Turn frontier AI research
into working systems.

Who it is for

Founders, CTOs, AI product teams, and investors making a consequential technical decision.

Typical problems

• Which model or architecture fits the product?
• What is genuinely differentiated in a fast-moving landscape?
• Where are the open-source and competitor risks?

Deliverables

• Technical landscape
• Key paper and project review
• Competitor and open-source analysis
• Model and architecture recommendation
• Risk register and next-step plan

Who it is for

Teams with an AI product that cannot yet measure model, agent, or prompt quality reliably.

Typical problems

• Which model is best for the actual workload?
• Are changes improving quality or shifting failure modes?
• What quality, cost, and latency trade-off should ship?

Deliverables

• Custom evaluation set
• Model or system benchmark
• Quality, cost, and latency analysis
• Regression baseline
• Deployment recommendation

Who it is for

Teams building research, support, extraction, or internal knowledge workflows with agents.

Typical problems

• Where should an agent act and where should software remain deterministic?
• How should tools, memory, and recovery be designed?
• How will the workflow be evaluated and observed in production?

Deliverables

• Workflow and system design
• Prototype or production implementation
• Evaluation and observability
• Model and tool integration
• Handover documentation