levity.ai

Levity targets high-volume, email-driven logistics communication — the kind of work where thousands of repetitive requests flow through shared inboxes every day. Based on its own product materials, the platform is built around a set of named email categories: Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, and General Inquiry, with shipment-specific scenarios including Shipment Status, ETA Updates, POD Requests, and Exceptions. If your operation handles these request types at scale, Levity is designed for that shape of work. If your workflows are low-volume or not email-centric, the fit is weaker.

The workflow categories Levity names

Levity's dashboard groups incoming email into categories, which gives a concrete picture of what it expects to process:

A second layer of categories covers shipment-level events: Shipment Status, ETA Updates, POD Requests, and Exceptions. These are the recurring touchpoints where a customer, carrier, or partner needs a status answer or a document.

Why these categories are the automation target

The common thread is repetition with variation. A Track and Trace email and an ETA update request look similar but differ in shipment reference, language, and urgency. Levity's stated approach is to find "repetitive requests, handoffs, and bottlenecks at scale" — meaning it first measures which categories dominate your inbox, then turns the highest-impact ones into automations.

The scale in Levity's own example is large: a dashboard showing 8,240,312 emails processed in 30 days, with daily incoming volume charted in the hundreds of thousands. That's the regime the product is built for — global supply chain operations where manual triage doesn't scale.

How the workflow actually runs

Levity describes three stages, and each maps to a different kind of work:

1. Analyze — Connect inboxes and let the system categorize incoming email. The Control Tower dashboard shows volume by category, internal vs. external split, and trends over time. You can also "chat with your data" in natural language to explore operational patterns.
2. Build — Turn the high-impact categories into production automations. This is where a Track and Trace or POD Request workflow becomes something the system handles rather than a person.
3. Scale — Govern and improve automations through visibility, human oversight, and continuous feedback. The point is that humans stay in the loop for exceptions and quality control.

Where Levity fits — and where it doesn't

Levity is a reasonable fit when:

• Your logistics communication runs primarily through email at high volume
• Requests fall into recurring categories rather than one-off projects
• You have shared inboxes with handoffs between teams or regions
• You need visibility into what's actually arriving before automating it

It's a weaker fit when:

• Volume is low enough that manual handling isn't a bottleneck
• Workflows aren't email-driven (e.g., pure API or EDI integration without a communication layer)
• Requests are highly bespoke with little repetition

A practical way to decide

Before committing, the useful test is whether you can name your top three email categories and roughly how many arrive per day. Levity's own framing — "don't bolt AI onto chaos" — suggests the first value is measurement: seeing which categories dominate and where handoffs stall. If you can't yet describe your inbox in those terms, the Analyze stage is where you'd start. If you already know your Track and Trace or POD Request volume is in the thousands per day, that's the workflow to automate first.

Levity is a communication intelligence platform for logistics operations, and the site's own call to action is Schedule demo — that is the documented entry point for getting started. The page also offers Read case study links and Learn more links for its analytics features, so you can evaluate the platform before committing to a conversation. Everything below is drawn from what the site actually shows; pricing and sign-up terms are not stated on the page, so treat them as unknown until you ask.

What you can do before scheduling a demo

The homepage is structured around three stages — Analyze, Build, and Scale — and each stage has material you can review on your own.

1. Read the customer case studies

The page includes Read case study links, including one from TransAlpin Logistics Organisation. A quoted customer, Kevin Mangold Arias (Head of Corporate Digital Services), describes uncovering "clarity in areas we previously couldn't measure" and unlocking automation "where we never thought it was possible."

Use these to judge fit: if your operation resembles a global logistics organization with high email volume, the case material is the most concrete evidence available on the page.

2. Explore the Analyze capabilities

The Analyze section describes discovering automation potential "across millions of emails by spotting repetitive requests, handoffs, and bottlenecks at scale." Three Learn more links sit under it:

These are the features to ask about first if your goal is understanding where automation would pay off.

