AI Delivery TAT Tracking for Logistics & Distribution
Digitize delivery turnaround time with QR + geolocation-verified capture and analytics, so last-mile TAT is measured, trusted and acted on, not estimated.
Problem: Delivery turnaround time is self-reported and unverifiable, so last-mile performance is estimated instead of measured.
What we build: QR + geolocation-verified capture at each delivery step, digitized end-to-end and multilingual, feeding a TAT analytics layer.
Outcome: Trusted, verifiable delivery TAT that teams can measure and act on, instead of disputed manual logs.
Delivery TAT tracking becomes trustworthy the moment each drop is confirmed with a scanned QR and a live geolocation check instead of a typed-in timestamp, turning last-mile turnaround from an estimate into a measured, disputable-proof number. That single shift is the difference between a distribution team arguing over whose log is right and one that can point to where and when a shipment actually landed.
Most logistics operations can tell you precisely when an invoice was raised and almost nothing reliable about when the goods reached the customer. That gap, the real last-mile turnaround, is where SLAs slip, penalties get contested, and route performance stays a guess. Below is how the accountability problem plays out and what a verified-capture build does about it.
Why last-mile TAT stays a guess
The core issue is that delivery confirmation is a human report, not a fact. A driver marks a stop complete on paper or over a call, a supervisor keys it in hours later, and the delivered time becomes whatever fits the day’s story. There is no location proof, so a customer claiming a late or missing drop and a driver claiming an on-time one both have equal standing and neither can be settled. Aggregate that across thousands of stops and the TAT dashboard leadership reviews is built on numbers no one can defend. You cannot improve a metric you cannot trust, which is why last-mile efficiency projects tend to stall before they start.
What a verified-capture build looks like
The fix is to move the truth to the point of delivery. For a consumer-goods major, we built exactly this: SAP embeds a QR on every invoice, the driver scans it at the destination, and the app compares the customer’s stored location with the driver’s live GPS. Inside the distance threshold, the drop auto-confirms; outside it, or on a duplicate scan, or when a customer has no stored coordinates, the event is flagged and logged rather than waved through. The app is multilingual so field staff use it without friction, and the whole thing runs alongside the existing SAP flow rather than ripping it out. The output is delivery TAT digitized end-to-end, with first-time, duplicate, out-of-threshold and missing-location cases all captured systematically. The story of that build is in the Digitat delivery TAT case study.
From proof to a TAT analytics layer
Verified events are only step one; the payoff is what sits on top. Once every drop carries a place and a timestamp, TAT can be sliced by route, region, driver, customer and time of day, and outliers stop being anecdotes and become a queue to work. Missing-geolocation customers surface as a data-hygiene backlog, chronic out-of-threshold stops point to bad addresses or gamed scans, and genuine SLA breaches are provable to the customer instead of argued about. This is the same discipline that makes a broader supply-chain control tower work: clean, trusted signal at the edge feeding decisions at the center.
We are a roughly twenty-person senior India-based studio, and you own the code, models and data at handover, no lock-in, with a client reference available under NDA on a call. If measured last-mile TAT would change how you run distribution, see the full delivered-build ledger or scope your version, and browse related analytics and custom-software work.
Questions, answered.
How is delivery TAT actually verified instead of self-reported?
The proof lives in the capture step, not the log. In the Digitat build, a QR embedded on each invoice is scanned at the drop point and the app matches the customer's stored coordinates against the driver's live location before it will mark a delivery complete. In-threshold scans auto-confirm; out-of-threshold, duplicate and missing-location scans are flagged rather than quietly accepted, so every TAT figure traces back to a place and a moment.
Why can't we just track TAT from our SAP or order system?
Order systems know when an invoice was generated, not when goods physically reached the customer, and the gap between those two events is exactly the last-mile TAT nobody measures. Without a verified drop confirmation, the delivered timestamp is whatever a driver or supervisor types in later, which is why disputes are unwinnable. A geo-verified capture layer sits alongside SAP and fills that blind spot without replacing your core system.
Will field staff who don't share a language actually use it?
That is usually where these rollouts die, so the capture app is built multilingual from the start and the scan-plus-confirm flow is deliberately short. Drivers point at a QR and get an immediate accepted-or-flagged response, which is faster than filling a paper slip. Adoption tends to hold because the tool removes work rather than adding a form.
30 minutes with the founding team. Bring the problem; leave with a scope, a timeline, and the number it should move.