Context pruning
Select the passages needed for the current task, with provenance attached.
Token optimization
Make every token count.
Shape context, reuse stable inputs, and route work deliberately. Optimize consumption without losing sight of response quality.
Token optimizationConcept architecture
SELECTED STAGE / 01
Source context
Define the inputs and access boundaries before execution begins.
WHAT WE DESIGN
A focused approach to token optimization.
Semantic caching
Identify stable inputs that can be reused safely and define invalidation rules.
Model routing
Choose model capacity based on the task and verify the quality tradeoff.
THE ENGAGEMENT
From diagnosis to a testable design.
A context and routing strategy with an evaluation plan for your representative workloads.
- 01
Map the workload
Review representative tasks, dependencies, and constraints.
- 02
Design the intervention
Define responsibilities, boundaries, and expected behavior.
- 03
Validate the result
Evaluate against the agreed quality and operational objectives.
Questions worth asking.
Does this require replacing our model or infrastructure?
We start with your current system. The design may improve the surrounding harness without changing the model. Any infrastructure change should follow from your requirements and evaluation results.
How do you judge whether an optimization works?
Use representative workloads and agreed acceptance criteria. Compare response quality, token consumption, latency, and operational behavior together. No universal improvement percentage is promised.
Can human review remain part of the process?
Yes. Approval gates, reviewer evidence, and explicit revision paths can be designed into the execution lifecycle.
LET’S WORK THROUGH IT
Bring us your workload.
Start with your requirements, data, and definition of a good result.