<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M14.8314 4.68555V6.01888H1.16861V4.68555H14.8314Z" fill="currentColor"/> <path d="M7.85546 2.11296L5.8509 5.4515L8.28515 14.4932L6.99804 14.8395L4.41666 5.25228L6.71158 1.42676L7.85546 2.11296Z" fill="currentColor"/> <path d="M11.5835 5.25228L11.5093 5.52572L9.00215 14.8395L7.71439 14.4932L10.148 5.4515L8.14472 2.11296L9.28795 1.42676L11.5835 5.25228Z" fill="currentColor"/> <path d="M11.8776 1.10352L15.6523 5.26562L9.81706 15.3333H6.18294L0.347656 5.26562L4.1224 1.10352H11.8776ZM1.98958 5.43945L6.95117 14H9.04883L14.0104 5.4388L11.2871 2.43685H4.71289L1.98958 5.43945Z" fill="currentColor"/> </svg>
Asset management
Built for precision. Structured for scale. And ready for what’s next.
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Banking
Compliance. Risk. Customer insight. Invisible helps make sense of it all.
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Consumer
Forecast, adapt, and operate with confidence.
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Energy
Catch hazards, cut losses, prevent downtime.
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Healthcare
Custom AI for healthcare operations.
<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_3079_2147)"> <path d="M15.4025 8.5V14.1283L12.0001 15.9199L8.5978 14.1283V8.5H15.4025ZM9.93113 13.3236L11.9995 14.4128L14.0692 13.3229V9.83333H9.93113V13.3236Z" fill="currentColor"/> <path d="M15.1126 6.40462L14.2207 7.39551L8 1.79655L2 7.19629V14.0003H7.33333V15.3337H0.666668V6.60319L8 0.00292969L15.1126 6.40462Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_3079_2147"> <rect width="16" height="16" fill="white"/> </clipPath> </defs> </svg>
Insurance
Move faster, without cutting corners.
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Life sciences
Speed up trials, submissions, and campaigns without the compliance risk.
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Private equity
Transform portfolio operations, fund controls, and data infrastructure.
<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_3079_2306)"> <path d="M6 8.66699V14.667H4.66667V8.66699H6Z" fill="currentColor"/> <path d="M11.3333 8.66699V14.667H10V8.66699H11.3333Z" fill="currentColor"/> <path d="M8.66667 12V14.6667H7.33333V12H8.66667Z" fill="currentColor"/> <path d="M13.3333 5.9694V9.33333H2.66667V5.9694L8 2.54102L13.3333 5.9694ZM4 6.69661V8H12V6.69661L8 4.12565L4 6.69661Z" fill="currentColor"/> <path d="M10 0.666992V2.00033H8.66667V3.33366H7.33333V0.666992H10Z" fill="currentColor"/> <path d="M14.6667 8V15.3333H1.33334V8H14.6667ZM2.66667 14H13.3333V9.33333H2.66667V14Z" fill="currentColor"/> <path d="M15.3333 8V9.33333H0.666668V8H15.3333Z" fill="currentColor"/> <path d="M15.3333 14V15.3333H0.666668V14H15.3333Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_3079_2306"> <rect width="16" height="16" fill="white"/> </clipPath> </defs> </svg>
Public sector
Cut red tape. Connect systems. Serve people better.
