries, observable systems, and handoff documentation that lets your internal team maintain and extend the work after delivery.
CTOs and VPs of Engineering at product companies that have outgrown their current stack or need to build a net-new backend capability. Ideal for teams who want to move fast without accumulating architectural debt.
Enterprise systems accumulate over time. ERP, CRM, EHR, IoT devices, third-party APIs, legacy databases — each making sense in isolation, none talking to each other cleanly. The result is manual data wrangling, brittle scripts, and reporting you can't trust.
We build integration layers that make these systems coherent — reliable data flows, consistent schemas, and orchestration logic that handles the messy real-world cases. Whether it's connecting medical devices to cloud backends or unifying ten internal tools into one operational view, we've done it.
We work closely with your existing engineering team to understand what's already in place and design integrations that are maintainable, not just functional.
Operations and engineering leaders at companies dealing with fragmented data infrastructure. Common in healthcare, logistics, manufacturing, and enterprise SaaS — anywhere systems have grown faster than integration strategy.
The gap between "using an LLM" and "running reliable AI in production" is significant. Most teams discover this the hard way: hallucinations in critical paths, context windows that don't scale, agents that work in demos and fail in production.
We build AI agentic systems that are designed for production from the start — structured around explicit reasoning steps, grounded in your own data, integrated with your existing tools and workflows, and instrumented so you can see what they're doing and why.
This includes LLM orchestration frameworks, retrieval-augmented generation pipelines, multi-agent coordination, tool-using agents, and the evaluation infrastructure to measure quality over time. We've built these systems in healthcare, enterprise operations, and IoT contexts where the cost of failure is high.
We are model-agnostic and cloud-agnostic — we work with OpenAI, Anthropic, open-source models, and custom fine-tuned deployments depending on what fits your requirements and budget.
Product and engineering leaders at companies that have identified clear AI use cases and need an experienced team to take them to production. We are particularly well-suited to teams in regulated industries where explainability and reliability are requirements, not afterthoughts.
Most projects touch more than one of these areas. Start with a discovery call and we'll scope it together.