Today we are launching OrchestrAI, the first product in the Bolor Intelligence platform, and it solves a problem that every team running AI in production faces: you are paying too much for model inference because you are routing every query to the same model. A simple factual lookup gets the same expensive GPT-4 treatment as a complex multi-step reasoning task. A yes-or-no classification uses the same resources as a nuanced analysis. This is not just wasteful — it actually hurts your latency and throughput for the simple cases that could be handled in milliseconds.
OrchestrAI is an intelligent routing layer that sits between your application and your model providers. When a query comes in, OrchestrAI analyzes its complexity, intent, and requirements in real time and routes it to the optimal model or reasoning approach. Simple factual queries go to fast, cost-effective models. Complex reasoning tasks go to deep-thinking models. Queries that require factual accuracy get routed through knowledge-graph-backed symbolic reasoning. And queries where consensus matters get sent to multiple models simultaneously with our compare endpoint.
The routing engine uses a lightweight classifier that evaluates query complexity across several dimensions: linguistic complexity, reasoning depth required, domain specificity, and ambiguity level. This classification happens in under 10 milliseconds and adds negligible overhead to the total request time. Based on the classification, OrchestrAI selects from your configured model pool. You define the models you want available, set cost and latency constraints, and OrchestrAI handles the rest. If your primary model is unavailable or returns low confidence, fallback chains automatically escalate to the next model in your defined hierarchy.
The compare endpoint is particularly powerful for high-stakes decisions. Send a query to multiple models simultaneously and get back ranked responses with individual confidence scores, a consensus analysis, and a recommended best answer. We have seen teams use this for legal document analysis, medical triage decisions, and financial risk assessments where getting a second opinion from a different model architecture provides meaningful signal about answer reliability.
In our beta program, design partners saw cost reductions between 35% and 62% depending on their query distribution. The biggest savings came from teams with high volumes of simple queries that were previously all going to expensive models. One design partner was sending 80% of their queries to GPT-4 when only 15% of those queries actually required that level of reasoning capability. After deploying OrchestrAI, those simple queries were routed to faster, cheaper models with identical output quality, while the truly complex queries continued to get the deep-reasoning treatment they needed.
Beyond cost savings, teams reported meaningful improvements in average latency. Simple queries that previously waited in GPT-4 queues now complete in 50-200 milliseconds through fast models, while complex queries still get the time they need. The net effect is that the overall user experience improves because the majority of interactions feel instant, while the minority of complex interactions maintain their quality.
We built OrchestrAI to be model-agnostic. It works with OpenAI, Anthropic, Google, open-source models, and any custom model endpoints you want to add. The routing logic is based on query characteristics, not model-specific features, so you can swap models in and out of your pool without changing your application code. This also means you are never locked into a single provider — OrchestrAI gives you automatic failover across providers if one experiences an outage.
Getting started takes about five minutes. Sign up for a Bolor Intelligence account, configure your model pool with the API keys for your preferred providers, set your cost and latency constraints, and point your application at the OrchestrAI endpoint. We handle the routing, load balancing, failover, and monitoring. You can start with our free tier, which includes 100 routed requests per hour, and scale up as your usage grows.