A clear decision.
A working handover.

Who it is for

Founders, CTOs, AI product teams, and investors choosing a technical direction before committing engineering time or compute.

Typical decisions

• Which model or architecture fits our workload and constraints?
• Does the evidence support investing in RL or another post-training method?
• What is differentiated, and what can we build with existing open-source tools?

What you take away

A technical decision your team can defend.

• Technical landscape
• Decision memo
• Risk register
• Experiment plan

Who it is for

AI product and engineering teams with a working prototype or live product, but no reliable way to measure regressions and choose between models.

Typical decisions

• Which model performs best on our actual customer tasks?
• Did the new prompt improve quality or just change the failure modes?
• What quality, cost, and latency trade-off should we ship?

What you take away

A benchmark your team can run again.

• Evaluation set & rubric
• Repeatable benchmark
• Failure analysis
• Release recommendation

Who it is for

Teams building a specific research, support, extraction, or internal knowledge workflow with access to the tools and data it needs.

Typical decisions

• Which steps need an agent, and which should be deterministic?
• How should tools, memory, permissions, and recovery work?
• How will we measure success and investigate failures after launch?

What you take away

A working workflow with an operating handover.

• Workflow & system design
• Agent implementation
• Evaluation & observability
• Operating handover