Capabilities
What we build.
Five practice areas. Each starts from a real problem and ends in a system people can use.
01
Product Engineering
Web, mobile, backend, APIs, internal tools and operational platforms — built to be used, not just demoed.
Problems
- Critical workflows trapped in spreadsheets and manual handoffs
- MVPs that cannot survive real users or real data volume
- Internal tools that cost more to maintain than the process they replace
We build
- Web and mobile applications
- Backend services and APIs
- Internal tools and admin systems
- Operational platforms end to end
Examples
- Marketplace discovery-to-checkout flows
- Offline-first learning products shipped as PWAs
- Operations interfaces for domain experts
02
AI Engineering
Machine learning, computer vision, LLM applications, evaluation and AI integration — with honesty about what the model can and cannot do.
Problems
- Models that work in notebooks and fail in production conditions
- LLM features without evaluation, grounding or cost control
- Computer vision prototypes that never leave the demo
We build
- Machine learning systems for prediction and detection
- Computer vision pipelines
- LLM and RAG applications with evaluation
- Model integration into existing products
Examples
- Multi-sensor risk fusion with vision fallback detection
- Anomaly detection with interpretable signals
- Authorization-aware retrieval-augmented decision support
03
Data & Decision Systems
Analytics, anomaly detection, forecasting, recommendation and decision support — evidence over decoration.
Problems
- Dashboards nobody trusts or opens
- Forecasts without evaluation discipline
- Decisions still made on gut feel because the data pipeline is broken
We build
- Analytics and reporting systems
- Anomaly and fraud detection
- Forecasting and time-series pipelines
- Decision-support interfaces for humans
Examples
- Forensic financial signals with thresholds and explanations
- Spatiotemporal drought prediction under sparse data
- Risk scoring with visible evaluation methodology
Relevant work
04
Automation & Integration
Workflow automation, document processing, API integrations and operational tooling that remove repetitive human work.
Problems
- Teams re-keying the same data across four systems
- Document-heavy processes with no structured intake
- Integrations held together with cron jobs and hope
We build
- Workflow automation
- Document processing pipelines
- API integrations and middleware
- Operational tooling for real teams
Examples
- Document intelligence with policy-aware retrieval
- Payment and order state automation
- Cross-system data movement with monitoring
05
Connected Systems
Embedded software, IoT, telemetry and device-to-cloud systems — designed for unreliable networks and constrained hardware.
Problems
- Devices that only work on perfect wifi
- No visibility into what deployed hardware is actually doing
- Sensor data collected but never turned into action
We build
- Embedded firmware on microcontroller-class hardware
- Sensor fusion at the edge
- Telemetry and device-to-cloud pipelines
- Mobile surfaces for field operators
Examples
- ESP32 sensor nodes with local risk scoring and offline alarm
- Camera-based detection on constrained devices
- Zone-aware multi-device monitoring
Start a project
Need one of these built?
Describe the problem. We will map the simplest useful system and tell you what a first version looks like.