You need an agent that answers from your company’s knowledge and acts in your tools, or a phone line that handles calls without losing the ones that need a person.
This pillar covers two related capability areas. AI agents: retrieval over company knowledge, tool-using and workflow agents, and support and qualification agents. Voice AI: receptionists, inbound support, outbound qualification, and booking, on real telephony.
In both, the engineering is in the connections. An agent is only useful when it can retrieve accurate context, call the right tools, keep state across steps, and hand off to a person with the conversation intact. Qzentra builds those connections, not just the prompt.
Layers this service works in
Company knowledge is indexed for retrieval; the agent reasons over the retrieved context; it calls tools and telephony through APIs; its actions run through orchestration; and the conversation escalates to a person when needed.
Agents that retrieve from company knowledge, act through your tools, carry state across steps, and stop for a person when the business requires it.
Retrieval over documents, tickets, product data, and policies, with chunking, indexing, and refresh designed for the corpus, and answers that cite their source.
Agents that call your APIs and internal tools to look things up and take actions, within defined permissions.
Multi-step agents that carry state through a process, decide the next step, and stop for approval where the business requires it.
Conversational qualification on messaging channels, scored against your criteria and routed to sales.
Answer from approved knowledge, act in the ticketing or CRM system, and escalate with the history attached.
Read, extract, compare, and summarize across large document sets, producing structured output people can check.
Handle text, voice notes, and images in one conversation, as in the sales funnel built for a crypto-mining hardware enterprise.
Architecture may include
Voice systems on real telephony that handle the routine calls, act in your systems during the call, and transfer to a person with the conversation summarized.
Answer inbound calls, understand the request, answer common questions, book or route, and transfer to a person when needed.
Resolve routine calls from approved knowledge and account data, with escalation for anything outside scope.
Place calls to leads, qualify against your criteria, and hand qualified conversations to sales.
Read availability, book, confirm, and reschedule through your calendar or booking system.
Log every call, outcome, and next step in the CRM, and trigger the follow-up workflow.
Transcribe calls, extract intents and outcomes, and feed them to reporting or quality review.
Warm transfer to a person with a summary of the conversation so far.
Architecture may include
The situations that lead to this work, across both capability areas, and what an engineered answer needs in each case.
Answers exist in PDFs, tickets, and people’s heads, and nobody can find them quickly.
The engineered answer
A retrieval system over the corpus with refresh and source attribution, and an agent that answers within it.
A chatbot answers questions but cannot look up an order, update a record, or create a ticket.
The engineered answer
Tool-using agents with permissioned access to your APIs and a record of every action taken.
Inbound calls go unanswered outside hours, or a team spends its day on routine ones.
The engineered answer
A voice system that handles the routine calls and transfers the rest, with every call logged to the CRM.
Qualification calls and follow-ups depend on how many people are available that day.
The engineered answer
Outbound qualification and booking with human handoff at the point of real interest.
The AI keeps going when it should stop.
The engineered answer
Escalation designed in: confidence thresholds, explicit stop conditions, and a transfer that carries the context.
Agents and voice systems share one hierarchy. Models and platforms are the last decision, made once the channel, the knowledge, the tools, and the handoff are defined.
Recorded experience across both areas. Models and platforms are chosen per project.
One verified production agent, and a voice capability record that waits for a specific project before it is presented as a case study.
AI Systems & Automation
A multimodal agent on Instagram and WhatsApp that handles text, voice notes, and images, retrieves from an Airtable-backed catalog of 500+ ASIC miners, qualifies and scores leads, and hands off to sales with automated follow-up.
Read the case file: Multimodal AI Sales Funnel500+ASIC miners in the product catalog the system answers from
AI Agents & Voice AI
An AI voice receptionist that books appointments into Google Calendar, and outbound AI calling agents for sales. Both answer from a knowledge base and hand urgent calls they cannot answer to a person.
Read the case file: AI Voice Receptionist and Outbound Sales AgentsAppointments booked into Google Calendar during the call
Discuss a project
Tell us the channel, the knowledge it must answer from, the tools it must act in, and where a person takes over. That is the whole specification to start from.
Often combined with
Agents rarely stand alone. The actions they trigger and the follow-ups they schedule are automation work.
Retrieval quality is set by the corpus. When the knowledge is fragmented or stale, that is fixed first.
When the agent needs an interface of its own, for customers or for the team supervising it.