[ The measure that matters is resolution, not deflection ]
Everyone has met the chatbot that cannot answer and will not escalate. The reputational cost of that experience is why many teams hesitate now — and why the measure that matters is not deflection rate but resolution rate. Techtaru Digital is an AI chatbot development company building LLM and RAG-grounded assistants that answer from your real content, admit when they do not know, and hand over to a person cleanly.
Everyone has met the chatbot that cannot answer and will not escalate. The reputational cost of that experience is why many teams hesitate now — and why the measure that matters is not deflection rate but resolution rate. Techtaru Digital is an AI chatbot development company building LLM and RAG-grounded assistants that answer from your real content, admit when they do not know, and hand over to a person cleanly.
Send us a slice of your help centre and we will build a working prototypeassistants built on your content, tone and workflows rather than a configured template.
multi-turn experiences that hold context, handle interruptions and complete tasks rather than just answering.
LLM-powered assistants replacing rigid decision trees.
answers grounded in your documentation, policies and product data, with citations.
speech interfaces for phone support and IVR replacement, with barge-in and latency tuning.
deployment inside your security boundary, with SSO, role-based answers, audit logging and data residency.
replacing failed rule-based bots without losing the intents and content already built.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Grounded in your help centre, policies and order data. Handles status queries, returns, troubleshooting and policy questions, escalating to a human with full conversation context when confidence drops or the customer asks.
Internal assistants answering from wikis, SOPs, contracts and technical documentation, with permission-aware retrieval so people only see answers from documents they are entitled to read. Underneath sits our RAG and LLM engineering.
Product discovery and comparison, size and compatibility guidance, order tracking, cart recovery and qualified lead capture routed into your CRM.
WhatsApp Business API, Instagram, Messenger and SMS assistants with template message compliance, opt-in handling and rich media.
Speech-to-text, LLM reasoning and text-to-speech with the latency budget managed end to end, interruption handling, and warm transfer to a human agent.
HR policy Q&A, IT support triage, finance and procurement queries — the high-volume repetitive questions that consume internal teams. Where answering is not enough, you want agents that take action.
Assistants operating across English, Hindi and regional languages with consistent behaviour and quality evaluated per language rather than assumed from the English version.
real ticket and query data analysed to find the highest-volume intents and whether content exists to answer them.
launch intents agreed, golden conversations built with your support team.
retrieval pipeline, prompt design, escalation logic, tone tuning and channel integration.
the bot answers alongside human agents without customer exposure, and its answers are reviewed against what agents actually said.
released to a traffic percentage, monitored on resolution and escalation, then widened as quality holds.
Models: GPT, Claude, Gemini, Llama and open-weight models, routed by complexity and cost. Retrieval: pgvector, Pinecone, Qdrant or Weaviate with hybrid search and reranking. Channels: web widget, WhatsApp Business API, Instagram, Messenger, Slack, Teams, SMS, voice via Twilio or Exotel. Helpdesk integration: Zendesk, Freshdesk, Intercom, HubSpot, Salesforce Service Cloud. Voice: Deepgram or Whisper for STT, ElevenLabs or Azure for TTS. Evaluation: golden conversation sets, LLM-as-judge with human review, containment and resolution tracking.
Measure resolution, not deflection. Deflection counts conversations that did not reach a human. Resolution counts problems actually solved. A bot that frustrates a customer into abandoning the query scores well on deflection and badly on everything that matters. We instrument resolution, escalation rate, containment and post-chat satisfaction from launch.
Escalation is a feature, not a failure. The fastest way to destroy trust is trapping someone in a loop with no route to a human. Every bot we build has an explicit escalation path, triggers on low confidence, repeated rephrasing and explicit requests, and passes the full conversation to the agent so the customer never repeats themselves.
Grounding beats prompting for accuracy. A chatbot answering from retrieved documentation with citations is verifiable. One answering from model knowledge alone will eventually state your refund policy incorrectly with complete confidence. RAG is the default architecture for anything customer-facing.
Scope narrowly at launch, then expand. Bots that attempt everything on day one answer everything badly. We launch on the top intents by volume — typically covering a majority of contacts with a small number of topics — measure, then widen. This also makes the business case provable early.
Evaluation before launch, and continuously after. A golden set of real conversations with acceptable answers, run on every prompt or content change. Without it, you discover regressions from customer complaints.
Regulatory position. Transparency obligations under the EU AI Act mean people must know they are interacting with an AI system, and several jurisdictions require the same. Consent for messaging channels and data retention rules under GDPR and India's DPDP Act apply to conversation logs, which frequently contain personal data.
Fixed scope for defined bots; phased pricing for multi-channel enterprise deployments; monthly retainers for tuning, content updates and evaluation.
Indicative cost: a focused support or eCommerce chatbot on one channel typically runs $8,000–$25,000. Enterprise deployments with multiple channels, CRM integration, voice and permission-aware retrieval generally run $30,000–$80,000. Recurring model inference and channel costs are modelled separately — a support bot's monthly inference cost is usually a small fraction of the agent time it replaces, but it should be calculated rather than assumed.
The quickest way to judge an AI chatbot development company is to see a bot answering from your own documentation. Send us a slice of your help centre and we will build a working prototype you can test against real questions.
A focused single-channel bot typically runs $8,000–$25,000; enterprise multi-channel deployments $30,000–$80,000. Recurring inference costs are modelled separately and are usually modest relative to agent time saved.
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