What We Do
AI engineering from architecture to production.
Applied AI
End-to-end AI product development, from prototype to a system real users depend on.
- A prototype works in a demo but breaks under real usage and edge cases.
- Internal teams don't have the bandwidth to take an AI feature from idea to shipped product.
- Point solutions don't generalize past the first use case they were built for.
- LLM application design
- Prompt and context engineering
- Multimodal AI
- Production hardening
- Human-in-the-loop UX
- A production-grade AI feature shipped to real users
- An internal prototype hardened into a supportable system
Agentic AI
Autonomous and human-in-the-loop systems that reason, use tools, maintain state, and complete multi-step work.
- A single-turn model call can't complete work that spans multiple steps and tools.
- Long-running tasks need to survive restarts, failures, and human review without losing progress.
- Autonomy without oversight is a liability in regulated or high-stakes workflows.
- Agent orchestration
- Tool use
- Planning
- State management
- Human approvals
- Persistent workflows
- A multi-step agent that completes real work with defined approval gates
- Workflow infrastructure that resumes cleanly after failure
Machine Learning
Production ML systems spanning data, modeling, inference, deployment, and evaluation.
- A model that performs well offline doesn't hold up once it's serving production traffic.
- Feature and data pipelines break silently, and nobody notices until output quality drops.
- There's no reliable way to evaluate model changes before they ship.
- Model development
- Inference
- MLOps
- Feature pipelines
- Evaluation
- A model pipeline with monitored inputs, outputs, and drift
- An evaluation harness that gates releases on measured quality
AI Platforms
Shared infrastructure for model access, retrieval, orchestration, observability, and governance.
- Every team is building its own model access layer, retries, and logging from scratch.
- There's no shared visibility into cost, latency, or failure across AI systems.
- Governance and security review can't keep pace with how many systems now call a model.
- Model gateways
- Orchestration
- Observability
- Evaluation
- Security
- Platform APIs
- A single model gateway used across every internal AI system
- Shared tracing and evaluation infrastructure with per-team visibility
Enterprise AI
AI systems grounded in enterprise context, permissions, workflows, and business knowledge.
- Answers need to be grounded in internal knowledge, not general model training data.
- Retrieval has to respect the same permissions as the underlying systems it reads from.
- AI features need to integrate with the tools the business already runs on.
- RAG
- Enterprise search
- Knowledge systems
- Permissions-aware retrieval
- Enterprise integrations
- A knowledge assistant grounded in live internal documentation
- Permissions-aware retrieval enforced at the query layer