Advisory · Data Foundation

Your data lives in 12 systems.
None of them agree.

POS says one thing. Inventory says another. Finance reconciles by hand every month. Without a unified data layer, every AI initiative is built on sand. We design the architecture that makes your data AI-ready.

You can’t run AI
on data that contradicts itself.

Every system has its own schema, its own update cadence, and its own definition of a “store” or a “transaction.” POS in one vendor’s cloud, inventory in a legacy WMS, finance in an eight-year-old ERP, marketing in a stack nobody can access, labor in a tool with no API.

A data lake isn’t a nice-to-have. It’s the prerequisite for everything else.

Two weeks for one answer

“What’s our margin by category by region?” takes two weeks to reconcile by hand.

Inconsistent forecasts

Demand forecasting trained on inconsistent data produces inconsistent forecasts.

No real-time cross-reference

Shrinkage detection can’t cross-reference POS and inventory in real time.

AI amplifies the mess

AI doesn’t fix fragmented data. It amplifies the problem at scale.

One intelligence layer.
Every system feeding into it.

We design the full architecture, ingestion to transformation to serving, around your actual systems and use cases. Platform-agnostic: Snowflake, BigQuery, Databricks, Redshift.

Real-time where it matters

Streaming ingestion for POS and inventory, batch for finance and marketing.

Unified data model

One definition of a store, a transaction, a SKU. No more reconciliation spreadsheets.

Quality & governance

Validation rules, alerts, and quality scoring built in, so you know a feed broke before your forecasts do.

Cloud architecture

A platform matched to your existing footprint, team skills, and budget. No vendor lock-in by default.

Architecture you can hand
to your engineering team tomorrow.

No theory. Technical specs, architecture diagrams, vendor comparisons, and a phased implementation plan, ingestion to serving.

PHASE 01
Model

Entity definitions, relationships, normalization rules.

PHASE 02
Ingest

Pipeline design with vendor evaluation and cost estimates.

PHASE 03
Govern

Quality rules, access controls, compliance.

PHASE 04
Cost out

Projected storage, compute, ingestion at your scale.

Unified data model spec & pipeline architecture

Entity definitions, relationships, and normalization rules across all systems, plus an ETL/ELT design with vendor evaluation, cost estimates, and sequence.

Data governance framework & cloud cost model

Quality rules, monitoring alerts, access controls, and compliance, plus a platform recommendation with projected storage, compute, and costs at your scale.

We built the pipelines
before we advised on them.

Ward’s platform ingests POS, inventory, ERP, marketing, and finance data through the brittle, mismatched systems retail actually runs on. You get the architecture we ship against, not one drawn on a whiteboard.

Drops recordsThe POS we designed for
CSV onlyThe ERP export reality
Since 2019The WMS with no API update
Your stackBuilt for the systems you have

Your data should work together.

One unified data layer. Every system feeding into it. Designed for the AI workloads you need tomorrow.

Get a demo

Find out what your data has been hiding.

Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.

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What are your goals?
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About your operation
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