3. Understand what the Control Tower shows

The product screenshots on the page depict a Control Tower interface with a Main Overview dashboard. Visible elements include:

• Incoming Emails (Daily) — a line chart with a selectable window (1, 7, 14, 21, 28 days)
• Emails processed last 30 days — shown as 8,240,312 emails in the example
• Emails by Category — a donut chart breaking down Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, and General Inquiry
• Shipment Status — ETA Updates, POD Requests, Exceptions
• Internal vs. External Emails — a monthly stacked view
• Ask AI — a natural-language search box

If those categories match the email traffic your team handles, the dashboard is a reasonable preview of what you would be monitoring.

What happens after the demo

The Build stage is described as turning "high-impact workflows into production-grade AI," and the Scale stage covers governing and improving automations "through visibility, human oversight, and continuous feedback." The page also states Levity connects "seamlessly into your everyday tools," though it does not name specific integrations — ask for the integration list during the demo.

A useful way to prepare: bring a sample of your own inbox categories and volumes. The page's framing is "Don't bolt AI onto chaos. Rewire your operations," which signals that Levity positions itself as a redesign of the workflow rather than a tool layered on top of existing processes. Expect the conversation to focus on which workflows to rewire first.

Common sticking points

• No self-serve signup is shown. The only documented entry point is Schedule demo, so plan for a sales conversation rather than immediate hands-on access.
• Pricing is not published. The page contains no pricing signals, so cost and contract terms are open questions.
• Integration specifics are vague. "Connect Levity seamlessly into your everyday tools" is the extent of what the page says; verify your actual stack during evaluation.
• The metrics shown are illustrative. Figures like 8,240,312 emails are dashboard examples, not benchmarks for your operation.

Levity builds reliability by treating automation as a redesign of work rather than an add-on: it first finds high-impact workflows in real inbox data, then turns them into production-grade AI that runs with visibility, human oversight, and continuous feedback. This approach fits teams running high-volume, complex communication workflows — the site positions it for global supply chain and logistics operations — where errors are costly and volume makes manual review impractical. If your workflows are low-volume or purely internal, the governance layer described here is more than you need.

The reliability model: analyze, build, then govern

Levity's own framing is a three-stage loop — Analyze, Build, Scale — with governance running across all three rather than bolted on at the end. The site's headline makes the intent explicit: "Don't bolt AI onto chaos. Rewire your operations."

Analyze: find workflows worth automating

Before anything is automated, Levity surfaces where automation actually pays off. Per the site, Control Tower lets you "discover automation potential across millions of emails by spotting repetitive requests, handoffs, and bottlenecks at scale."

The concrete capabilities listed:

• Analyze your inboxes — "Track operational performance and automation impact in real time."
• Chat with your data — "Ask questions and explore operational data using natural language."
• Find automation opportunities — "Identify and prioritize high-impact workflows worth automating."

The dashboard evidence shows what this looks like in practice: incoming email volume tracked daily, emails processed over a rolling 30-day window (8,240,312 in the example shown), and a breakdown by category — Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, General Inquiry — plus a shipment-status split (ETA Updates, POD Requests, Exceptions) and an internal vs. external ratio.

Why this matters for reliability: you can only govern what you can measure. Categorizing and counting requests before automating establishes the baseline that later proves whether an automation is working or drifting.

Build: turn workflows into production-grade AI

The Build stage is described as: "Build and run automations reliably by turning high-impact workflows into production-grade AI." The emphasis on production-grade is the reliability claim — the output is meant to run in live operations, not as a pilot.

Scale: govern, improve, and extend

The Scale stage is where governance lives: "Govern, improve, and scale AI automations through visibility, human oversight, and continuous feedback." Three mechanisms are named, and each addresses a different failure mode:

Connecting into existing tools

Reliability also depends on where the automation runs. Levity states it will "connect seamlessly into your everyday tools," so automations operate inside the systems teams already use rather than in a separate console. The site does not list specific integrations in the available material, so treat the exact connector set as something to confirm during a demo.

