AI Solution · Logistics & Freight

AI in Logistics Software DevelopmentDispatch · Routing · ETA · Load Matching · Forecasting

Add AI dispatch, route and stop sequencing, ETA prediction, load matching and document extraction to a courier, parcel or freight platform. Miracuves builds the AI layer on a ready-made logistics base from $2,499 or into the system you already run, as 2-8 weeks of custom work, with 100% source code and a dispatcher override on every automated call.

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3,900+ Apps Published9,000+ Projects DeliveredSee past platform builds →

  • AI Layer in 2-8 Weeks
  • Shadow Mode First
  • Bases From $2,499
  • 100% Source Code
$2,499Logistics bases from
$3,399Multi-merchant courier base
2-8 weeksAI layer, custom build
6 daysReady-made base build
AI modules scoped on your own shipment history
AI dispatchStop sequencingETA predictionLoad matching
Logistics basesDAT Load Board CloneFedEx CloneLalamove Clone
  • $2,499Logistics bases from
  • 2-8 WksCustom AI build
  • 6 DaysReady-made base, FACT-005
  • 90+Ready-made solutions
  • 100%Source code yours
More than 6,000+ Companies Trust us Worldwide
In short

AI in logistics is software that makes or advises the calls dispatchers, planners and brokers make: which driver takes a job, the order of stops, the arrival time, which carrier fits a load, and whether a delivery photo is valid. Miracuves adds these modules to a ready-made logistics base from $2,499 or to your own system, as 2-8 weeks of custom work, with 100% source code.

Our Approach

How Miracuves builds AI in logistics - one decision at a time

AI in logistics is software that makes or advises the calls a dispatcher, planner or broker makes all day: which driver takes a job, in what order the stops run, when the parcel will arrive, which carrier fits a load, whether a proof photo shows a real delivery, and what a scanned bill of lading says. Miracuves builds that layer on a logistics platform it already ships, or into the one you run. The model side is covered on the AI development and machine learning pages.

Who this is for: courier and parcel operators with a busy dispatch desk, freight marketplaces and brokers matching loads to carriers, and 3PLs sitting on months of trip logs. Every model starts in shadow mode: it suggests, a person decides, and both are logged, so you see how often it agrees with your team before it acts on its own.

Each engagement opens with an NDA and a written scope that names the decision, the data it learns from, the override rules, how accuracy is tested and what it will cost per shipment to run. Data stays in your cloud accounts; hosted models such as GPT, Claude or Gemini are called with your own keys, and open models such as Llama run on your servers when the data must not leave them.

When AI is the wrong tool: if you run a small fleet in one city or have no trip history yet, the nearest-driver assignment, zone pricing and multi-stop routing in a ready-made base do the job. Launch, collect clean records, and add a model when there is something to learn from.

  • Shadow mode first: the model suggests, dispatch decides
  • Tested on your own trip history, never a demo dataset
  • Every AI decision logged with its inputs and an override
  • Running cost per shipment estimated before the build
  • 100% source code, prompts and training scripts handed over

Written by Miracuves AI Team · September 2026 · Updated September 2026See past platform builds →

9,000+Projects delivered since 2010
3,900+Apps published by Miracuves
90+Ready-made solutions to start from
6 daysReady-made clone delivery
2-8wMiracuves custom build timelines
100%Source code ownership

Decisions First

Six decisions to make before any model is trained

A logistics model is only as useful as the choice it takes off a dispatcher, planner or broker. These six are settled in writing before a line of model code.

Decision

Which call the AI makes or advises

Assigning a driver, ordering stops, quoting an arrival window or pricing a load. Pick one call with a clear cost of getting it wrong before picking a model.

Scoped upfront
Data

What history you already hold

Completed trips, GPS pings, stop times and failed-attempt reasons. A few months of clean records matter more than a bigger model; without them, start on rules and collect.

Your data
Control

Who can overrule it

Which suggestions a dispatcher approves, which run on their own, and how an override is logged so it teaches the next version.

