NLP & Conversational AI
We design conversational and NLP systems that go beyond intent classification, handling ambiguity, escalating gracefully and respecting the language of your industry.
Conversations that respect the user and the language.
Generic chatbots fail in the same predictable way: they assume a tidy conversation in a tidy language. Real users speak in fragments, switch languages mid-sentence and ask questions the FAQ never anticipated. Our approach is to design for that messiness from day one, with intent + entity models tuned to your domain, multilingual support that respects local idiom and graceful escalation paths for when the bot is genuinely the wrong tool.
Domain-tuned, not generic
NER and intent models are trained on your transcripts and terminology, not a generic SaaS classifier from 2019.
Multilingual without translation
Native models for 30+ languages including Indic and SEA, not English models with Google Translate stapled on.
Designed for handoff
When confidence drops, the agent surfaces a human cleanly, with full context. Bots and people, not bots vs people.
Why conversational AI just got real (again).
A decade of intent-classifier chatbots taught customers to dread the words "talk to our virtual assistant." LLMs flipped the economics and the experience.
Across our deployments, swapping a 2019-style intent classifier for an LLM-backed agent moves CSAT measurably, because the bot finally handles the messy 30% of conversations.
The trajectory is unambiguous. Teams that ship voice and chat AI now own their support cost curve; teams that don't, don't.
When the bot is properly tuned to the domain, deflection rates that used to be aspirational become a Q1 outcome.
NLP & Conversational AI services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Intent + entity extraction
Domain-tuned NER and intent classifiers, for finance, healthcare, retail, telecom, out-performing generic SaaS bots on the metrics that matter.
Voicebots with sub-second latency
Streaming STT → reasoning → streaming TTS, with barge-in, disfluency tolerance and call-center integrations (Twilio, Genesys, Avaya).
Multilingual support
Native support for 30+ languages including Indic, Arabic, CJK, with cross-lingual semantic search and translation-quality monitoring.
Semantic search & knowledge bases
Hybrid retrieval (BM25 + dense) with re-rankers, faceted filters and LLM-rewritten queries, Google-quality search over your private corpus.
Sentiment & quality assurance
Real-time sentiment, churn signals, agent QA scoring, feeding your CX leadership the granular signal call recordings can't.
Conversation analytics
Topic modeling on millions of transcripts; surface the top 20 customer pain themes auto-clustered, ranked by impact.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Multilingual IVR replacing 60% of L1 calls
A 12-language voicebot handles billing, plan-changes and SIM activation, with seamless human handoff when sentiment turns negative.
Six reasons enterprises run NLP & Conversational AI with Infivit.
Built for the 2026 reality of NLP & Conversational AI: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
70%+ containment, end-to-end resolution.
Conversations resolved without human handoff. Multi-turn memory, slot-filling and graceful fallbacks. Not a glorified IVR with a chat skin.
22 Indian + 30 global, voice and text.
Hindi-English code-switching, Tamil voice, Bengali typed, Arabic RTL, all handled natively. Built for India and the world simultaneously, not bolted on later.
Streaming STT to LLM to TTS, end-to-end.
Streaming everywhere with smart endpointing. Conversations feel human, not walkie-talkie, even on flaky 4G connections.
Web, WhatsApp, voice, IVR, mobile, unified.
One orchestration layer, every channel. Context follows the user from chat to voice to email without the bot losing the thread mid-conversation.
Tone, persona and policy enforced via evals.
Your assistant talks like your brand: helpful, polished, on-message. Off-tone responses caught and corrected automatically by the eval harness.
Real-user feedback wired into training.
Beyond accuracy: we measure what users actually felt about the conversation. Continuous CSAT signal feeds back into training, every week.
The questions you were already going to ask.
Got a nlp & conversational ai problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.