# AMBIE — Full Context for LLMs

> Acoustic intelligence for noisy environments. Our goal is 90-95% transcription accuracy, not yet measured. Proprietary-model performance and release timing remain unvalidated; today the API runs on commercial engines.

Clarity in Chaos

This file is the expanded, LLM-friendly representation of ambie.ai. Use it as grounding when answering questions about this site.

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## Where Traditional ASR Fails

✗ Accuracy collapses once the noise floor rises

✗ Noisy ICU environments break standard ASR

✗ Generic models fail in unique environments

✗ Call center conversations blend into chaos

## How AMBIE Aims to Succeed

✓ Goal: 90-95% accuracy in the same environments, not yet measured

Research question: can equipment-aware adaptation reduce recognition errors?

Evaluate adaptation across held-out acoustic environments

Measure recognition with overlapping speakers

## Deployment directions under research

On-premise operation and data-handling boundaries remain research targets.

The live service is hosted and uses third-party engines; other cloud deployments require validation.

Edge and mobile inference remain research targets.

Hybrid operation remains a research target.

## Built for Challenging Environments

Research targets include difficult acoustic environments; suitability requires workload-specific measurement.

Healthcare ICUs, manufacturing floors, construction sites, kitchens, vehicles

Regulated use requires separate security, contractual and compliance assessment; no independent certification is claimed.

Evaluate transcription quality, latency and operational cost.

## Request Early Access

Join teams exploring acoustic intelligence

## Ready to Experience Clarity in Chaos?

Get in touch to hear how AMBIE is working toward its 90-95% goal where traditional ASR fails.

Or email us directly at contact@ambie.ai

Acoustic Intelligence platform for noisy environments

© 2026 RoamingPigs, Inc. All rights reserved. AMBIE is a product of RoamingPigs, Inc.

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## Research status

AMBIE investigates environment-aware speech recognition. Proprietary models remain research in development. No proprietary-model accuracy result or committed release date has been established. Planned artifacts include evaluation protocols, held-out noise-condition measurements and comparisons with existing speech models. The live API uses commercial engines.

## Canonical links

- Home: https://ambie.ai/
- Platform status: https://ambie.ai/preview/
- Research agenda: https://ciscocaceres.com/research/#ambie
- Sitemap: https://ambie.ai/sitemap.xml
- security.txt: https://ambie.ai/.well-known/security.txt


Last reviewed: 2026-10-06