Scoped upfront
Speed

How fast the answer must come

Re-sequencing a van mid-route needs an answer in seconds; overnight planning can wait minutes. That choice decides where the model runs.

Scoped upfront
Baseline

How success is measured

Late drops, empty miles or desk minutes per order, measured before switch-on, so the model is judged against your own numbers rather than a vendor benchmark.

Before launch
Running cost

What each prediction costs

Map and traffic calls, hosted-model tokens for documents and chat, GPU time for photo checks. Estimated per shipment before the build is quoted.

Scoped upfront

Logistics Bases

Platforms the AI layer is built on

Five of these have product pages and launch in 6 working days as ready-made bases. An AI module counts as part of a base only where its product page says so; everything else on this page is custom work on top, 2-8 weeks. The sixth card brings models to a system you already run.

View All 90+ Solutions
01Freight marketplace6 days

DAT Load Board Clone

Load board with a carrier TMS behind it; its page lists AI rate suggestions, voice load posting and email-to-load import as built.

Freight + TMS4 Roles
02Parcel6 days

FedEx Clone

Parcel shipping with routing, tracking and proof of delivery, where learned ETAs and document extraction are added as modules.

$2,4993 Apps
03Van & truck6 days

Lalamove Clone

Booking by vehicle type with auto-assignment and live tracking; AI dispatch scoring and demand forecasts sit on top.

$2,4993 Apps
04Multi-merchant courier6 days

Postmates Clone

Many merchants sharing one rider fleet, the setting where batching and assignment models pay off first.

$3,3993 Apps
05Hyperlocal6 days

Dunzo Clone

Pick-up-and-drop inside the hour, where short windows make ETA prediction and exception alerts worth building.

$2,4993 Apps
06Your own stackcustom scope

AI in Your Existing TMS

Dispatch, ETA or document models connected to the TMS, WMS or ERP you already run, through its API.

Get Pricing2-8 Weeks

AI Modules

Where the AI sits - shipper, driver and dispatch

What the AI layer adds to each surface of a logistics platform. Each item is a module chosen in the written scope, not a bundle: most operators start with one or two on the dispatch console and add the customer and driver side once those have proved themselves.

Shipper & Customer App

iOS & Android · customer side
8 modules
  • Predicted delivery window from your own trip history
  • Where-is-my-order assistant that reads the shipment record
  • Suggested pickup slot when capacity is tight
  • Parcel size estimate from a photo at booking
  • Delay notice sent before the customer asks
  • Address check that flags incomplete drops
  • Returns and redelivery answers in several languages
  • Handoff to a human agent with the chat attached

Driver App

iOS & Android · driver side
8 modules
  • Stops re-sequenced when a job is added or canceled
  • Job offers ranked by distance, vehicle and shift
  • Proof photo checked on the phone before upload
  • Voice notes turned into delivery exception codes
  • Scan of bills of lading and receipts into fields
  • Assistant for gate codes and delivery instructions
  • Routing that respects breaks and shift end
  • Fallback to the last good route when offline

Dispatch Console

Web · dispatchers and operations
8 modules
  • Auto-assignment with a one-click dispatcher override
  • Late-risk alerts ranked by customer impact
  • Demand and capacity forecast by zone and hour
  • Load-to-carrier match scores with the reasons shown
  • Bills of lading and invoices extracted to fields
  • Review queue for flagged proof-of-delivery photos
  • Accuracy and override-rate reports per model
  • Decision log for every AI suggestion

Three Routes

Ready-made vs custom vs white-label - where your logistics AI should live

Three ways to put AI into dispatch, routing and tracking. They differ in who owns the models, where your trip data sits and whether the AI can be tuned to your lanes.

