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About Quorlytic

Engineering is the foundation of trust.

By QuorlyticUpdated

Quorlytic is an engineering-led data, analytics and AI consultancy. We build the data pipelines, analytics and machine learning that organizations depend on for decisions, and every deliverable ships with a named owner, a monitoring plan and a recovery path, so the system stays accountable after launch, not only at handover.

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  1. Operations & ownershipMonitoring, runbooks and a named owner, so the system holds up after launch.
  2. Machine learning & AIForecasts and AI applications evaluated against a baseline, with visible uncertainty.
  3. Analytics & BIMetrics defined once, and reporting built around the decision it supports.
  4. Data engineeringPipelines, warehouses and lakehouses that give every team the same trusted data.
Each layer depends on the one beneath it. Data engineering is the base.
  • Founder-led
  • Engineering first
  • Method published
  • Evidence with client approval

What we do

Four disciplines, built in the order they depend on each other.

Analytics and AI are only as reliable as the data beneath them, so the work starts with the foundation and builds up.

Mission

Build the data foundation that lets an organization make a better decision, and operate it with confidence.

Evidence

The work, and the thinking behind it.

Client work is published only with the client's approval and without invented figures. Our engineering method is published in full.

Client case study · ManufacturingAn AWS data platform for manufacturing demand intelligenceQuorlytic engineered an AWS data platform that unified a manufacturer's ERP, sales, production and inventory data through automated ETL, put Power BI reporting on one trusted layer, and added a Python demand forecast built on the same history.Read the case study
Published thinking37 engineering guidesArchitecture decisions, trade-offs and failure modes, each with its technical references.Publication standardHow we document client workSituation, architecture, measurement and limits, before anything is published.

Delivery method

Discover → Design → Build → Deploy → Optimize.

Each stage ends in an artifact you can review and a named owner. The method is written down because a firm that can state its own process can be held to it.

  1. 01

    Discover

    Agree the decision, map the data and define acceptance criteria before building.

    Artifact
    A written decision brief
    Owner
    Confirmed before Design starts
  2. 02

    Design

    Choose the architecture around workload, governance and operating cost.

    Artifact
    An architecture decision record
    Owner
    Quorlytic's engineering lead
  3. 03

    Build

    Deliver a focused data product with quality checks and access controls.

    Artifact
    A tested, documented implementation
    Owner
    Quorlytic, reviewed against the brief
  4. 04

    Deploy

    Release with monitoring, runbooks, rollback and a named owner.

    Artifact
    Monitoring, runbooks and a rollback plan
    Owner
    Named before go-live
  5. 05

    Optimize

    Review incidents, cost and adoption, then improve deliberately.

    Artifact
    An incident, cost and adoption review
    Owner
    Whoever holds operational ownership

Principles

How the work holds up.

Three rules every engagement follows, and four things we will not do.

  1. 01

    Solve a real constraint

    A technical choice should answer a concrete constraint — latency, reliability, cost or access — not a preference.

  2. 02

    Test against a baseline

    A model, dashboard or pipeline is only useful if it beats the process it replaces, measured fairly.

  3. 03

    Leave an owner and a runbook

    Every deliverable ships with monitoring, documentation and a named owner who can operate it.

What we don't do

  • Publish a client name, logo or result without explicit permission and a verified measurement.
  • Claim official partnership, certification or reseller status for a platform we work with.
  • Present a reference architecture as a completed client deployment.
  • Deploy a model, dashboard or pipeline without a named operational owner and a recovery path.

Responsible AI & data

Controls before capabilities.

  • Access and permissions are enforced before data reaches a model.
  • Deletion, retention and consent propagate through derived stores.
  • Evaluation includes unsupported questions, not only demonstrations.
  • Actions with material impact keep a human approval path.

Leadership

Led by the engineer who writes the method.

Deepesh Patel

Deepesh Patel

Founder & Data Engineer

Deepesh Patel leads Quorlytic's approach to dependable data engineering, decision-focused analytics and responsibly operated enterprise AI, and writes the engineering guides published on this site.

Profile and guides LinkedIn

Your next data decision

Start with a clear problem.
Build what comes next.

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Bring the business question.
We'll help shape the engineering brief.