Kueizen Optimize
Use-case-specific neural routing that reduces AI costs by up to 90%
Powered by GOLEM (Genetically Optimized Language Ensemble Model)
The Problem
AI costs are exploding as usage scales.
What started as a manageable API bill in the prototype phase is becoming your company's largest infrastructure expense. As you scale to millions of users, the linear cost of inference eats directly into your margins, making unit economics unsustainable.
You're paying frontier model prices for tasks that don't need them.
Sending every simple greeting, data extraction, or classification task to the smartest, most expensive model in the world is an inefficiency that you cannot afford. It is like hiring a team of PhDs to do data entry.
Manual model evaluation is time-consuming and quickly outdated.
By the time your team finishes benchmarking the latest model, three more have been released. Keeping up with the state of the art requires a dedicated team just to test prompts against new checkpoints, distracting you from building product features.
Generic routing doesn't understand YOUR specific use case.
Routers trained on public benchmarks like MMLU or GSM8K fail to capture the nuances of your proprietary data and edge cases. Your customers don't ask standardized test questions; they ask about your specific products and services.
How Kueizen Optimize Works
Define your use case
We begin by analyzing your specific traffic patterns. Whether you are running high-volume customer support agents, complex code generation pipelines, or creative writing tools, we characterize the actual queries your users are sending. We look at the distribution of complexity, length, and intent to understand your baseline.
Synthetic data generation
We don't rely on generic benchmarks. Instead, we generate thousands of realistic, domain-specific test scenarios that mirror your actual production traffic. This creates a rigorous ground-truth dataset tailored exactly to your unique business logic and requirements. We use advanced techniques to ensure these synthetic examples cover edge cases that generic datasets miss.
Multi-model evaluation
We test your specific prompts against a massive matrix of models—ranging from efficient 8B parameter models to expensive frontier models like GPT-5.2 and Claude Opus 4.5. We meticulously measure quality, latency, and cost for every single permutation to map the performance landscape. This data forms the "complexity surface" that allows us to see exactly where smaller models fail and where they succeed.
Neural router training
We train a lightweight classifier that learns to predict which model can handle a specific query with the required quality. This router learns the "complexity signature" of your tasks, intelligently sending simple queries to cheap models and complex ones to capable models. This is not a static rule-based system; it is a neural network trained on your specific data distribution.
Prompt optimization
Routing is only half the battle. We automatically rewrite and optimize prompts to maximize the performance of each target model. This enables smaller, cheaper models to punch above their weight class and match frontier model quality on your specific tasks. We use evolutionary algorithms to find the optimal prompt structure that elicits the best response from less capable models.
Deploy
We deploy a production-ready neural router to your infrastructure. It operates with <20ms latency overhead and serves as a drop-in replacement for the OpenAI SDK—simply change your base URL and immediately start seeing cost reductions. The router is highly optimized for throughput and adds negligible latency to your request pipeline.
Key Capabilities
Use-case specific routing
We don't optimize for the average; we optimize for you. Our routing logic is derived from your actual data, not generic internet text. This ensures that the router respects your specific quality definitions.
Combined prompt + model optimization
We treat the prompt and the model as a coupled system, optimizing both simultaneously for maximum efficiency and performance. This holistic approach yields gains that neither technique can achieve alone.
Near-zero latency architecture
Our optimized router runs in your stack with <20ms overhead, which is faster than the standard variance of network requests. We prioritize speed in the serving layer so you never have to trade latency for cost.
Continuous learning
The AI landscape moves fast. Our system adapts as new models are released, ensuring you are always operating on the efficient frontier. We constantly re-evaluate new open and closed weights to see if they offer better performance per dollar.
Use Cases
Customer support agents
Automatically route tier-1 queries and FAQs to fast, cheap models while reserving expensive frontier models for complex escalations and nuanced sentiment handling. This drastically reduces the cost per ticket.
Appointment booking systems
Utilize specialized small models for entity extraction, date parsing, and slot filling at a fraction of the cost of large generalist models. These tasks are deterministic and well-suited for smaller, faster engines.
Document processing pipelines
Process millions of pages for summarization and data extraction using efficient models tuned for reading comprehension. Scale your RAG pipelines without breaking the bank.
Code generation workflows
Route simple boilerplate completion and syntax fixes to 7B models while sending architectural planning and complex debugging queries to frontier models. This gives developers speed where they need it and intelligence where it counts.
Content generation at scale
Generate SEO content, product descriptions, and marketing copy using models specifically tuned for your brand voice and style guidelines. Maintain high volume without high costs.
The Technology
Kueizen Optimize is built on a foundation of deep research, not thin wrappers.
Proprietary optimization framework
Built on 10+ years of R&D in evolutionary algorithms and multi-variable solving. This framework, originally designed for complex system simulation, is now applied to the high-dimensional space of language model performance.
Neural architecture
Designed specifically for production latency requirements, ensuring our router never becomes a bottleneck in your stack. We use distilled models that run on CPU or low-memory GPU instances for maximum efficiency.
Ensemble model evaluation
Leverages the power of diversity by systematically testing and routing across different model families and sizes. We believe that an ensemble of specialized models will always outperform a single generalist model.
Built on the Mothership framework
Utilizes our battle-tested generative solver to navigate the complex trade-off space between cost, speed, and quality. Mothership applies nature-inspired heuristics to find optimal solutions in vast search spaces.
Integration
Guided onboarding process
We walk you through characterizing your traffic and defining your quality gates. Our team works with you to ensure the synthetic data accurately represents your needs.
API-based integration
Integrates cleanly into your existing backend with standard API patterns. No need to rewrite your application logic.
Works with your existing model providers
Keep your own keys and relationships with OpenAI, Anthropic, and other providers. We sit in the middle as an intelligent routing layer.
Drop-in SDK compatibility
Minimal code changes required—often just a configuration update to point to the Kueizen endpoint. You can be up and running in minutes.