Zaply
Zaply
Services · AI & Data

Your AI is only as good as the data feeding it.

Most companies don't have a data problem — they have a data infrastructure problem. Siloed systems, inconsistent definitions, unreliable pipelines. We fix the foundation so your analytics are trustworthy and your AI actually works.

01What we deliver

From scattered sources to a unified data layer

  • Data audit & architecture design

    We map every data source, flow, and system in your organization. You get a clear picture of what you have, what's missing, what's wrong, and what the target architecture should look like.

  • ETL/ELT pipeline engineering

    Pipelines that move data from your systems into a unified layer, with validation, transformation, lineage and error alerts.

  • Data warehouse & lakehouse

    A single source that analysts, operations and AI systems can query. We work with BigQuery, Snowflake, Redshift, Databricks or open-source options, depending on scale and budget.

  • Data quality & governance

    Validation rules, freshness checks, schema contracts and access controls. Every dataset has an owner and a definition.

  • Analytics & self-service BI

    Dashboards and reports business teams can use without relying on engineering, in Looker, Metabase or Superset.

  • AI-ready data infrastructure

    Structured, clean, versioned data feeds that your ML models and AI agents can consume reliably. Feature stores, embedding pipelines, and vector databases when the use case calls for them.

02Tech stack

We work with the tools your team already uses.

We use open-source and cloud tools, chosen for your scale, budget and your team's ability to maintain them.

Ingestion
Airbyte · Fivetran · Custom connectors
Transformation
dbt · Apache Spark · Pandas
Storage
BigQuery · Snowflake · Redshift · PostgreSQL
Orchestration
Apache Airflow · Prefect · Dagster
Visualization
Looker · Metabase · Apache Superset
Quality & governance
Great Expectations · dbt tests · Monte Carlo

03How it works

From audit to production data layer

  1. 01

    Data audit

    We catalogue sources, assess their quality, document schemas and identify gaps before designing anything.

  2. 02

    Architecture design

    We design the target data architecture: which warehouse, which orchestration tool, which transformation layer, and how data flows from source to consumption.

  3. 03

    Pipeline build

    We build the pipelines, set up transformations with dbt, configure quality checks, and establish monitoring. Every pipeline has an owner, an SLA, and an alert.

  4. 04

    Instrument & hand over

    We set up dashboards for pipeline health, data quality scores, and freshness. We document everything and train your team to own it after go-live.

04Areas of application

Where does this apply in your organization?

Examples of applications by business area.

Finance & Accounting

  • Automatic financial reports
  • Real-time P&L dashboards
  • Budget variance alerts

Document Management

  • Intelligent data extraction (OCR + NLP)
  • Automated document indexing

Data & BI Analytics

  • Real-time predictive dashboards
  • Pattern & opportunity detection
  • Periodic report automation
  • Natural language queries on data

Operations & Logistics

  • Real-time operational KPIs
  • Supply chain visibility dashboards

Sales & Marketing

  • Customer analytics & segmentation
  • Marketing attribution modeling

Do your reports not add up?

In a 45-minute diagnostic session we review your current stack and tell you what it would take to sort it out.

Book a diagnostic