MetricFive things that decide cost, speed and reach
Miracuves

Ready-made base + AI

Logistics base, with AI modules added

AI in your own system

Models wired into your TMS or WMS

White-label AI SaaS

A routing or visibility tool, rented

01Time to launch
6 days + 2-8 weeksBase first, then each AI module
2-8 weeksPer module; longer is quoted in writing
Days to weeksDepends on the vendor's onboarding
02What you pay
From $2,499Base published; AI module quoted
Scoped quoteMilestone billing, agreed first
Monthly feeOften per vehicle, driver or shipment
03Models and code
100% yoursCode, prompts, scripts, trained models
100% yoursHanded over in your accounts
Stay with the vendorYou rent the output
04Your trip data
In your cloudUsed only for your models
In your cloudRead through your API
On the vendor's serversTerms set by the vendor
05Best for
New operatorsPlatform and AI from one team
Running operatorsA TMS you keep
Trying one featureBefore owning a model
Start from a ready-made base if…

you are launching the platform and the AI together and want both in your name. Freight marketplaces start on the DAT Load Board Clone; parcel and courier operators on the FedEx Clone or Lalamove Clone.

Choose custom or white-label if…

you already run a TMS or WMS your team relies on (custom modules, 2-8 weeks each), or only want to test one feature such as ETAs before owning a model (white-label).

Questions Before You Build

What logistics teams ask before adding AI

Straight answers on when AI pays, what already ships in a base, how load matching works, what data you need, what it costs to run and how to judge a vendor.

When is AI worth adding to a logistics platform, and when is it not?

AI earns its place where a person makes the same judgment many times a day and there is history to learn from: assigning jobs across a large fleet, ordering dozens of stops, quoting arrival windows customers hold you to, matching loads to carriers across many lanes, or keying in paperwork that arrives as scans and emails.

It is the wrong first step when volume is small, the history is thin or messy, or the decision is already settled by a clear rule. A courier with a handful of drivers in one city gets most of the value from the nearest-driver assignment, zone pricing and multi-stop routing a ready-made base already runs. In that case launch first, collect clean trip records for a few months, and add a model once there is something for it to learn.

Which AI features already ship in a Miracuves logistics base?

It depends on the base, and this page counts only what the product pages say. The DAT Load Board Clone page lists AI rate suggestions from lane, equipment, weight and distance, voice load posting through speech transcription, inbound email import that turns freight messages into load drafts, and AI-assisted message drafting as built and demonstrable, and sells AI coverage as an add-on module.

The parcel, courier and van-booking bases behind the FedEx, Lalamove and Dunzo clones ship driver assignment, route optimization, live tracking and proof-of-delivery capture; the Lalamove and Dunzo pages also list a traffic- and weather-aware AI routing add-on. ETAs learned from your own trips, dispatch scoring, photo checks, document extraction and support assistants are custom modules on top, 2-8 weeks.

How does AI load matching work in a freight marketplace?

Load matching ranks carriers for a load, or loads for a truck, instead of leaving both sides to scroll a board. A useful score combines equipment fit, pickup timing, the truck's position and direction of travel, the carrier's lane history and reliability, verification and insurance status, and rate expectations, and it shows its reasons so a broker can accept or reject it.

Carriers that fail verification never enter the ranking; the score sits on top of those checks. Accepted and refused offers, cancellations and late deliveries feed the next version. Miracuves sets out the design in AI Load Matching in a Digital Freight Marketplace: Improving Carrier Fit, Route Efficiency, and Rate Decisions. On the DAT Load Board Clone, matching is planned around your business model and built as custom scope; a board without enough loads and carriers yet should build that liquidity before it builds a matcher.

What data do you need before AI dispatch or ETA prediction?

Models learn from what your platform already records, so a data check comes before any quote. If the history is short or scans are unreliable, the first weeks go into fixing capture in the driver app, and the model follows. For dispatch and ETA work, these records matter most:

  • Completed jobs with pickup, drop, promised window and actual arrival times
  • GPS pings at a steady interval, with gaps and device changes marked
  • Stop dwell times and failed-attempt reasons, not only a delivered flag
  • Driver, vehicle and shift records, including who accepted or rejected each job
  • For document work, a sample of real bills of lading, invoices and rate confirmations with the correct values keyed in

What does AI in logistics cost to build and to run?

