Backend engineering · AI infrastructure

Production-grade backends for AI systems that can't afford to fail.

We build the backend systems, integrations, and infrastructure that connect AI models to real-world products. Across healthcare, fintech, and robotics, we help teams turn promising prototypes into secure, reliable production platforms.

Talk to an engineer See what we build
fig.01 / system_layer sustained throughput
01  /  problem The problem we solve

AI demos are easy to launch. Production AI is where things break.

A working model is only one part of the system. Real products need secure data access, reliable infrastructure, audit trails, orchestration, observability, fallback paths, and deep integrations with existing systems.

That is the layer we build.

Our team has spent more than 15 years shipping backend systems for companies where reliability, security, and performance are not optional.

We've watched too many promising prototypes stall at the production boundary because the team that built the model wasn't equipped to build the system around it.

We come in to close that gap — fast, and without the technical debt that makes the next year painful.

02  /  practice What we build

Three areas where the gap between prototype and production is widest.

02.01→
// robotics

Fleet backends for drones and robotics

We build real-time backends for drone fleets and autonomous robotics platforms.

Our work includes flight planning, fleet coordination, telemetry pipelines, live video streaming, simulation environments, and synthetic data generation for computer vision training.

We work alongside hardware, autonomy, and CV teams to deliver the orchestration and operator-facing layers that turn drones and robots into reliable production platforms.

02.02→
// healthcare

Healthcare and clinical AI platforms

We build clinical platforms that combine computer vision analysis, AI-assisted decision support, and secure healthcare workflows.

Our work includes end-to-end encryption, audit logging, role-based access control, and HIPAA-ready infrastructure design from day one — so teams don't have to retrofit security and compliance after the product is already built.

02.03→
// integration

AI integration into production systems

The demo works. Production does not. That gap is one of the most common failure points in AI products. We help close it.

We integrate LLMs, RAG systems, and agentic workflows into existing products with proper authentication, RBAC, audit trails and cost controls.

We also build retrieval over data stored in compliance-bound systems and connect model inference to applications with real latency, uptime, and reliability requirements.

The stack

languages · data · infra · ai · domain
TypeScript Node.js C Rust PostgreSQL MySQL MongoDB Couchbase Kafka RabbitMQ Redis Docker Kubernetes GCP AWS OpenAI Anthropic LangChain LangGraph LlamaIndex RAG ONNX Vector stores YOLO MAVLink ArduPilot PX4 SITL AirSim EMR
03  /  engagement How we engage

Three practical ways to work with us.

We also scope custom engagements when your project needs something more specific. These are the most common starting points.

03.01 · A // review

Architecture review

2–3 weeks · fixed price

A senior architect reviews your existing system, identifies risks and bottlenecks, and delivers a written architecture assessment with prioritized recommendations.

// best for Teams preparing for scale, production launch, security review, or a major technical decision.
03.02 · B // build

Prototype build

6–8 weeks · fixed price

We build a working prototype with real code, deployable infrastructure, and an architecture that can grow beyond the first demo.

// best for Teams that need to prove a product direction quickly without creating technical debt from day one.
03.03 · C // embed

Embedded engineering team

Ongoing · monthly

A senior engineering pod plugs into your organization as a focused delivery team. Depending on the project, that can include architecture, backend, frontend, QA, data science, and platform support.

// best for Teams that need senior execution without slowing down to hire and manage a full internal team.
04  /  team Why this team

Seven senior engineers. Fifteen years shipping together.

We are not a staffing agency, and we are not a large outsourcing shop. Clients work directly with senior people who understand architecture, production constraints, and what it takes to ship reliable software in complex environments.

That continuity is the point. Fifteen years of working alongside the same engineers means we have a shared instinct for where systems break, where to invest early, and where to leave the seams clean.

07
senior engineers · no juniors, no agency layer
15+
years together · across multiple companies
03
regulated domains · robotics · healthcare · fintech