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The Process

From manifest to optimized sequence in under 90 seconds.

Infralo fits into your existing dispatch workflow — no driver retraining, no new apps required. Your stops go in, the optimized sequence comes out.

Abstract flow visualization showing route stops being reordered and optimized in sequence

Step by Step

Four steps from route data to delivered manifest.

Connect your TMS or upload a stop manifest

Infralo connects to your existing TMS via REST API or pre-built connector. Alternatively, upload a CSV stop manifest directly. No manual data re-entry — the stops come in exactly as your dispatch system knows them: address, requested window, customer reference.

Infralo scores each stop for availability risk

The availability signal layer runs each stop address against our residential availability model. Each stop gets a confidence score (0.0–1.0) indicating predicted home-presence probability at the planned delivery window. Stops below threshold are flagged for re-sequencing or dispatcher review.

Engine re-sequences the manifest to minimize predicted failures

The sequencing engine re-orders stops to maximize the number predicted to succeed on first attempt — while respecting vehicle capacity constraints, time-window commitments, and geographic clustering. For a 50-stop route, this typically takes 15–30 seconds.

Syncs to your existing dispatch system or driver app

The optimized manifest is pushed back to your dispatch console or driver app via webhook — in the same format it came in. The driver sees a re-ordered stop list with no new interface. Dispatchers see a side-by-side of original vs. optimized sequence with predicted success rates.

Under the Hood

Not a black box — here's how the engine works.

Regional carrier ops leads want to understand the mechanism before they trust it with their routes. Here's what Infralo actually does.

The Signal Model

How availability scores are built

Scores are derived from aggregated delivery outcome data — success / failed attempt / no-answer — triangulated across multiple carriers at the zip+4 and address level. Time-of-day decay is applied: a stop with high morning availability doesn't carry that score into the afternoon. Scores are updated continuously and re-computed per run.

Re-sequencing Constraints

What the engine can and can't move

The engine operates within your existing constraints: hard time-window commitments (business deliveries with receiving hours), vehicle capacity caps, and geographic clustering bounds. Stops that can't be moved without violating constraints are flagged rather than forcibly re-sequenced — dispatchers see the conflict explicitly.

Low-Confidence Handling

What happens when signals are sparse

In zones with sparse availability data (new delivery areas, low-volume routes), Infralo flags affected stops with a "low signal confidence" indicator rather than assigning an unreliable score. Dispatchers can decide whether to attempt, reschedule, or leave in original position. The engine never guesses silently.

Output Format

What comes out the other end

The output manifest includes: re-ordered stop list, availability_score per stop, a "moved" flag for re-sequenced stops, predicted first-attempt success rate for the full route, and conflict flags for any stops that couldn't be re-sequenced. All in JSON or CSV, matching whatever structure you sent in.

Common Questions

Ops leads ask these before running a pilot.

No. Infralo is a sequencing layer, not a routing engine. It assumes your existing routing software has already determined the set of stops for each route and the time-window constraints. Infralo then re-orders those stops within those constraints to maximize first-attempt success. Your existing TMS or routing tool keeps doing what it does — Infralo adds the availability intelligence layer on top.
The availability signal layer uses aggregated, anonymized delivery outcome data — success, failed attempt, no-answer — from carrier networks, combined with time-of-day residential occupancy patterns derived from aggregate mobility data. We do not track individual residents, use GPS location of people, or access personal schedules. All signals are pattern-level, not individual-level.
For typical regional carrier route sizes (30–150 stops per route), re-sequencing takes 15–60 seconds end-to-end via API. Batch processing for a full day's routes (10–20 routes simultaneously) typically completes in under 3 minutes. The system is designed to run as part of your pre-dispatch workflow — trigger it after your morning route building, receive results before drivers depart.
Yes, in most cases. Infralo outputs the re-sequenced manifest in the same format it received — JSON via API, CSV, or direct webhook push. If your driver app receives stop lists from your dispatch system, Infralo pushes the optimized sequence back to that system, and the driver app gets the updated list exactly as it normally would. Driver-facing interface changes: none.
Stops in low-signal zones (new delivery areas, low-carrier-density regions) are flagged with a "low_confidence" indicator in the output manifest rather than being assigned potentially unreliable scores. Dispatchers see these explicitly and can decide whether to re-sequence, attempt as-planned, or schedule for a different time window. We never silently apply a low-confidence score as if it were reliable.

Run a Pilot

See it on your actual routes.

Early access pilots use your real route data. We'll walk through a setup call and get Infralo connected to your existing system in the same week.

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