AI systems for teams that ship.
We work with SaaS teams and product groups across regulated and unregulated domains. The common thread is a real engineering problem with AI as part of the answer.
Where the work tends to land.
We are not industry-locked. The list below reflects where AI-native engineering has the highest leverage right now.
Healthcare
HIPAA-conscious platforms with audit-grade trails, AI triage flows, and clinician-in-the-loop discipline.
- FHIR adapters
- Guardrailed LLMs
- Audit-first data
Fintech
Risk and compliance systems where AI sits inline — fast enough for checkout, explainable enough to defend.
- Streaming features
- Hot-path inference
- Decision logs
E-commerce
Headless commerce with semantic search, reranking tuned to the catalog, and operator tooling that does not require code.
- Headless commerce
- Hybrid retrieval
- Edge rendering
Industrial
Asset graphs, time-series models, and signal-attribution interfaces for messy real-world telemetry.
- Asset graph
- Time-series ML
- Plant-floor adapters
SaaS
AI features inside SaaS products with tenant boundaries that hold all the way through retrieval, prompts, and tools.
- Multi-tenant retrieval
- Per-tenant evals
- Scoped tool use
Enterprise
Knowledge platforms, internal copilots, and workflow intelligence that respect the data governance you already have.
- Internal RAG
- Workflow intelligence
- Governance-aware
Education
AI surfaces that personalise without flattening — content sequencing, adaptive feedback, conversational tutoring.
- Adaptive sequencing
- Conversational AI
- Content pipelines
Logistics
Real-time routing, predictive ETAs, and exception handling tuned to the messy data the warehouse actually emits.
- Routing models
- Predictive ETAs
- Anomaly detection
Problems we solve for.
Common shapes of work across the engagements we run. If yours rhymes with one of these, we are probably a good fit.
Patient data scattered across systems
Unified portal with adapter-based integration and AI-assisted triage under audit-grade trails.
Manual or batch-based fraud signals
Streaming features with hot-path inference, deterministic fallbacks, and decision-log explainability.
Generic search and weak personalisation
Hybrid lexical + vector retrieval with reranking tuned to the catalog, surfaced through edge rendering.
Unexpected downtime on heterogeneous assets
Asset graph plus time-series models, with signal-attribution UI for operators rather than black-box scores.