Retail Data Foundation
The substrate AI runs on.
Pipelines, warehouses, and surfaces that turn raw data into the answers your team acts on — and the foundation every AI layer and product (including MetriQ) depends on. Decisions, not dashboards.
A retail data foundation
Reporting, automation, and AI are only as good as the retail data underneath them.
We connect product, order, customer, inventory, pricing, fulfillment, and returns data into one foundation — with source-of-truth rules settled first — so every dashboard, agent, and MetriQ signal reads the same numbers.
The pipeline we engineer
From source to decision, in minutes — not days.
The flow below is the canonical Destm data foundation. Each stage is observable, idempotent, and re-runnable. Backfills do not page anyone at 3am.
SOURCES
Shopify · SAP · Postgres · GA4 · Stripe
INGEST
Airbyte · Fivetran · Custom CDC — idempotent, observable
WAREHOUSE
dbt-modeled on Snowflake / BigQuery — staging → intermediate → marts
CONSUMERS
MetriQ NL queries · BI · ML training · Slack alerts · Reverse ETL
Throughput
60M+ events/day
Hot-path freshness
Sub-second
Analytical SLA
5 minutes
Tenant onboarding
6 weeks → 4 days
What ships in every pipeline
Every capability is a layer we own end to end.
7 layers · idempotent · observable · backfillable.
Real-time analytics dashboards
Sub-second freshness on the metrics your team checks first thing in the morning. Drill-down from rollup to row, with the SQL behind every chart inspectable.
Customer intelligence
Segmentation, LTV prediction, churn scoring, propensity models — fed back into the channels where they actually move outcomes (email, ads, on-site personalization).
ETL / ELT pipelines
Airflow, dbt, Fivetran, custom Rust workers — whatever the source demands. Idempotent, observable, with backfills that complete without paging anyone at 3am.
Revenue attribution
Multi-touch, time-decay, or your own model — built on first-party data, not the platform's self-reported numbers. So you stop overpaying for assists.
Forecasting & demand modeling
Cohort, season, channel, and SKU-level forecasts that feed procurement and ad spend. Updated daily, not quarterly. Confidence intervals included.
Data strategy and governance
The roadmap that aligns data work to business goals — plus the quality, privacy, and ownership rules the rest of the pipeline runs on. Agents and models can't be trusted or permissioned on an ungoverned estate: it's the layer the AI readiness audit checks first, and what AI agents get scoped against.
Intelligent log and system analysis (AIOps)
NLP and LLMs read logs and alerts — GCP, AWS, Opsgenie-class tooling — to interpret errors and speed up resolution before someone gets paged. Internal-only and fast payback: often the lowest-risk entry point into AI agents work, with no customer exposure.
Reference architectures
3 patterns we ship most often.
Most data pipelines we engineer fall into one of these shapes. We name them so the conversation skips the abstract and starts at the concrete trade-off.
Streaming
Sub-minute decisions
Best for
Fraud scoring · live pricing · anomaly alerts
Typical stack
Trade-off
Higher infra cost. Worth it when seconds matter.
Batch + warehouse
Hourly to daily refresh
Best for
BI dashboards · finance close · marketing attribution
Typical stack
Trade-off
Cheaper. Slower freshness. The default until streaming earns its place.
Reverse ETL
Warehouse → tools the team uses
Best for
Customer enrichment in CRM · audience sync to ad platforms · sales triggers
Typical stack
Trade-off
Locks decisions to warehouse cadence. Best when the warehouse is the source of truth.
Most production stacks blend two of these. Discovery picks the right blend for your data shape, your latency needs, and your team's operating cost.
Before the dashboards
First we decide who owns which truth.
Most reporting and AI failures trace back to two systems disagreeing about the same number. Every entity gets exactly one owning system before a single dashboard gets built.
Illustrative map. The real one is built in discovery against your systems and sync constraints.
Related from the iQ Suite
Skip the dashboard. Ask the data.
Where we built
Multi-tenant retail data platform, 4 brands on shared infrastructure.
60M+Events processed per day — sub-second freshness on hot-path metrics, tenant onboarding down from 6 weeks to 4 days
Read the case studyAI-assisted QA included.
Tests aren't a separate line item — they're how engineers ship covered code. Lower QA budget, faster feedback, better coverage than traditional QA cycles.
Auto-generated E2E
Playwright + LLM scaffold tests from product flows.
Self-healing selectors
Tests don't break when copy or DOM shifts.
Production replay
Real traffic patterns become regression suites.
PR-level impact
Only the relevant tests run on each diff.