Build and running costs are separate. The platform underneath is a published one-time price if you start from a ready-made base: from $2,499 today. Each AI module is custom work, quoted in writing after the data check and typically built in 2-8 weeks; one ETA or assignment model on clean history sits at the short end, several modules across regions at the long end.

Running costs grow with volume and are estimated per shipment before you sign: map and traffic API calls, hosted-model calls for documents and chat, GPU or vision API time for photo checks, storage for GPS history, and periodic retraining. Keys and bills sit in your accounts. Where call costs would outgrow the value, an open model on your own servers is priced as the alternative.

How should you evaluate an AI logistics vendor?

Ask to see the decision, not the model. A credible vendor names the one call the AI will make or advise, the baseline it will be measured against, and how it behaves when it is wrong. These five questions separate a working plan from a slide:

  • Will it be tested on my own history, held back from training, and reported by zone and vehicle?
  • Does it run in shadow mode before it acts, and can a dispatcher overrule any decision?
  • What happens to dispatch if the model or a hosted API is down?
  • What will it cost per shipment to run, at today's volume and at three times it?
  • Do I receive the code, prompts, training scripts and model versions in my own accounts?

How It Works

One AI dispatch decision - event to feedback

Every module on this page follows the same loop, shown here for assigning a job to a driver.

  1. An event arrives

    A new order, a driver going offline or a late scan triggers a decision.

  2. Inputs are assembled

    Distance, vehicle, load size, shift time and stop history are read from the shipment record.

  3. The model ranks options

    Drivers, stop orders or carriers are scored, and the reasons are stored beside the score.

  4. Dispatch approves or overrides

    In shadow mode a person decides; later, routine calls run on their own within set limits.

  5. The outcome feeds back

    Actual arrival, accept or reject and any exception go into the next training run.

Ranked

Driver suggested

Top three drivers shown with the reason for each.

Predicted

ETA refreshed

Arrival window recalculated on each GPS ping.

Flagged

Exception raised

Late-risk job or failed photo check sent to a person.

Business Case

6 places the AI layer earns or saves

An AI module has to pay for its build and its running cost. These are the lines where logistics operators usually look for that return, each checked against the baseline measured in the first week.

Premium delivery windows

Once predicted windows hold, a tighter window can be sold at a higher fee to merchants and customers who need it.

Revenue: premium fee

Fewer failed attempts

Address checks and pre-arrival notices cut the second trip that a missed drop costs.

Saving: repeat trips

More drops per shift

Better stop order and batching fit more deliveries into the same driver hours.

Saving: driver hours

Dispatch desk capacity

Routine assignments run on their own, so the same desk handles more volume and spends its time on exceptions.

Saving: desk time

Faster load coverage

Freight marketplaces can charge for quicker, better-matched carrier coverage on hard lanes.

Revenue: match fee

Data plans for merchants

ETA feeds, forecast reports and exception alerts sold to business shippers as a paid plan or API.

Revenue: merchant plan

No promised savings: each card shows where an AI module usually earns or saves, not a result. Miracuves measures your baseline before switch-on and reports against it; the size of any gain depends on your volume, lanes and the quality of your data.

Who This Is For

Who should add AI to logistics software?

Four kinds of operator with enough volume and history for a model to learn from.

Courier & Parcel Operators

Desks assigning hundreds of jobs a day by hand, where auto-assignment and stop sequencing leave dispatchers free for failed drops and complaints.

Parcel & courier

Freight Marketplaces

Load boards and brokers that need carrier-fit ranking, rate guidance and documents read from email, on the DAT base or their own.

Digital freight

3PLs & Fleet Owners

Operators with months of trip logs who want staffing and vehicle forecasts, and a warning before an SLA is missed rather than after.

Contract logistics

Retail Own-Fleets

Brands delivering with their own drivers who want customers to see a predicted window and ask an assistant instead of calling the store.

Own-fleet delivery

Why Miracuves

How Miracuves compares to typical logistics AI vendors

Logistics AI quotes often price the model and leave out the data work, the dispatcher override and who owns the result. These are the terms Miracuves puts in writing.