What the evidence supports — and what to verify

Supported by the site:

• A three-stage model (Analyze, Build, Scale) with governance across it
• Inbox analytics, natural-language querying of operational data, and opportunity identification
• Categorization of logistics email types (Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, General Inquiry)
• Visibility, human oversight, and continuous feedback as the governance pillars
• Seamless connection into everyday tools (specific tools not enumerated)
• A stated focus on global supply chain / logistics communication

Not specified in the available material — ask directly:

• Pricing and any plan or login restrictions
• The full list of supported integrations
• Accuracy thresholds, SLAs, or how "production-grade" is measured
• Deployment model and data-handling terms

A practical way to evaluate it

If you are assessing Levity for a high-volume inbox, use the Analyze stage as your own test:

1. Bring a real inbox sample. The value of the analytics stage depends on your actual email mix, not a demo dataset.
2. Check the category breakdown against your own taxonomy. The example categories are logistics-specific; confirm they map to how your team actually routes work.
3. Ask how a wrong automation is caught. The governance answer should name the visibility surface, the human review step, and how feedback changes the automation.
4. Confirm the integration path for the tools your team already runs in.

The core idea worth taking away: reliability in this model comes from measuring before automating, keeping a human in the loop, and feeding outcomes back — not from the AI model alone.

Levity is an AI automation platform built for logistics and supply chain operations. Its core promise is stated plainly on its homepage: "Don't bolt AI onto chaos. Rewire your operations." Rather than adding AI as another tool on top of existing processes, Levity positions itself as Communication Intelligence for the global supply chain — a system for analyzing high-volume operational communication (primarily email), finding repetitive patterns, and turning those patterns into production-grade AI automations that humans and AI run together.

If your team handles large volumes of inbound operational email — bookings, tracking requests, quotes, claims, customs questions — Levity is designed for that scenario. If your workflows aren't email- or communication-heavy, the fit is less obvious.

The problem Levity addresses

Logistics operations generate enormous volumes of unstructured communication. The homepage dashboard shows a representative scale: 8,240,312 emails processed in 30 days, with categories including Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, and General Inquiry. Within those, subcategories like Shipment Status, ETA Updates, POD Requests, and Exceptions appear.

At that volume, manual triage breaks down. Requests get handed off repeatedly, bottlenecks form, and no one can measure where the work actually goes. Levity's framing is that the bottleneck isn't a lack of AI capability — it's that the underlying work was never redesigned to be shared between humans and AI.

How Levity works: three stages

Levity organizes its product around three stages, each with a distinct purpose.

Analyze — find automation potential

This stage surfaces what's actually happening across millions of emails. It includes:

• Analytics / Control Tower — dashboards monitoring general email data, including incoming email volume over time, emails by category, and internal vs. external email splits.
• Track your Inboxes — real-time visibility into operational performance and automation impact.
• Chat with your Data — ask questions and explore operational data using natural language.
• Find Automation Opportunities — identify and prioritize high-impact workflows worth automating.

The stated goal is to spot repetitive requests, handoffs, and bottlenecks at scale — including in areas the organization previously couldn't measure.

Build — turn workflows into production AI

Once opportunities are identified, Levity builds and runs automations by turning high-impact workflows into production-grade AI. The emphasis here is on reliability, not just prototyping.

Scale — govern and improve over time

The third stage covers governance: visibility into running automations, human oversight, and continuous feedback loops so automations improve rather than drift.

What makes Levity's approach distinctive

Two things stand out from the homepage positioning.

Redesign over replacement. Levity explicitly rejects treating AI "like another hire." The stated approach is to redesign the work so humans and AI can run it together. This matters for teams that have tried bolting a generic AI tool onto existing processes and found it created more chaos than it removed.

Integration into existing tools. Levity states it connects "seamlessly into your everyday tools," rather than requiring teams to work inside a separate system.

Evidence from a customer

The homepage includes a quote from Kevin Mangold Arias, Head of Corporate Digital Services, describing that Levity "helps us uncover clarity in areas we previously couldn't measure and unlock automation where we never thought it was possible," adding that the team "understands the complexity of a global organization."

