Embedded Logistics Analytics Software.

Embedded logistics analytics software is in-app reporting and dashboards built directly into a transportation, freight, fleet, 3PL, or shipping product. Instead of exporting data to spreadsheets or opening a separate business intelligence tool like Tableau or Power BI, it renders native, secure, multi-tenant dashboards inside the product your customers already use, with your brand and your access controls.

The core logistics analytics KPIs are on-time-in-full (OTIF), cost per mile, cost per package, carrier scorecard, fleet utilization, dwell time, load acceptance rate, and perfect order rate. Freight brokers also track margin per load and spot-versus-contract rates, while 3PLs add order accuracy and dock-to-stock time.

Predictive analytics in logistics uses historical and real-time data with machine learning to forecast outcomes such as delivery delays, demand spikes, capacity shortfalls, and freight cost changes. It shifts teams from reporting what already happened to anticipating what will happen, for example flagging at-risk loads or recommending the lowest-cost lane before a booking is confirmed.

DataBrain enforces row-level access at the query layer using a multi-tenant security model. A TMS shipper sees their loads only. A 3PL client sees their warehouse only. A carrier sees their lanes only. A fleet manager sees their vehicles only. All from the same underlying data, configured once.

DataBrain reads from any SQL-accessible data warehouse, including Snowflake, BigQuery, Redshift, Databricks, Postgres, and MySQL. Your TMS, WMS, ELD, and shipping data from systems like MercuryGate, Cargowise, Manhattan, Samsara, and Shippo already lives in your warehouse via your existing ETL, and DataBrain reads it there with no data copy.

Most product teams ship the first embedded logistics dashboard in week one and the full embedded surface in 2 to 4 weeks. Freightify deployed customer-facing freight analytics in one week instead of spending months building in-house.

Building embedded logistics analytics in-house typically costs $70K to $1M in the first year (ScienceSoft benchmark), plus several months of engineering and a multi-person data hire. DataBrain replaces that with a flat monthly subscription that includes multi-tenancy, white-labeling, and a pre-built logistics semantic layer. Contact sales for current pricing.

Power BI, Tableau, and Metabase are built for internal business intelligence. DataBrain is built for external embedding inside customer-facing SaaS. Power BI Embedded uses capacity pricing that scales unpredictably with users, Tableau carries heavy per-viewer license costs, and Metabase requires custom engineering for multi-tenant carrier, shipper, and 3PL isolation. DataBrain ships native multi-tenancy, full white-labeling, and a logistics semantic layer out of the box.

Yes. DataBrain includes a low-code dashboard builder, so product managers, operations leads, and analysts can create and embed logistics dashboards without writing SQL. At Freightify, anyone in the organization could build metrics and share data after adopting DataBrain.

Yes. Logistics SaaS teams run both from the same DataBrain instance: internal ops dashboards such as lane analysis, carrier QBRs, and exception monitoring, plus customer-facing embedded analytics like shipper portals, 3PL client dashboards, and fleet operator UIs. Different access controls, same underlying data.