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Qzentra

AI agents and voice systems connected to your data, tools, and people.

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

01 The route

Route of an agent or voice system.

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.

Route of an agent or voice system.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.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data123456
  1. 1knowledgeData
  2. 2retrievalInfrastructure
  3. 3agentAI
  4. 4tool callsAPIs
  5. 5actionsAutomation
  6. 6handoffApplication

02 Capability area

AI agents

Agents that retrieve from company knowledge, act through your tools, carry state across steps, and stop for a person when the business requires it.

  • RAG and company knowledge systems

    Retrieval over documents, tickets, product data, and policies, with chunking, indexing, and refresh designed for the corpus, and answers that cite their source.

  • Tool-using agents

    Agents that call your APIs and internal tools to look things up and take actions, within defined permissions.

  • Workflow agents

    Multi-step agents that carry state through a process, decide the next step, and stop for approval where the business requires it.

  • Lead qualification agents

    Conversational qualification on messaging channels, scored against your criteria and routed to sales.

  • Support agents

    Answer from approved knowledge, act in the ticketing or CRM system, and escalate with the history attached.

  • Research and document-processing agents

    Read, extract, compare, and summarize across large document sets, producing structured output people can check.

  • Multimodal agents

    Handle text, voice notes, and images in one conversation, as in the sales funnel built for a crypto-mining hardware enterprise.

Architecture may include

  • Chunking, embedding, and index refresh matched to the corpus
  • Retrieval with source attribution and confidence thresholds
  • Tool schemas with explicit permissions and validation
  • State machines or graphs for multi-step orchestration
  • Human escalation with full context and a queue
  • Evaluation sets and logging for every conversation

Recorded technology

  • OpenAI
  • Claude
  • Gemini
  • LangChain
  • LangGraph
  • Pinecone
  • Supabase Vector
  • n8n
  • APIs and webhooks

03 Capability area

Voice AI

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.

  • AI receptionist

    Answer inbound calls, understand the request, answer common questions, book or route, and transfer to a person when needed.

  • Inbound customer support

    Resolve routine calls from approved knowledge and account data, with escalation for anything outside scope.

  • Outbound qualification

    Place calls to leads, qualify against your criteria, and hand qualified conversations to sales.

  • Appointment booking

    Read availability, book, confirm, and reschedule through your calendar or booking system.

  • CRM-integrated calling

    Log every call, outcome, and next step in the CRM, and trigger the follow-up workflow.

  • Transcription and call analysis

    Transcribe calls, extract intents and outcomes, and feed them to reporting or quality review.

  • Escalation and human transfer

    Warm transfer to a person with a summary of the conversation so far.

Architecture may include

  • Telephony integration for inbound and outbound calls
  • Speech recognition and synthesis tuned for latency
  • Conversation logic with an LLM inside defined guardrails
  • Tool calls to calendars, CRMs, and internal APIs during the call
  • Transfer and fallback paths for out-of-scope or failed calls
  • Call logging, transcription, and analysis pipelines

Recorded technology

  • Vapi
  • Retell AI
  • Twilio
  • ElevenLabs
  • Deepgram
  • LLM APIs
  • webhooks
  • CRM and API integrations

04 What arrives

Where agents and voice systems earn their place

The situations that lead to this work, across both capability areas, and what an engineered answer needs in each case.

  1. Knowledge trapped in documents

    Answers exist in PDFs, tickets, and people’s heads, and nobody can find them quickly.

    An engineered answer

    A retrieval system over the corpus with refresh and source attribution, and an agent that answers within it.

  2. Agents that cannot act

    A chatbot answers questions but cannot look up an order, update a record, or create a ticket.

    An engineered answer

    Tool-using agents with permissioned access to your APIs and a record of every action taken.

  3. Calls that need coverage

    Inbound calls go unanswered outside hours, or a team spends its day on routine ones.

    An engineered answer

    A voice system that handles the routine calls and transfers the rest, with every call logged to the CRM.

  4. Outbound work that does not scale

    Qualification calls and follow-ups depend on how many people are available that day.

    An engineered answer

    Outbound qualification and booking with human handoff at the point of real interest.

  5. No safe path to a person

    The AI keeps going when it should stop.

    An engineered answer

    Escalation designed in: confidence thresholds, explicit stop conditions, and a transfer that carries the context.

05 Depth

From the business problem down to the technology

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.

  1. Business problem

    • Knowledge nobody can find
    • Support and qualification that does not scale
    • Phone coverage
    • AI that cannot take action

  2. System

    • RAG and knowledge systems
    • Tool-using and workflow agents
    • Qualification and support agents
    • AI receptionists and outbound calling
    • Transcription and call analysis

  3. Architecture

    • Corpus pipelines: chunking, embedding, refresh
    • Retrieval with attribution and thresholds
    • Permissioned tool schemas
    • Stateful multi-step orchestration
    • Telephony, speech, and LLM layers tuned for latency
    • Escalation and transfer with context
    • Conversation logging and evaluation

  4. Technology

    Recorded experience across both areas. Models and platforms are chosen per project.

    • OpenAI
    • Claude
    • Gemini
    • LangChain
    • LangGraph
    • Pinecone
    • Supabase Vector
    • Vapi
    • Retell AI
    • Twilio
    • ElevenLabs
    • Deepgram
    • n8n
    • webhooks

06 Evidence

Evidence

One verified production agent, and a voice capability record that waits for a specific project before it is presented as a case study.

  1. 01Case fileAI Systems & Automation

    Multimodal AI Sales Funnel

    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.

    Verified factAirtable-backed catalog of 500+ ASIC miners

    Enters through01AI Systems & Automation

    Read the case file
    Multimodal AI Sales Funnel: route through the six layers.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data12345678
    1. 1Instagram DMsAPIs
    2. 2Lead qualificationAutomation
    3. 3WhatsAppAPIs
    4. 4Multimodal AI (text, voice notes, images)AI
    5. 5Product knowledge retrievalData
    6. 6Lead scoringAutomation
    7. 7Human sales handoffApplication
    8. 8Automated follow-up or cold-lead archivingAutomation

  2. 05Capability caseAI Agents & Voice AI

    Voice AI Systems

    Voice system capability across reception, inbound support, lead qualification, outbound calling, appointment booking, CRM-integrated workflows, transcription and call analysis, and escalation. A specific deployed project has not been supplied.

    Enters through03AI Agents & Voice AI

    Read the case file
    Voice AI Systems: route through the six layers.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data1234567
    1. 1Telephony: inbound or outbound callAPIs
    2. 2Speech recognitionAI
    3. 3LLM conversation logicAI
    4. 4Tools and CRM actionsAutomation
    5. 5Speech synthesisAI
    6. 6Transcription and call analysisData
    7. 7Escalation and human transferApplication

07 Discuss a project

Need an agent or a phone line that can be trusted with real customers?

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.

Discuss a ProjectAll services

Often combined with

  • 01AI Systems & Automation

    Agents rarely stand alone. The actions they trigger and the follow-ups they schedule are automation work.

  • 02Data Engineering

    Retrieval quality is set by the corpus. When the knowledge is fragmented or stale, that is fixed first.

  • 04Custom AI Applications

    When the agent needs an interface of its own, for customers or for the team supervising it.