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Replit intelligent model routing delivered the same tested output quality at 65% lower cost. See the enterprise architecture and caveats.
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Small, instant, invisible.
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You don’t need a frontier model for every task
We build small, specialized AI models so that intelligence can be fast, affordable, and invisible — embedded in the products that already do the work.
Conscious Engines
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Meta Sends 85% of This AI Workload Through Rules, Not an LLM
Conscious Engines
Meta deterministic AI routing sends about 85% of asset classifications through rules and reserves an LLM path with roughly 400x compute cost.
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Siemens AI cost reduction reached 70% after switching global search to Nova 2 Lite, alongside 300% faster search and 11% higher satisfaction.
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Pinterest open-source AI delivers similar task quality at less than 10% of proprietary cost. See the build, adapt and buy model portfolio.
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Sully.ai open-source models cut reported inference cost over 90% and median latency 65% after clinical agents moved away from closed services.
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Decagon voice AI cost fell nearly 6x per turn versus closed models such as GPT-5 mini, with reported p95 model latency below 400 ms.
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Boosted.ai Cut a Nearly $1 Million Model Cost by 90%
Conscious Engines
The Boosted.ai smaller model reduced a nearly $1M annual general LLM cost by a reported 90% while maintaining finance-task quality.
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StudyFetch specialized speech AI cut its largest inference workload cost roughly 10x after a managed transcription bill reached six figures monthly.
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CarbonTrail AI cost reduction reached a reported 88% versus comparable GPT-4 with embeddings, with 87% faster processing and better confidence.
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Forethought Cut Model-Serving Cost Up to 80% by Matching Infrastructure to the Workload
Conscious Engines
Forethought task-specific models used shared and serverless inference to cut relevant serving costs up to 66% and small-classifier cloud costs about 80%.