case study
StudyFetch AI cost reduction reached roughly 10x on its largest workload. See the architecture, verified metrics and enterprise replication plan.
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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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Research, engineering write-ups, and the thinking behind the models we build. We publish what worked and what didn't.
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The Regulated Voice Layer: Safe AI Agents for Banking
Conscious Engines
A technical guide to banking voice AI agents for service, fraud, disputes, collections, vulnerability and controlled actions.
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How construction voice AI turns daily reports, inspections, delays and change evidence into structured project intelligence.
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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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The Multilingual Front Door to Government
Conscious Engines
How multilingual government voice AI can improve citizen access while preserving identity, policy, accessibility and human review.
case study
How India's Bhashini platform built multilingual speech and language infrastructure across 36 text languages, 22 voice languages, and 500 websites.
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Healthcare AI in Production: Eight Named Deployments With Measured Results [Evidence Index]
Conscious Engines
Compare eight healthcare AI deployments across TPMG, SolutionHealth, McLeod, Cleveland Clinic, Providence, UCLA, Inova, and Houston Methodist.
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Explore enterprise AI for healthcare across clinical speech, RAG, operations, imaging, safety and patient service workflows.
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When Generic Speech Models Enter the Clinic
Conscious Engines
Learn how medical speech recognition handles clinical terminology, accents, numbers, workflow integration and safety evaluation.
case study
[Case Study] 2.58 Million Encounters Later: What TPMG's Ambient AI Rollout Teaches Healthcare Leaders
Conscious Engines
How TPMG scaled ambient clinical AI across 2.58 million encounters, what improved, and what healthcare leaders should verify before deployment.