Astral Mantra Labs builds production-grade conversational AI — LLM-powered support, sales, and internal-ops assistants — grounded in your own documents, integrated with your stack, and continuously evaluated. Nepal's AI-native studio, shipping in 4–8 weeks.
Conversational AI is software that interacts with humans in natural language — typed or spoken — using a large language model (LLM) to understand intent, hold context, and produce relevant replies. Modern conversational AI goes far past the rule-based chatbots of five years ago. It listens, reasons, retrieves from your data, and responds in your tone.
The category covers a wide spectrum: customer-support assistants, sales co-pilots, internal knowledge bots, in-product help, voice agents in call centres, WhatsApp concierges, and Slack or Teams bots that answer the questions your operations team is tired of repeating.
Every conversational AI we ship is built on the same five layers, regardless of channel (web, WhatsApp, Slack, voice, in-product). Treating these as separate engineering concerns is the difference between a demo and a production system:
The model that interprets the user's request and produces the answer. We pick what fits — GPT, Claude, Llama, Mistral, or fine-tuned open-source.
Your knowledge base, support docs, product specs, and CRM are indexed into a vector store. The assistant grounds every answer in what you actually wrote.
For account questions, refunds, scheduling, etc., the assistant calls real APIs — your CRM, your booking system, your support tooling — instead of guessing.
Regression tests on dozens of prompts every deploy, plus red-team prompts to catch jailbreaks. We measure what we ship.
Graceful escalation to a human agent for anything ambiguous, high-stakes, or beyond the assistant's policy. No black-box dead-ends.
The same brain plugs into web chat, WhatsApp, Slack, Teams, voice, or your own app via SDK. One assistant, every surface your users live on.
The patterns where conversational AI consistently moves a real business metric:
4 weeks for a single-channel assistant grounded in your existing docs (e.g. web support bot). 6–8 weeks when we add tool calls (CRM, booking, refunds) and a continuous evaluation harness. 10–14 weeks for multi-channel deployments (web + WhatsApp + Slack), voice, or custom analytics.
These ranges include discovery, prompt and retrieval engineering, integrations, the evaluation harness, deployment, and the first weeks of production tuning where most of the real-world adjustments happen.
Every engagement begins with a fixed-price discovery scope. For a deeper breakdown of how AI project pricing works, see our guide to AI development cost in Nepal.
Direct answers to the questions buyers, support leads, and engineering leads ask us most.
Conversational AI is software that interacts with humans in natural language — typed or spoken — using a large language model. Modern conversational AI goes far beyond rule-based chatbots: it understands intent, holds context across a conversation, calls tools, and grounds its answers in your own documents and data.
Older chatbots use scripted decision trees — every reply is hand-coded. Conversational AI uses an LLM that understands free-form language, holds context, and can be grounded in your live data through retrieval-augmented generation. The user experience is night and day.
Astral Mantra Labs ships a focused production conversational AI — single channel, grounded in your data, with an evaluation harness — in the low-to-mid four figures USD. Multi-channel assistants (web, WhatsApp, Slack, Teams) with custom analytics scale into five figures.
Astral Mantra Labs typically delivers a production conversational AI in 4–8 weeks, including discovery, prompt and retrieval engineering, integration, evaluation, and the first weeks of production tuning.
Yes. We use retrieval-augmented generation (RAG) so the assistant grounds its answers in your own knowledge base, support docs, product specs, or CRM. The model never invents content from your domain — if the answer isn't in your data, it says so.
No. The pattern that works in production is conversational AI handling Tier 1 (FAQs, account questions, common how-tos) and gracefully escalating to humans for anything ambiguous or high-stakes. Teams typically see 40–70% Tier 1 deflection without hurting CSAT.
Three layers: (1) we ground every answer in retrieved documents from your knowledge base, (2) we run a continuous evaluation harness with regression tests and red-team prompts, and (3) for high-stakes answers we add a verification step where the model checks its own output against the source.
Tell us the channel, the user, and the workflow. We'll come back within 24 hours with a scope, a timeline, and a fixed-price discovery proposal.