  • Code, prompts & scripts100% Yours
  • Where models runYour cloud
  • Shadow mode before automationAlways
  • NDA before data is sharedDay One
  • Base platform pricingPublished
  • Post-launch support60 Days
  • Running cost per shipmentEstimated upfront
  1. 01

    Your data stays in your accounts

    Trip history, GPS traces and scanned documents are processed in your cloud, and hosted-model calls go out under your own keys. Your records are not used to train a model for another client.

  2. 02

    A human override on every automated call

    Any assignment, route or ETA the model sets can be reversed from the dispatch console, and each reversal is logged as training signal for the next version.

  3. 03

    Accuracy measured on your lanes

    Weeks of your own history are held back from training and used to test the model, reported by zone, vehicle and time of day in plain terms rather than a vendor benchmark.

  4. 04

    Rules first where rules are enough

    If nearest-driver assignment or zone pricing in the base already does the job, Miracuves says so. A model is scoped only where volume and data give it something to learn.

  5. 05

    Honest timelines, not marketing numbers

    A ready-made base takes 6 working days; each AI module is 2-8 weeks of custom work, and any longer data or integration work is quoted in writing before payment.

Architecture

Logistics AI architecture - data, models and the override

The shipment record stays the source of truth. Models read from it and write their suggestions beside it, never over it, unless a rule or a person approves, so the audit trail shows who decided what and why.

  • 01

    Event stream

    Bookings, scans, status changes and GPS pings captured as they happen.

  • 02

    Feature store

    Travel times, dwell times and driver history kept ready for scoring.

  • 03

    Model services

    Assignment, ETA, vision and document models behind one internal API.

  • 04

    Decision log

    Every suggestion, override and outcome stored for audits and retraining.

The order is scoredSize, window and zone decide which drivers are eligible
A driver is ranked firstThe model picks; a dispatcher can overrule in one click
The ETA is predictedLearned from your own stop times, refreshed on each ping
The proof is checkedA vision model reviews the photo before the job closes

Built withAI DevelopmentMachine LearningData EngineeringNode.js Development

Technology Stack

Logistics AI stack - solvers, models and geo data

The tools behind the AI layer. The exact mix follows your volume, your data and where the models must run; architecture notes are part of the handover.

Py
PythonModel training and services
OR
OR-ToolsRoute and stop sequencing
XG
XGBoost / LightGBMETA and demand models
PT
PyTorchVision checks on POD photos
LM
GPT · Claude · GeminiDocuments and assistants
Ll
LlamaOpen model, self-hosted
OC
OCR engineScanned BOLs and invoices
PG
PostgreSQL + PostGISShipment and geo data
Kf
Kafka / Redis StreamsScan and GPS events
Vc
pgvectorSOP search for assistants
GM
Google Maps / MapboxDistances and traffic
Mf
MLflowModel versions and runs
Dk
Docker / KubernetesModel serving
Gf
GrafanaAccuracy and drift dashboards
No
Node.jsDispatch API
FL
FlutterDriver and shipper apps

Quality Standards

Model QA for logistics AI

A logistics model fails in ways a demo never shows: a holiday week, a new depot, a driver who always parks round the corner. Each gate below tests for one of those.

  • Back-test on your historyGate
  • Shadow-mode runGate
  • Override and fallbackGate
  • Data access reviewGate
  • Handoff packageGate
  • 60-day monitored supportGate

Delivery Gates

Six checkpoints before a model goes live

01

Back-test on your history

Models are scored on weeks of your own trips they never saw in training, split by zone, vehicle type and time of day, so a weak depot or a bad hour shows up before launch.

02

Shadow-mode run

The model runs beside your dispatchers on live orders without acting. Its suggestions and the human choices are compared, and automation is switched on only where the two agree.

03

Override and fallback

Every automated decision can be reversed from the console. If a model or a hosted API is down, the platform falls back to the base rules instead of stopping dispatch.

04

Data access review

No keys in source; hosted-model calls go through your accounts; driver locations and customer addresses are masked in prompts and logs wherever the task allows.

