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Qzentra

Services

Four entry points into one engineering capability.

Clients come to Qzentra with an operational bottleneck, a data problem, an agent or voice requirement, or a product to build. Each is a service pillar. Underneath, the same team works through the same six layers and goes as deep as the problem requires.

01 The four pillars

What each pillar engineers

Each entry names the situation that leads to it, what gets built, and the layers the work usually touches. The detail pages go into the systems, the architecture, and the evidence.

  1. Your team is doing too much by hand, or your systems do not talk to each other.

    AI Systems & Automation

    Intake, qualification, CRM and document workflows, approvals, reporting, and multi-system orchestration, with explicit business logic, integrations that fail safely, and human handoff built in.

    Representative systems

    • Lead intake and qualification
    • Customer-service automation
    • Document processing
    • Multi-system orchestration
    Explore

  2. Your data is slow, fragmented, or hard to query, or a production database has to move without downtime.

    Data Engineering

    Pipelines, migrations, database architecture, and query infrastructure: the foundation that the analytics, automation, and AI above it depend on.

    Representative systems

    • ETL / ELT pipelines
    • Zero-downtime database migrations
    • Distributed query architectures
    • Analytics backends
    Explore

  3. You need an agent that answers from company knowledge and acts in your tools, or a phone line that handles calls.

    AI Agents & Voice AI

    Retrieval and knowledge systems, tool-using and workflow agents, and voice systems on real telephony, all with escalation to people designed in.

    Representative systems

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

  4. You have a product idea that needs custom AI architecture, or a prototype that has to become real software.

    Custom AI Applications

    Internal tools, customer-facing applications, AI SaaS, dashboards, and agent consoles, built together with the backend, data, and AI layers under them.

    Representative systems

    • Internal AI tools
    • Customer-facing AI applications
    • Agent consoles
    • API-backed AI services
    Explore

02 By layer

Where each service does its engineering

No pillar lives in one layer. A filled mark is a layer the service usually owns in an engagement; an outlined mark is a layer it regularly reaches into. Every layer has at least one service that owns it.

ServiceDataInfrastructureAPIsAIAutomationApplication
AI Systems & Automation
Data Engineering
AI Agents & Voice AI
Custom AI Applications
  • usually owns
  • reaches into
  • 01Data
  • 02Infrastructure
  • 03APIs
  • 04AI
  • 05Automation
  • 06Application

03 Across boundaries

Real projects cross these boundaries

The pillars are entry points, not departments. Three approved projects show how far a single engagement travels through the section.

  1. 01AI Systems & Automation

    Multimodal AI Sales Funnel

    Read the case file
    1. Messaging channels
    2. Multimodal AI
    3. Product knowledge retrieval
    4. Lead scoring
    5. Human handoff
    6. Automated follow-up

  2. 03Data Engineering

    5 TB Zero-Downtime MySQL Migration

    Read the case file
    1. Compatibility audit
    2. Infrastructure
    3. Replication
    4. Cutover risk management
    5. Query modernization

  3. 02AI Systems & Automation

    AI Website Audit & Outreach Engine

    Read the case file
    1. Web data extraction
    2. PageSpeed API
    3. LLM generation
    4. Segmentation
    5. Outbound follow-ups

04 Evidence

Work that proves it

The approved projects, each mapped to the service it enters through.

  1. 01Case fileAI Systems & Automation

    Multimodal AI Sales Funnel

    An end-to-end sales funnel for a crypto-mining hardware enterprise: Instagram and WhatsApp leads qualified, answered from a catalog of 500+ ASIC miners, scored, and handed to sales.

    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. 02Case fileAI Systems & Automation

    AI Website Audit & Outreach Engine

    An automated pipeline from a list of target URLs to personalized, segmented outreach: crawling, contact extraction, PageSpeed analysis, LLM-written messages, and automated follow-ups.

    Verified factEvery message grounded in a Google PageSpeed audit of the prospect’s site

    Enters through01AI Systems & Automation

    Read the case file
    AI Website Audit & Outreach Engine: route through the six layers.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data1234567
    1. 1Target URLsData
    2. 2Crawling and contact/social extractionData
    3. 3Google PageSpeed APIAPIs
    4. 4Technical analysisAI
    5. 5LLM-generated personalized outreachAI
    6. 6SegmentationAutomation
    7. 7Automated follow-upsAutomation

  3. 03Case fileData Engineering

    5 TB Zero-Downtime MySQL Migration

    A 24/7 production system on legacy MySQL 5.7 with roughly 5 TB of data, moved to a modern MySQL environment by replication with no service interruption at cutover.

    Verified factApproximately 5 TB, zero service interruption at cutover

    Enters through02Data Engineering

    Read the case file
    5 TB Zero-Downtime MySQL Migration: route through the six layers.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data12345
    1. 1Compatibility audit of MySQL 5.7 workloadData
    2. 2Deprecated variable remediationInfrastructure
    3. 3Replica build and synchronizationInfrastructure
    4. 4Replica promotion at cutoverInfrastructure
    5. 5Query modernization with CTEsData

  4. 04Case fileData Engineering

    Enterprise Data Query Architecture

    Query architectures for analytical workloads across data platforms and data lakes, using distributed and embedded engines over Azure Data Lake storage.

    Enters through02Data Engineering

    Read the case file
    Enterprise Data Query Architecture: route through the six layers.06  Application05  Automation04  AI03  APIs02  Infrastructure01  Data1234
    1. 1lake storageData
    2. 2query enginesInfrastructure
    3. 3federationAPIs
    4. 4analyticsApplication

  5. 05Capability caseAI Agents & Voice AI

    Voice AI Systems

    Voice systems on real telephony: AI receptionists, inbound support, outbound qualification, appointment booking, and CRM-integrated calling, with transcription, call analysis, and escalation to people.

    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
View the Work

05 Discuss a project

Have a systems problem that doesn't fit neatly into one category?

Most do not. Start with the problem: what you are trying to build or fix, what it has to integrate with, and what a working result looks like. The first conversation scopes the system, not the service.

Discuss a Project