Indian financial conversations rarely stick to one language. A customer might start in Hindi, slip into English for “EMI” or “foreclosure charges,” and finish in a regional dialect — all on a noisy phone line. Blue Machines AI says its new model, Aurora, is built precisely for that chaos.
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What Makes Aurora Different
Launched on September 7, 2026, in Bengaluru, Aurora is a multilingual speech-to-text model designed specifically for Banking, Financial Services, and Insurance (BFSI) conversations. Unlike general-purpose transcription tools, it’s trained on financial vocabulary — EMIs, disbursals, KYC, premiums, SIPs, NAVs, and more — plus the exact entities (amounts, policy numbers, transaction IDs) that actually drive banking workflows.
Blue Machines AI: Benchmark Numbers
| Metric | Result |
|---|---|
| English Semantic WER | 1.51% |
| Hindi BFSI Semantic WER | 2.43% |
| Multilingual Semantic WER | 5.52% |
| BFSI Entity Error Rate | 4.23% |
| P50 Latency | 236 ms |
| Concurrent streams (per H100, 320ms) | 960 |
| Concurrent streams (per H100, 1.12s) | 2,400 |
These figures come from Blue Machines AI’s internal benchmarking against representative BFSI datasets covering banking, lending, insurance, and collections calls in Indian English, Hindi, Hinglish, and code-mixed speech.
Blue Machines AI: Built on a Streaming Architecture
Aurora runs on a cache-aware FastConformer encoder paired with a streaming transducer decoder, a setup akin to the broader class of models used in modern automatic speech recognition systems, but tuned here for incremental, low-latency processing. This lets it retain conversational context while transcribing speech in real time — critical for high-concurrency call centers.
The company also offers customer-specific retraining using enterprise data, letting institutions teach Aurora their own product names, accents, and terminology. Internal tests show this can cut recognition errors by 40–45% relative to the base model.
Where It Fits
Aurora plugs into Blue Machines AI’s broader CX platform, supporting customer journeys across acquisition, onboarding, lending, collections, servicing, and claims. It’s deployable on managed cloud, inside an enterprise VPC, or fully on-premises — giving BFSI players control over data residency and compliance.
“India’s financial conversations do not happen in a single language or follow a standard script,” said Nirmit Parikh, Founder and CEO of Blue Machines AI, adding that misreading a policy number or EMI amount can directly affect customer outcomes.
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FAQs
Q1. What is Aurora built for?
Aurora is a speech-to-text model designed specifically for multilingual, code-mixed BFSI conversations in India, covering banking, lending, and insurance use cases.
Q2. How accurate is Aurora compared to other models?
In internal benchmarks, Aurora recorded a 1.51% English and 2.43% Hindi Semantic WER, with 236 ms latency, reportedly outperforming leading general-purpose ASR models.
