01 · Interpretability
Know what the model is doing—and where the evidence stops
Receive a human-readable model report, machine-readable findings, an interactive internal map, and replay-verifiable evidence. We follow real prompts through every measured block, read attention, K/V memory, residual and MLP behavior, trace output-token formation, identify circuits and recurring patterns, and state every denominator, unresolved state, and abstention.
02 · Efficiency
Save on inference
Profile load, prefill, decode, memory, storage, and concurrency. Test caching, batching, operator, and representation changes against an untouched baseline; promote only the savings that preserve the declared output contract under repeated measurement.
03 · Capacity
Right Size
Match model, context window, precision, runtime, hardware, and deployment shape to the work that actually arrives. The recommendation names its quality, latency, throughput, memory, storage, and concurrency envelope.
04 · Developer Experience
Developer Workflows
Give engineers a short path from local prompt to production evidence: reproducible environments, sealed model artifacts, OpenAI-compatible interfaces, evaluation gates, CI checks, observability, and useful failure diagnostics.
05 · Delivery
Bespoke refactoring on tight timelines
Take a narrow, high-stakes subsystem from “we cannot keep living with this” to an explicit contract and a reviewable change. Existing behavior stays protected by characterization tests, small commits, and a deliberate rollback path.
06 · Build
Implementation
Ship the work, not merely the recommendation: architecture, typed interfaces, integrations, tests, deployment manifests, instrumentation, migration, operator runbooks, and acceptance evidence.
07 · Governed Serving
Schemen Runtime
Deploy a policy-enforcement point at the model boundary. Runtime admits an exact model operation only after authenticated-principal binding, request-capability verification, replay consumption, Gate admission, and signed artifact verification.