Solutions
Paradime for Retail
Model inventory, orders, and customer data from every channel in dbt™, refreshed on a schedule. DinoAI agents fix the overnight failures and create the Monday morning reports. Merchandising, marketing, and finance work with the same numbers.
#trading
Priya@Paradime how did the weekend promo do by channel?
- Read 212K orders from
Databricks
- Compared against the previous weekend
- Used the governed net sales metric
Use Cases
What retail data teams run on Paradime
From the overnight order load to the segment that goes back to the marketing tools.
Ingest
Every channel in one warehouse
Schedule loads from Shopify, the ERP, point of sale, marketplaces, and ad platforms. DinoAI generates the sources and staging models from the landed schema.
Model
Orders, inventory, and margin defined once
Define sell-through, stock cover, and contribution margin in tested dbt™ models, so merchandising and finance stop reconciling spreadsheets.
Self-heal
The overnight failure fixed before the trade meeting
When a feed changes shape or a model breaks, self-healing opens a fix pull request right inside the Slack failure alert. A person reviews the fix instead of firefighting at 7am.
Report
Agent-driven reporting
A scheduled agent compares last week's sales, sell-through, and margin with the prior weeks and posts what moved to Slack before the meeting.
Activate
Send segments back to the marketing tools
After each run, use Bolt to trigger Census or Hightouch syncs to Klaviyo, Braze, or the ad platforms. Create new segments using DinoAI agents directly in Slack.
Spend
Control warehouse cost during peak season
Use Radar to track spend by model and workload with budgets and alerts. A DinoAI agent rewrites the expensive models before Black Friday volume arrives.
Customers
Why consumer brands choose Paradime
Emma, Lyst, Suitsupply, Psycho Bunny, and Mr & Mrs Smith run their analytics on Paradime.
“We went from spending 60-70% time on bug fixing to spending most of our time on feature work, using DinoAI and running all our pipelines in Bolt.”
Prompts
Ask DinoAI the retail questions
DinoAI reads your code base, warehouse schema, run logs, and lineage before it acts. Run these in the Code IDE, from Slack, on a Bolt schedule, or from Claude or Cursor through the MCP server.
Build a daily stock-cover model
In Agent mode, DinoAI builds units on hand, run rate, and days of cover by SKU and location, with tests for completeness.
Try it in ParadimeBuild a daily inventory_cover model from stg_inventory_snapshots and fct_sales that reports units on hand, 7-day run rate, and days of cover by SKU and location. Test that every active SKU appears once per day.
Roles
Built for every role in a retail data team
What each role gets out of Paradime and DinoAI.
One workspace for development, scheduling, and monitoring
Warehouse spend by team and workload, with budgets
Reliability across every schedule from run history
An agent fixes failed runs as pull requests, with no 6am pages
Trust
Built with zero-trust security
Security, deployment, and access controls that meet enterprise security requirements.
Enterprise security
SOC 2 Type II and GDPR. Single sign-on with Okta, Microsoft Entra ID, Google, and JumpCloud. IP allowlisting for the app, API, and CLI.
Deploy in your cloud
Run as managed SaaS, or deploy the Paradime data plane inside your own AWS, GCP, or Azure account with PrivateLink support.
Every agent action on the record
Agent session logs and traces are accessible. Paradime logs every run, change, and DinoAI action, and you can query them through the API. Store run artifacts in your own S3 bucket.
Agents propose, people approve
Agents ship changes as pull requests that run through CI like any other change. Admins manage warehouse access for DinoAI. Monthly credit budgets cap background agents per workspace, user, and agent.
Join industry leaders winning with DinoAI agents
Data engineers and analysts at the most AI-native startups and enterprises use Paradime's DinoAI agents everyday to become market leaders.