What We Optimize
Most teams leave significant performance and cost improvements on the table after their initial AI deployment. We systematically find and capture those gains across four dimensions.
Prompt Engineering
Structured prompt design, few-shot example selection, chain-of-thought patterns, and systematic evaluation. We turn ad-hoc prompting into a repeatable engineering discipline.
Pipeline Efficiency
Caching strategies, parallel execution, batch processing, and redundant call elimination. We reduce end-to-end latency without sacrificing output quality.
Token Cost Reduction
Model selection by task complexity, context window management, response length tuning, and intelligent routing between expensive and lightweight models.
Output Quality
Evaluation frameworks, regression testing, structured output validation, and feedback loops. We make quality measurable so improvements are verifiable.
Optimization Cycle
Baseline
Instrument cost, latency, quality
Identify
Find the 20% driving 80% of cost
Experiment
Test prompts, models, configs
Deploy
Roll out wins with monitoring
Baseline
Instrument cost, latency, quality
Identify
Find the 20% driving 80% of cost
Experiment
Test prompts, models, configs
Deploy
Roll out wins with monitoring
AI Optimization Services
Our Approach
Optimization starts with measurement. Before changing anything, we instrument your existing AI workflows to establish baselines for cost, latency, accuracy, and user satisfaction.
Baseline and instrument. We log every LLM call: model used, token counts (input and output), latency, and a quality score derived from your success criteria. This data set becomes the foundation for every decision that follows.
Identify the high-impact targets. Not every call is worth optimizing. We rank your AI workflows by total spend and frequency, then focus on the 20% of calls that drive 80% of your costs. A prompt that runs 10,000 times per day at $0.02 per call is worth more attention than one that runs twice a week at $0.50.
Test systematically. We run controlled experiments: alternative prompts, different models, adjusted parameters. Each variant is evaluated against the baseline using your quality criteria, not ours. We do not ship changes that trade accuracy for cost savings unless you explicitly approve the tradeoff.
Deploy and monitor. Winning configurations are rolled out incrementally with automated rollback triggers. We set up ongoing monitoring so you catch regressions before your users do.
Typical Results
Results vary by starting point, but these ranges reflect what we see consistently across engagements.
- 40-60% reduction in token costs through model routing, prompt compression, and caching. The largest gains come from routing simple classification tasks to smaller models while reserving large models for complex generation.
- 2-5x improvement in pipeline throughput by parallelizing independent calls, batching where APIs support it, and eliminating sequential bottlenecks. Many pipelines are accidentally serialized due to early prototyping decisions that were never revisited.
- 15-30% improvement in output quality measured by task-specific evaluation criteria. Better prompts, structured outputs, and validation layers catch errors that previously reached end users.
- Evaluation frameworks that persist beyond our engagement. Your team gains the tooling and process to continue optimizing after we leave. This is often the most valuable deliverable.
When To Optimize
Optimization is not always the right next step. Here is how to tell whether your situation calls for optimization or something else entirely.
Optimize when you have a working system that costs too much or runs too slowly. The core logic works. Users get value. But your LLM spend is growing faster than revenue, or latency is degrading the experience. This is where optimization delivers the highest ROI.
Do not optimize when the system does not work yet. If output quality is fundamentally poor, the problem is usually architecture or data, not prompt tuning. We will tell you that honestly. An optimization engagement on a broken system wastes your money.
Consider optimization before scaling. If you are about to increase usage 10x, optimizing first means you scale a lean system instead of a wasteful one. The savings compound.
Not sure which category you fall into? Send a note to ben@oakenai.tech with a brief description of your current setup. We will give you an honest assessment of whether optimization is the right investment right now.