That's a vendor-published testimonial, so treat it as directional rather than independent verification — but it aligns with the Analyze-stage value proposition: measurement first, automation second.

Who should consider Levity

Levity is likely a fit if:

• You operate in logistics, freight, or global supply chain and handle high volumes of operational email.
• You have repetitive request types (tracking, booking, quoting, claims, customs) that consume staff time.
• You need visibility into communication patterns before deciding what to automate.
• You want human-in-the-loop automation rather than full replacement.

It's likely a weaker fit if your operational communication is low-volume, already well-structured, or not email-centric.

Getting started

The homepage offers a Schedule demo entry point rather than self-serve signup, which suggests an implementation-led onboarding. Pricing is not published on the page, so cost and access terms would need to be confirmed directly with Levity.

Levity identifies automation opportunities by analyzing your email inboxes at scale, then surfacing the repetitive requests, handoffs, and bottlenecks worth automating. Its Control Tower and Analytics dashboards turn millions of emails into measurable categories and trends, and an Ask AI feature lets you query that operational data in natural language. This is aimed at teams running high-volume, complex logistics communication — for example, track-and-trace, booking, pricing, and customs requests — where manual triage hides where automation would pay off.

The problem it targets

In logistics operations, automation potential is usually invisible. Requests arrive as unstructured emails, get forwarded between people, and stall at handoffs. Levity's stated approach is to not "bolt AI onto chaos" but to first measure the work, then redesign it so humans and AI run it together. The identification step is what makes that possible: you can't prioritize a workflow you can't see.

How the discovery process works

1. Analyze inboxes at scale

Levity's Analytics layer is described as discovering automation potential "across millions of emails by spotting repetitive requests, handoffs, and bottlenecks at scale." The dashboard shown in the source material monitors general email data, including:

• Incoming emails (daily) — a line chart tracking volume over time
• Emails processed last 30 days — a headline count (the example shows 8,240,312 emails)
• Emails by category — a donut chart breaking volume into types such as Track and Trace, Booking, Pricing & Quoting, Damage Claims, Customs, and General Inquiry
• Shipment status sub-types — ETA Updates, POD Requests, and Exceptions
• Internal vs. external emails — a monthly breakdown showing the share of internal versus external communication

This categorization is the core mechanism: once email volume is grouped by request type, you can see which categories are large, repetitive, and therefore candidates for automation.

2. Track operational performance in real time

The Control Tower view is described as tracking operational performance and automation impact in real time. This matters for prioritization because it shows not just what's coming in, but how the operation is responding — so you can compare categories and spot where volume and manual effort are both high.

3. Ask questions in natural language

Ask AI (also labeled "Chat with your Data") lets you ask questions and explore operational data using natural language. Instead of building a report, you can query the same email data directly — useful for testing a hypothesis like whether a specific request type is growing or concentrated in one region.

4. Find and prioritize opportunities

Levity's Find Automation Opportunities capability is described as identifying and prioritizing high-impact workflows worth automating. This is the output of the discovery process: a ranked shortlist rather than a raw data dump.

From discovery to running automation

Discovery is the first of three stages in Levity's model:

The Analyze stage is what answers "where should we automate?" — and it feeds directly into Build, so the workflows you choose are grounded in measured email volume rather than guesswork.

What this means in practice

If you're evaluating whether Levity fits, the relevant question is whether your automation bottleneck is knowing where to start. Levity's discovery layer is designed for teams with large email volumes and many request categories, where the repetitive work is real but not obvious from the inbox alone. The example dashboard in the source material — 8.2M emails categorized across track-and-trace, booking, pricing, customs, and claims — illustrates the scale it's built for.

For a concrete test: take one high-volume category (for example, Track and Trace or POD Requests), check how it splits between internal and external senders, and ask whether the repetitive portion could be handled by an automation. Levity's Analytics and Ask AI are the tools for answering that before you commit to building.

Note that the source material describes capabilities and shows dashboard examples but does not publish pricing or plan details, so cost and access terms would need to be confirmed directly with Levity.