05

Handoff package

Repository, training and evaluation scripts, prompts, model versions, a retraining runbook and the accuracy and override dashboards.

06

60-day monitored support

Accuracy drift, bad extractions and noisy alerts caused by in-scope configuration are fixed, and Miracuves stays reachable through the first retraining cycle.

Delivery Process

AI module delivery - 2-8 weeks, scoped in writing

A data check comes before the quote, and shadow mode comes before automation. The NDA, written scope and handover of code, prompts and models come with every engagement.

  1. Week 0

    Brief, NDA & Data Check

    Tell us the decision you want the AI to make. The NDA is signed first, then a sample of your trip, scan and document records is reviewed and the module is scoped in writing.

  2. Week 1

    Baseline & Pipeline

    Current performance measured on your own records, and the event and feature pipeline set up in your cloud account.

  3. Build

    Model Build & Back-Test

    Model trained, tested on held-back weeks and wired to the dispatch console with its override and fallback rules.

  4. Shadow

    Shadow Mode on Live Orders

    The model suggests while your team decides. Agreement, errors and running cost are reported against the baseline.

  5. Handover

    Switch-On & Handover

    Automation enabled for the calls that passed shadow mode; code, prompts, scripts and dashboards handed over, and the 60-day support window starts.

Week 0NDA signed
Week 1Baseline set
ShadowLive comparison
HandoverCode and models

The 2-8 weeks are build time; your data decides where in that range

One module on clean trip history sits at the short end; several modules, patchy scans or a TMS without an API sit at the long end. If you are also launching a ready-made base, its 6 working days come first. Data access, API credentials and a dispatcher for shadow review are on your side, and we list them on the first call.

See the facts ledgerCanon timelines, audited quarterly

Cost & Pricing

What AI in logistics costs

Published prices for the platform, a written quote for the AI. Logistics bases from $2,499: the FedEx parcel base is $2,499 and the multi-merchant courier base $3,399. Each AI module is custom work, quoted after the data check.

Ready-Made Base

$2,499 /from

6 working days · add AI after

  • Shipper + driver apps + dispatch console
  • Assignment and multi-stop routing
  • Live tracking and proof of delivery
  • Your branding and payment setup
  • Full source code delivered
  • 60-day post-launch support
Start With a Base
Most Asked

AI Module Build

Custom Quote

2-8 weeks · milestone billing

  • Data check and baseline
  • One or more AI modules
  • Shadow mode on live orders
  • Override and fallback rules
  • Code, prompts and scripts handed over
  • 60-day support after switch-on
Scope an AI Module

Enterprise AI Program

Enterprise

Multi-region · retraining · written scope

  • Several modules across regions
  • Retraining pipeline in your cloud
  • Accuracy and drift dashboards
  • Named engineers on your project
  • First response under 2 hours, Mon-Sat 10:00-19:00 IST
  • Ongoing retraining retainer
Discuss Enterprise

Cost factors that change the pricehow many decisions the AI makes, how clean your history is, and whether models run on hosted APIs or your own servers.

What affects project cost

The number of modules, the state of your trip and document records, the systems the models must read from and write to, and whether the data may leave your servers. Running costs - map calls, model tokens, GPU time - are estimated per shipment and billed to your own accounts.

Typical budget ranges

  • Ready-made basefrom $2,4996 working days

AI module: scoped quote · 2-8 weeks, milestone billing.

Enterprise: multi-region, retraining - contact for written scope.

Example engagement

What adding AI dispatch to a courier platform looks like in practice

An illustrative example of a typical project of this kind, with client details anonymized. Figures show what this kind of build targets, not a named client's results.

A courier operator on the multi-merchant base whose dispatch desk assigns every job by hand, and the shape an assignment and ETA project takes on top of it.

  1. 01

    Challenge

    Dispatchers pick a rider for every order from the live map, and customers are quoted a fixed window that peak hours regularly break. The decisions to automate, the override rules and the baseline are agreed in writing first.

