We build the AI.
Not just the app.
We design and train the whole system ourselves, natively for India — with the Emotion Engine running across every stage of the conversation. Deploy our models or bring your own. Either way, your data never crosses your boundary.
Two ways to run the conversation.
The cascade is the more capable system: it reaches every channel, acts on what it hears, and leaves a record. Speech-to-speech is the more natural one. Both run on your hardware — you pick per deployment, not per vendor.
Cascade
Speech becomes language, language becomes action, action becomes speech. Because there is a language layer in the middle, the same deployment serves every channel and can act on what it hears — not just answer it.
- One brain, every channel — voice, WhatsApp, web chat and in-app share the same models and the same memory
- Multi-modal: reads documents and forms, returns structured data, not only speech
- Calls tools and systems — books the appointment, checks the EMR, updates the CRM mid-conversation
- Full transcript every turn, so the conversation is auditable and searchable after the fact
- Guardrails, redaction and PII masking apply at the language layer, where they can be enforced
- Components stay swappable — run our models, or bring your own LLM behind the same pipeline
Anything that has to act, integrate, span channels, or be logged
Speech-to-speech
The model answers in speech directly. Tone, hesitation, emphasis and timing carry through the exchange instead of being flattened at a text boundary — so the conversation sounds like one, rather than like a system taking turns.
- Prosody and emotion survive the round trip, in both directions
- Interruptions and barge-in land naturally — the caller can talk over it
- Shorter gap between speaking and being answered
- Available for on-premise deployment, not only as a hosted API
Voice-first conversations where how it sounds is the product
Three models, trained together rather than assembled from third-party parts.
LLM
- Fine-tuned for Indian business, healthcare, and financial contexts
- Multi-turn dialogue with Indian-language code-switching
- Hinglish + 13 other regional languages in one model
- Domain-specific: medical, NBFC collections, EdTech admissions
- No external model provider in the critical path
TTS
- 14+ Indian languages with natural prosody
- Not English text transliterated. Natively trained voice.
- Regional accent variants per language
- Low-latency streaming for real-time voice agents
- Emotion and emphasis control
ASR
- Trained on licensed and consented Indian call-centre and clinical audio
- Hinglish code-switching handled natively
- Speaker diarisation for doctor–patient conversations
- Streaming transcription, tuned for interactive latency
- Works on noisy OPD and NBFC call environments
Two deployments. Only one stays inside your walls.
Cloud-routed AI (any model)
Your application
External cloud API — your data crosses your boundary
Data egress risk · DPDP & HIPAA exposure · Vendor lock-in
AntEngage on-premise
Your application
AntEngage proprietary models, or your preferred LLM, on your infrastructure
DPDP compliant · Zero data egress · Predictable cost
Problems we're solving.
We don't have all the answers yet. Here's what we're actively working on.
Low-resource Indic-language ASR
Extending ASR coverage to languages with limited training data: regional dialects, tribal languages, and endangered Indic scripts. Focusing on dialectal variation and accent robustness.
On-device / on-premise inference
Optimising our models for inference without cloud dependency. Running full LLM + TTS + ASR pipelines inside hospital hardware or bank infrastructure, with no API egress.
Domain-specific fine-tuning
Medical terminology alignment for NABH protocols, NBFC regulatory language for collections scripts, EdTech admission dialogue flow. Building domain-specific adaptors.
Natural Indian prosody in TTS
Solving for natural speech rhythm in Indian languages, not just phoneme accuracy. Regional intonation patterns, sentence stress, and emotional register.
Duplex turn-taking in speech-to-speech
Knowing when a speaker has finished, when an interruption is meant, and when a pause is just thinking. Hard in any language, harder across Indic code-switch where prosodic cues differ from the English-centric data most turn-taking work is built on.
Contributions to the Indian AI community.
One release published, the rest dated only when they ship. Status is stated per item rather than promised in general.
empathy-conversations
4,010 multi-turn dialogues centred on emotional support, with a stated limitations section. Mirrored on Hugging Face, Zenodo, Kaggle, Harvard Dataverse, Mendeley Data and the Internet Archive.
Indian-language intent classification dataset
A labelled dataset of customer-support intents across Healthcare, NBFC, EdTech, and D2C verticals, in Hindi, Kannada, Tamil, and Telugu.
Indic ASR benchmark
A standardised benchmark for evaluating ASR accuracy on Indian accents, regional dialects, and Hinglish code-switching.
Medical dialogue fine-tuning tutorial
An open-source guide to adapting language models for Indian clinical dialogue, worked through on consented, de-identified material.
14+ Indian languages. Natively trained.
हिंदी
ಕನ್ನಡ
தமிழ்
తెలుగు
मराठी
Hinglish
বাংলা
ਪੰਜਾਬੀ
ଓଡ଼ିଆ
ગુજરાતી
മലയാളം
অসমীয়া
اردو
Sindhi
Three reasons to own your AI stack.
Compliance
DPDP, RBI, HIPAA, NABH. When data never leaves your building, compliance is architectural, not procedural.
No API cost explosion
Cloud-provider pricing scales with usage. On-premise means predictable, fixed infrastructure costs.
No vendor lock-in
We don't depend on any external model provider. You don't depend on us for the model, only for the deployment.