Can customers see their own data?

Yes, through a scoped portal view showing only their shipments. It is frequently a retention feature in its own right and reduces status enquiry volume at the same time.

Will it work across multiple carriers and systems?

That is the main task. Each source gets a connector and the data is normalised into common definitions, which is what makes cross-carrier comparison possible at all.

How is this different from a standard BI tool?

The dashboard layer is similar. The difference is the work underneath — unifying systems that do not agree with each other — plus a question interface for people who will never write a query, and commentary that explains movement rather than just plotting it.

How fresh is the data?

Configurable per source. Operational metrics usually run near real-time; anything pulled from a rate-limited third-party API syncs on a schedule. Every panel shows its own last-updated timestamp so nobody acts on stale numbers unknowingly.

AI Analytics Dashboards · Logistics

AI Analytics Dashboards for Logistics

Operational performance is only visible after the month has closed.

On-time performance, cost per shipment, carrier reliability, and exception rates all exist in operational systems but only become visible in a monthly report — by which point the pattern has been costing money for weeks. A live dashboard surfaces it as it happens.

Get a Free Assessment See Proof

Data freshness: LiveData freshness
Source of truth: OneSource of truth
In plain language: AskIn plain language

Typical stack

Next.js
Postgres
dbt
OpenAI
Recharts

Use cases

What this handles for logistics operators

Track on-time delivery and exception rates by carrier, lane, and customer live
Monitor cost per shipment against quoted rates to catch margin erosion early
Surface carrier performance trends before they show up in a customer complaint
Give customers a self-serve view of their own shipment performance

Carrier and lane problems visible within days rather than after the monthly review.

Context

The logistics problem underneath it

Logistics runs on documents and status enquiries — bills of lading, customs paperwork, proof of delivery, and an endless stream of 'where is my shipment' calls. Both are highly automatable, and both currently consume operational staff who should be handling actual exceptions.

Shipping documents arrive in every format and get manually keyed into the TMS
Status enquiries flood phone and email, most of them answerable from tracking data
Exceptions and delays are spotted late because nobody is watching continuously
Carrier rates and capacity live across portals, spreadsheets, and inboxes

What we build

Inside an AI analytics dashboard

We define the metrics before we build anything, because most dashboard projects fail on disagreement about what a number means rather than on engineering. Once definitions are agreed, we pull data continuously, model it once, and expose both a fixed executive view and an ad-hoc question interface.

Connectors pulling from CRM, billing, ads, support, product, and operational databases
A single semantic layer so a metric means the same thing everywhere it appears
Live executive dashboards with drill-down to the underlying records
Natural-language querying for ad-hoc questions without SQL
Anomaly detection that flags unusual movement rather than waiting to be asked
Scheduled digests into email or Slack with plain-language commentary on what changed

Deliverables

What ships with the engagement

Agreed metric definitions documented and version-controlled
Data pipeline with freshness monitoring and failure alerts
Dashboard covering the decisions you actually make weekly
Natural-language query interface over the modelled data
Scheduled digest configuration per team

FAQ