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Seven capabilities. One connected foundation.

Quorlytic's services cover the path from source systems to production intelligence: data and AI advisory, data engineering, business intelligence, data modernization, advanced analytics, AI and machine learning, and managed data and AI operations. Each service is scoped around a deliverable, an acceptance test and a named owner, so you can see what is being built and who operates it.

From dependable data pipelines to production AI, explore engineering services built around clear deliverables and operational ownership.

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Reliable data pipelines

Capability system

From data foundation to production intelligence.

Four capability families cover the path from raw sources to production systems. Every service sits in one of them, with the outcome it is measured against and the stack behind it visible up front.

01 — Build the foundation

Fragmented sources, brittle pipelines and reporting that still depends on manual exports.

Explore data engineering

Typical outputs

  • Source inventory and data contracts
  • Versioned pipelines and quality tests
  • Lineage, ownership and runbooks

Services in this family

Outcome

Trusted data, available when teams need it — with lineage, freshness and a named owner.

StackPython · Spark · dbt · Airflow · Kafka

Explore data engineering

Featured capabilities

A closer look at the three engagements enterprise teams ask for first: the data foundation, the decision layer and production AI. Each is scoped around a problem, a capability and an outcome.

Data engineering

A data foundation you can build on.

Problem
Reporting depends on manual exports, and a failed job is discovered by a business user.
Capability
Batch and change-data-capture ingestion, warehouse and lakehouse models, versioned transformations and quality tests.
Outcome
Reliable data available when teams need it — with lineage, freshness and an owner.

Technical contextPython · Spark · dbt · Airflow · Kafka

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Analytics & BI

One definition. A clearer decision.

Problem
Two teams report different numbers for the same metric.
Capability
Governed semantic models, metric dictionaries and Power BI, Tableau or Looker dashboards.
Outcome
One definition, and a clearer decision in the meeting where it happens.

Technical contextPower BI · Tableau · Looker

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Enterprise AI

AI that belongs in production.

Problem
Knowledge is scattered and AI pilots never reach production.
Capability
Retrieval-augmented generation, assistants and bounded agents with access control and evaluation.
Outcome
AI that belongs in production, not only in a demo.

Technical contextLLM APIs · RAG · Vector stores

Explore enterprise AI

Capability depth

Where a service goes deeper.

Three engagements have enough distinct architecture, trade-offs and buyer questions to carry their own page. Everything else is covered inside the service it belongs to, rather than split across a page of its own.

Business outcome bridge

From capability to a decision someone can make.

Technical work only matters if it changes what an operator, analyst or executive can decide. Each capability below is connected to the operational change it produces and the business decision it unlocks.

  1. Reliable dataPipelines, freshness and reconciliation are monitored rather than assumed.Teams work from one trusted view.
  2. Decision-ready reportingMetrics carry one definition, a grain and a named owner.Reviews start from agreed numbers.
  3. Predictive planningForecasts ship with documented uncertainty and operating limits.Planners act on a range, not a single number.
  4. Production AIRetrieval, permissions and evaluation are engineered into the system.Answers are grounded, traceable and safe to use.
  5. Modern data platformsMigration is reconciled against the system it replaces.The business keeps running through the move.
  6. Governed operationsService objectives, runbooks and ownership are explicit.Production systems stay dependable after handover.

Technology ecosystem

Tools follow the workload.

Every layer exists to answer a specific question: where data comes from, how it is governed, what makes decisions, and who operates it. Tools are chosen against the workload, governance and operating cost — not the other way around.

  1. WorkloadVolume, latency and concurrency the decision actually requires.
  2. GovernanceAccess, residency, retention and audit obligations.
  3. Existing skillsWhat the team can operate and extend after handover.
  4. Operating costConsumption, licensing and dual-running during a migration.
  1. 01Business consumptionDecision surfaces and reporting.
    • Power BI
    • Tableau
    • Looker
    • Metabase
  2. 02AI & MLModels, retrieval and evaluation.
    • LLM APIs
    • Vector databases
    • RAG frameworks
    • Evaluation tooling
  3. 03Data engineeringPipelines, transformation and orchestration.
    • Python
    • SQL
    • Spark
    • Airflow
    • dbt
    • Kafka
  4. 04Data platformsWarehouse and lakehouse foundation.
    • Snowflake
    • Databricks
    • BigQuery
    • Redshift
    • PostgreSQL
    • Microsoft Fabric
  5. 05CloudWhere the platform runs.
    • AWS
    • Azure
    • Google Cloud
  6. 06OperationsRelease, monitor and operate across every layer.
    • CI/CD
    • Model monitoring
    • Data observability
    • Orchestration

Technologies we work with. No partnership, certification or endorsement is implied unless explicitly supported.

Questions

Common questions.

What does a data engineering consultancy do?

It designs and builds the systems that move data from operational sources into models a business can rely on: ingestion, transformation, warehouses or lakehouses, quality checks and the ownership around them. Analytics, BI and AI work then read from that foundation instead of rebuilding it for every project.

Which service should we start with?

Start with the decision that is hardest to make today. If reports disagree, begin with business intelligence and metric definitions; if data arrives late or broken, data engineering; if a platform move is planned, modernization. Data & AI advisory fits when the order itself is unclear.

Do you work to a fixed scope or as a team?

Both. A fixed-scope project suits a defined build or migration with acceptance criteria. A dedicated senior team suits a roadmap that will change as you learn. Ongoing managed support covers platforms that are already in production.

Will you work with our existing platform and tools?

Yes. Most engagements start from an existing estate such as Snowflake, Databricks, BigQuery, Redshift, Fabric or Power BI and the pipelines around them. A platform change is recommended only when the workload, cost or operating model justifies it.

Your next data decision

Start with a clear problem.
Build what comes next.

Talk to a Data Expert

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