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Zobique Labs

Zobique Labs engineers AI systems that do real work inside other businesses. We map the workflow that costs your team time, design a system around how your operation actually runs, build it against your real data and tools, and keep it running after launch.

Talk to Zobique LabsVisit labs.zobique.com
labs.zobique.comlabs.zobique.com homepage: 'Production AI Systems for Business'

What we build

Turn sales operations into a revenue engine.

AI agents qualify leads, enrich data and keep your pipeline moving.

  • Lead Qualification
  • CRM Automation
  • Email & Follow-Up
Explore AI Sales Agent
Illustration of an AI sales agent qualifying a lead, updating a CRM pipeline and scheduling a follow-up

Resolve issues faster, keep the human touch.

AI support answers common questions and routes complex tickets to the right person.

  • Ticket Classification
  • Knowledge Retrieval
  • Human Escalation
Explore AI Customer Support
AI Customer Support illustration: a friendly support robot above a ticket dashboard with a resolved follow-up

Turn paper-heavy work into automated systems.

Extract, classify and route data from contracts, invoices, forms and PDFs.

  • Invoice Processing
  • Contract Analysis
  • Forms & Intake Automation
Explore Document AI
Document AI illustration: PDFs, spreadsheets, images and documents flow into an AI processor that extracts fields into a review dashboard

Custom AI products built for your use case.

AI applications built around your workflow, from productivity tools to autonomous systems.

  • AI Integration Services
  • AI Model Training & Fine-Tuning
  • AI Maintenance & Optimization
Explore AI Application Development
AI Applications illustration: documents, images, data, settings and users connect to an AI model that powers an analytics dashboard

How a project runs

How a business problem becomes a system that runs: workflow analysis, architecture, build, deployment, and the tuning that follows.

  1. Map the Workflow. Find the AI Opportunity.

    We sit with the operation, trace where work actually slows down, and identify which steps an AI system can take over without breaking the process around them.

    • Workflow audit and bottleneck identification
    • Feasibility, ROI modeling, and model selection
    • System architecture blueprint and delivery timeline
    Explore a WorkflowSee what we build
    Discover: a flat app window on orange showing a six-step workflow map with the fourth step highlighted in orange as the bottleneck, above a bar breakdown and a rising trend line

  2. Design the System. Define the Guardrails.

    We turn that map into a system architecture: agent behaviour, tool access, escalation paths, and the interfaces the people around the system will actually use.

    • Human-in-the-loop interaction design and escalation paths
    • Agent architecture, tool access, and permission boundaries
    • Working prototypes validated against the real workflow
    See what we buildSee Case Studies
    Design: a flat app window on orange showing an AI agent connected to a database, a document and an integration inside a dashed orange guardrail with a padlock, and an orange arrow handing over to a person outside it

  3. Build It. Connect It. Ship It.

    We build the system against your real data and real tools: agent pipelines, retrieval, integrations, and the infrastructure it runs on.

    • Application layer, cloud infrastructure, and API integrations
    • Agent pipelines, custom RAG, and private model fine-tuning
    • Access controls, automated testing, and strict type safety
    See What We've BuiltStart a conversation
    Build: a flat app window on orange with a dark code editor on the left and a four-step build pipeline on the right, three steps ticked in black and the final step ticked in orange

  4. Deploy to Production. Keep It Working.

    We deploy without downtime, watch what the system does against live traffic, and keep tuning it as the business and the models change underneath it.

    • Zero-downtime deployment and cloud provisioning
    • Telemetry, agent observability, and latency tuning
    • Model retraining, iteration, and A/B testing
    Build an AI SystemTalk to an engineer
    Launch and evolve: a flat app window on orange with a live indicator, three small trend tiles and a wide chart with an orange line rising steadily, beside a refresh symbol for continuous tuning

Selected Labs work

Startup Ecosystem / Platform Engineering

Outcome

Marketplace module shipped on a new modular platform

Read the case studyservice.bizzowl.com
Client
Bizowl
Problem
Bizowl's product had outgrown its architecture; new features were hard to add.
What we built
A modular platform, a marketplace module and a clearer user experience.
Result
Marketplace module live, with existing data migrated.Qualitative: delivery of a module and a data migration.
Bizowl's service marketplace: 'Unlock Your Business!' with service categories and browsable services

Specialised Services / Lead Intelligence

Outcome

About ₹0.80 estimated cost per researched lead

Read the case study
Client
Vastav Intellect
Problem
Two people researched every lead by hand, about ₹40,000 a month.
What we built
A three-stage research pipeline: search, extract, classify.
Result
About ₹0.80 per researched lead, versus ₹2–₹8 before.Estimated API cost per lead, not audited.
VIIPS Lead Intelligence dashboard: lead queue with totals, research status, filters and bulk actions

Have a workflow in mind?

We map the workflow, build the AI system around it, and keep it running.

  • Discover → Design → Build → Run
Talk to Zobique Labs