Zeta Strategy
Zeta / Production AI systems Reliability · Task performance · Latency · Compute cost

AI systems in production

Evaluate deployed AI systems. Prioritise engineering changes.

Technical assessment / deployed AI system System scope · Evaluation dimensions · Assessment outputs
01 / System scope Deployed system
  • Models
  • Data pipelines
  • Retrieval & tool integrations
  • Workflow orchestration
02 / Evaluation dimensions Measurement criteria
Reliability
Robustness across distribution shifts
Task performance
Output quality & task success
System efficiency
End-to-end latency, compute use & inference cost
03 / Assessment outputs Technical report & engineering plan
  • Measured results & system limitations
  • Ranked changes & validation criteria
Production conditionsInput segmentsUsage patternsTraffic patternsDistribution shifts

01 / About

Zeta Strategy is a technical consultancy for teams operating AI systems in production.

We evaluate deployed systems and turn measured results into prioritised engineering changes.

The unit of analysis is the deployed system: models, data pipelines, retrieval, tool integrations and workflow orchestration.

Purpose
Identify constraints on reliability, task quality, latency and compute cost.
Method
Measure system behaviour across input segments, usage patterns, traffic patterns and distribution shifts.
Output
Produce a technical report and engineering plan with ranked changes and validation criteria.

02 / Areas of work

Evaluate reliability and resource efficiency together.

A

Reliability
& robustness

Standard benchmarks do not always reflect how an AI system performs in production.

We test the deployed system across input segments, operating conditions and usage patterns to identify failure modes and prioritise engineering changes.

Evaluation
Model-level and end-to-end system evaluation
Testing
Distribution-shift and robustness testing
Infrastructure
Evaluation frameworks and production monitoring
B

Task performance
& efficiency

As an AI system adds models, retrieval steps and agent workflows, compute cost and operational complexity can accumulate.

We analyse inference, routing, retrieval and agent steps to identify the components that drive task quality, end-to-end latency and compute cost.

Model serving
Inference cost, model routing and model selection
Orchestration
Agent workflows and retrieval pipelines
Compute efficiency
Numerical methods and compute utilisation

03 / Technical assessment

A scoped technical assessment of a deployed AI system.

It documents current task quality, reliability, latency, evaluation coverage, system architecture and cost structure before follow-on work is scoped.

Review scope

  • 01 Task quality, reliability and latency
  • 02 Evaluation coverage, datasets and metrics
  • 03 Model, retrieval and workflow architecture
  • 04 Inference and workflow cost structure

Output

A technical report and engineering plan.

It documents the evaluation scope, methods and measurements, then ranks proposed changes and defines how each change should be tested.

04 / Principles

Principles used in the work.

01
Evaluate the system that users encounter.Model quality is one component of system quality.
02
Use conditions that reflect production.Inputs, traffic patterns and the consequences of failure determine the evaluation conditions and acceptance criteria.
03
Treat cost as part of system design.Task performance, reliability and compute use are joint system-design constraints.
04
Make recommendations testable.Each recommendation should state the expected change and the evidence needed to assess it.

Contact

For enquiries about evaluating a deployed AI system:

hello@zetastrategy.org