  2. 02

    What Miracuves Delivered

    An assignment model that ranks riders by distance, load and shift, and an ETA model trained on the operator's own completed trips, both wired into the dispatch console with an override and a fallback to the base rules.

  3. 03

    Outcome

    What a build like this targets: fewer manual assignments per dispatcher and a smaller gap between predicted and actual arrival, with no rise in late drops. Results are reported against the week-one baseline, and the code and models are handed over.

2-8wCustom build window
2AI modules
100%Source code
View All Case Studies
Engagement Brief
  • BasePostmates Clone
  • AI modulesDispatch + ETA
  • Timeline2-8 weeks
  • RolloutShadow first
  • Source100% owned

Client Reviews

What Miracuves clients say about dispatch and AI builds

Three Miracuves clients, quoted word for word from their published testimonials. All three built on a Miracuves base and brought their own layer on top: a courier network, a field-service dispatch operation and a multi-service delivery business. None of them bought the AI logistics modules on this page.

★★★★★Client testimonial
"We added a multi-tenant layer for our B2B clients, our branded customer tracking page, and a custom COD/settlement module. We launched in 6 weeks."
MW
Marsha WilliamsFounder, Bmore On Demands LLC
Delivery Genie base
★★★★★Client testimonial
"Dispatch, the technician app and work orders were already there. We added parts inventory, customer sign-off with photos and SLA dashboards."
AR
Arjun ReddyCo-founder & CTO, Odient Technologies Pvt. Ltd
Field service base
★★★★★Client testimonial
"Our market, our payment partners and our categories were the work. Dispatch, the rider and driver apps and the merchant console were already built and proven."
AT
Aeygo TeamFounding team, Aeygo
Multi-service delivery base
6,000+Clients served
3,900+Apps published
35+Industries served
Read All Reviews

Why Miracuves

Six places to check us before you ever call us

Each one is either run by someone else or open to anyone. Check them in any order; the whole list takes about a minute.

Why clients choose Miracuves
01

Company registration

Miracuves Solutions Pvt. Ltd., CIN U62099MH2023PTC406639. Search the CIN on the Ministry of Corporate Affairs portal.

mca.gov.in 02

Every number, sourced

Projects, clients, prices and timelines, each one defined and sourced on our public facts ledger.

miracuves.com/facts 03

Reviews on Clutch

Client reviews published by Clutch, an independent B2B review platform, not by us.

clutch.co 04

Reviews on GoodFirms

A second, separate review platform. Read what clients wrote there too.

goodfirms.co 05

The product itself

Web app, admin panel and APK with printed credentials. Try the real thing before a single call.

miracuves.com/solutions 06

Clients, by name

Named clients describing their launches, in their own words.

miracuves.com/client-testimonials

Three promises we would stake the company on

Every promise on this site rests on these three. Each one is something you can check, not something you have to take on trust.

  • 01People you can name

    Our leadership is public, with real LinkedIn profiles, not a stock-photo team page. A named team works your build and sends you progress on WhatsApp every working day.

    Meet the leadership

  • 02Proof over promises

    Every number we publish, pricing, timelines, project counts, is defined and sourced on a public facts ledger. If we can't back a claim, we don't make it.

    Read the facts ledger

  • 03A process with a deadline

    Ready-made platforms go from kickoff to live deployment in 6 working days, guaranteed: miss it for reasons on our side and we work free until launch. Custom builds get a fixed quote after a free feasibility study.

    Get a feasibility study

Explore Miracuves

Miracuves catalog - AI services and logistics products

Products90+ ready-made solutionsParcel, courier, van-booking and freight bases with the price on each product page.AIAI developmentCustom models and AI features for products outside logistics too.AIMachine learningForecasting, ranking and prediction models trained on your records.AIComputer visionPhoto and scan checks such as proof-of-delivery review.VerticalLogistics app developmentThe courier, parcel and fleet platforms themselves, before any AI.AIAI integration servicesModels connected to software you already run, through its API.

