Overview
A fast-growing transportation management software provider partnered with CloudEQS to replace a nonexistent data foundation with a secure, governed Snowflake architecture and launch a customer-facing AI analytics product. In just two quarters, the CloudEQS team built 13 foundational data models, stood up enterprise-grade RBAC and row-level security, and rolled out five persona-based Snowflake Cortex Analysts to 62 customers, turning a former reporting bottleneck into a new revenue line.
Client Snapshot
- Industry: Transportation Management Software (Freight & Logistics)
- Company Stage: Series B-funded, ~150 employees
- Product: A transportation management system (TMS) used by carriers, brokers, and hybrid freight operators
- Partner Technology: Snowflake, including Snowflake Cortex
The Challenge: Rapid Growth Outpacing Data Infrastructure
The client, a fast-growing transportation management system (TMS) provider serving the freight industry, had a data problem hiding behind every growth story:
- Zero data foundations. The company had no formal data models in place, no bronze-to-silver pipeline, no governed source of truth. Every downstream request, reporting or otherwise, started from scratch.
- No in-house data engineers. No internal team to build or own their data foundations.
- Customer reporting demands outpacing capacity. Adding 200+ customers a month, each one arrived with its own reporting expectations. Without foundational models or a scalable architecture, custom reporting requests became one-off scrambles that the business had no realistic way to keep up with.
- Skillset gaps across the stack. Beyond headcount, the team lacked the specific expertise the solution required: data modeling with dbt, Snowflake Cortex, and Snowflake security and architecture, including role-based access control (RBAC) and row-level security (RLS) for a multi-tenant environment.
Underneath it all was a bigger ambition: leadership wanted to turn data from a cost center into a direct revenue-generating product via a customer-facing AI powered analytic offering that carriers and brokers could pay to use. But without a data foundation, in-house engineering capacity, or the specialized Snowflake skillset to build securely at scale, that product had nowhere to start.
The Approach: Securing the Data, Building the Foundation, Enabling Snowflake Cortex
CloudEQS was brought in as a Snowflake Premier services partner to solve both problems at once: implement the underlying data foundations and use it to launch a new, AI-powered revenue line, not just a reporting dashboard.
The engagement moved in three connected phases:
Phase 1: Security Architecture
Before building a single data model, CloudEQS stood up the security architecture the client’s multi-tenant environment demanded. That meant role-based access control (RBAC) to govern who could see and do what, row-level security (RLS) to enforce those permissions down to individual records, and tenant-level data isolation to guarantee that one customer’s data could never be visible to another. This security layer became the foundation everything else was built on top of, not a feature bolted on afterward.
Phase 2: Data Foundations
With governance in place, CloudEQS designed and built 13 foundational data models in just two quarters. Source data from the client’s product database (Cosmo DB) and Salesforce was ingested via Fivetran into Snowflake, then transformed through a bronze-to-silver pipeline managed with dbt, all under proper code versioning and control, and all operating within the RBAC and RLS policies established in Phase 1.
Phase 3: AI-Powered, Persona-Based Analytics
With a governed data foundation in place, CloudEQS built 5 domain-specific Snowflake Cortex Analysts, each mapped to a specific user persona within the client’s customer base and backed by its own semantic views. Rather than a single generic chatbot layered on top of raw data, each Cortex Analyst was purpose-built to answer the questions about a specific type of user type, such as a dispatcher, safety manager, or finance lead, would ask.
The final piece was embedding this experience directly into the client’s own product, so customers interacted with AI-powered analytics natively, without ever knowing Snowflake was underneath. Every query respected strict tenant boundaries: RBAC and row-level security policies were applied per user and per tenant, ensuring one customer’s data and users never crossed into another’s.
Architecture at a glance
Data flows from source systems, Product and Sales, through a Fivetran-orchestrated ingestion layer into Snowflake, where it’s transformed via dbt across Bronze and Silver layers, then served to five persona-based Cortex Analysts and their semantic views behind a Snowflake Agent, and delivered into the front-end application, all governed by per-user row-level security, per-tenant data isolation, and version-controlled code.
The Results: Secure Foundations, Embedded AI, and a New Revenue Line
In two quarters, the engagement delivered:
- 13+ foundational data models built from the ground up, replacing a data environment with no formal architecture.
- 5 persona-based Cortex Analysts, each with its own semantic view, tailored to distinct user roles across the customer base.
- 62 customers rolled out from pilot to full production within two quarters.
- New revenue line via AI-powered Data Product embedded natively inside the client’s platform, governed by enterprise-grade RBAC and row-level
Why It Matters
This engagement is a useful blueprint for any software company sitting on valuable operational data without the in-house team unlocking it. Data foundations and AI-powered products are often treated as separate initiatives, but the two are sequential: you cannot ship trustworthy, tenant-safe AI analytics without governed data models, role-based access, and row-level security underneath them first.
For fast-growing SaaS companies, the fastest path to a defensible AI feature isn’t a bigger data team. It’s a partner who can build the foundation and the product on top of it, in parallel, without slowing the business down.
About CloudEQS
CloudEQS is a Premier Snowflake Services Partner specializing in data strategy, data modeling, data engineering, data architecture, and AI enablement. Operating across the US, India, and remote teams, CloudEQS helps growing software companies turn fragmented data into governed, production-ready foundations and new AI-powered products.
Interested in what a data foundation and embedded AI analytics could unlock for your product? Contact CloudEQS to talk through your use case.
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