Related Solutions

Explore Miracuves products & services

The logistics bases the AI layer is built on, and the AI services behind each module on this page.

FreightDAT Load Board Clone
ParcelFedEx Clone
Van & truckLalamove Clone
AIAI Agent Development
AILLM Development
AIChatbot Development
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Frequently Asked

AI in Logistics - FAQ

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What is AI in logistics?

It is the use of trained models and language models in the daily decisions of moving goods: assigning drivers, sequencing stops, predicting arrival times, matching loads to carriers, forecasting demand, checking proof-of-delivery photos, reading shipping documents and answering driver and customer questions. In a Miracuves build each is a separate module that reads the shipment record and writes a suggestion back, with a person able to overrule it.

Can you add AI to the logistics software I already use?

Yes, if it exposes its data. Miracuves connects models to an existing TMS, WMS, ERP or dispatch tool through its API, a read replica of its database or scheduled exports, and returns suggestions the same way or in a small console beside it. A system with no API needs an export first, scoped in writing. AI integration services covers that work across industries.

How is AI route optimization different from the routing in a ready-made base?

The base routing is what each product page describes: stop ordering and job assignment with live tracking. An AI route and stop sequencing module adds what your own history teaches: how long drops really take at each address, which roads slow down at which hour, delivery windows, vehicle capacity and driver shifts. A solver such as OR-Tools does the ordering with those learned times, and it re-runs when a job is added or a driver falls behind.

How accurate is AI ETA prediction?

It depends on your lanes and data, so Miracuves does not quote a figure before seeing them. The model is trained on completed trips, tested on weeks it never saw, and reported as the typical gap between predicted and actual arrival by zone, hour and vehicle. That test is agreed before switch-on, and customers see a window rather than a single minute when the model is less certain.

Can AI check proof-of-delivery photos?

Yes, as custom work. A vision model checks each photo as it is taken: is there a parcel, is it at a door or locker rather than on a road, is the image sharp, and do its time and place match the stop. Passing photos close the job; doubtful ones go to a review queue in the dispatch console. More on the computer vision development page.

Can AI read bills of lading, invoices and other shipping documents?

Yes. Scanned or emailed documents go through OCR and a language model that pulls shipper, consignee, weights, reference numbers and charges into the shipment record. Each field carries a confidence level, and low-confidence values go to a person before they reach billing. The DAT base already turns inbound freight emails into load drafts; other document types are scoped as custom work.

Do I own the AI code and the models?

Yes. Source code, prompts, training and evaluation scripts, and any model trained on your data are handed over in your own repositories and cloud accounts, under an NDA with IP assignment. Hosted models such as GPT, Claude or Gemini remain the provider's; you own how they are used and the data sent through your keys. Support runs 60 days from switch-on.

Which AI models and tools does Miracuves use for logistics?

The choice follows the job. Gradient-boosted models handle ETA and demand forecasts, a route solver handles stop order, vision models check photos, and language models such as GPT, Claude, Gemini or an open Llama model handle documents and assistants. Where data must stay on your servers, the open-model route is priced alongside. The LLM development page covers the language-model side.

Can AI forecast delivery demand and driver capacity?

Yes. A demand forecasting model for logistics predicts orders or loads per zone and hour from your own history, including weekends, paydays and holidays, and turns that into the number of drivers or vehicles to schedule. The console shows the forecast beside the actual count, so planners can judge it for a few weeks before shifts are planned from it.

Will AI replace my dispatchers?

It changes their work more than their number. Routine assignments and re-sequencing can run on their own once shadow mode shows the model agrees with your team, which leaves dispatchers with the exceptions: failed drops, unhappy customers, a van off the road. Every automated call stays visible and reversible, and their overrides become the training signal for the next version.

Get Started

Ready to add AI to your logistics platform?

Tell us the decision you want the AI to make and the data you hold. Miracuves scopes the module in writing, on a base from $2,499 or on the system you run, with full source code.

$2,499Bases from
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Page reviewed by Miracuves AI Team · Last updated September 2026 · Clutch & Google Reviews

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