<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_3079_2330)"> <path d="M13 2.33333C13 1.78105 12.5523 1.33333 12 1.33333C11.4477 1.33333 11 1.78105 11 2.33333C11 2.88561 11.4477 3.33333 12 3.33333C12.5523 3.33333 13 2.88561 13 2.33333ZM14.3333 2.33333C14.3333 3.622 13.2887 4.66667 12 4.66667C10.7113 4.66667 9.66667 3.622 9.66667 2.33333C9.66667 1.04467 10.7113 0 12 0C13.2887 0 14.3333 1.04467 14.3333 2.33333Z" fill="currentColor"/> <path d="M10.069 4.43132L12.4023 7.43132L15.2109 8.36751L14.7891 9.63184L11.5977 8.56803L9.26367 5.56738L6.73373 4.72428L3.99024 6.24902L3.3431 5.08366L6.3431 3.41699L6.59896 3.27441L10.069 4.43132Z" fill="currentColor"/> <path d="M10.1785 5.42741L7.67201 8.43522C7.44182 8.71171 7.47276 9.12133 7.74168 9.36035L9.92983 11.3057L10.98 15.5055L9.68634 15.8285L8.73582 12.0277L6.85561 10.3564C6.0488 9.63919 5.95686 8.41117 6.64793 7.58171L9.15444 4.57324L10.1785 5.42741Z" fill="currentColor"/> <path d="M5.52083 10.7503L4.1875 12.417L3.98698 12.667H0.666668V11.3337H3.3457L4.47917 9.91699L5.52083 10.7503Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_3079_2330"> <rect width="16" height="16" fill="white"/> </clipPath> </defs> </svg>
Sports
AI is changing how teams train, win, and engage fans.
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Back office automation
From days of manual work to minutes of execution
<svg width="16" height="16" viewBox="0 0 22 22" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M11.0016 5C7.25081 5 5.88688 7.66667 3.5 11C5.88688 14.3333 7.25081 17 11.0016 17C14.7524 17 16.1164 14.3333 18.5033 11C16.1164 7.66667 14.7524 5 11.0016 5Z" stroke="currentcolor" stroke-width="2"/> <path d="M11 10C11.5523 10 12 10.4477 12 11C12 11.5523 11.5523 12 11 12C10.4477 12 10 11.5523 10 11C10 10.4477 10.4477 10 11 10Z" stroke="currentcolor" stroke-width="2"/> <path d="M7 21H1V15" stroke="currentcolor" stroke-width="2"/> <path d="M21 15V21H15" stroke="currentcolor" stroke-width="2"/> <path d="M1 7V1H7" stroke="currentcolor" stroke-width="2"/> <path d="M15 1H21V7" stroke="currentcolor" stroke-width="2"/> </svg>
Computer vision
Turn motion into data for advanced insights.
<svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M1 9H5V16H1V9Z" stroke="currentcolor" stroke-width="2"/> <path d="M19 9H23V16H19V9Z" stroke="currentcolor" stroke-width="2"/> <path d="M5 16V8C5 4.13401 8.13401 1 12 1C15.866 1 19 4.13401 19 8V16C19 19.0376 16.5376 21.5 13.5 21.5H12" stroke="currentcolor" stroke-width="2"/> <path d="M9.5 11L9.50002 8" stroke="currentcolor" stroke-width="2"/> <path d="M14.5 11V8" stroke="currentcolor" stroke-width="2"/> <path d="M8 13.7446C9.16574 14.5563 10.56 15.0001 12 15.0001C13.4401 15.0001 14.8343 14.5563 16.0001 13.7446" stroke="currentcolor" stroke-width="2"/> <path d="M12 24V19" stroke="currentcolor" stroke-width="2"/> </svg>
Contact center
Built for your contact center. Better with every interaction.
<svg width="16" height="16" viewBox="0 0 24 20" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M0 9.625H4L8 2.125L16 17.125L20 9.625H24" stroke="currentcolor" stroke-width="2"/> </svg>
Demand forecasting
Unified data. Reliable forecasts.
What's your untapped advantage?
Explore custom solutions
<svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M14.6824 1H9.31703V3.57365C8.18119 3.94698 7.14999 4.55093 6.27882 5.33012L4.04793 4.04211L1.36523 8.68867L3.59464 9.97577C3.47696 10.5424 3.41511 11.1294 3.41511 11.7308C3.41511 12.3322 3.47696 12.9192 3.59464 13.4858L1.36524 14.7729L4.04793 19.4195L6.27883 18.1315C7.14999 18.9106 8.18119 19.5146 9.31703 19.8879V22.4616H14.6824V19.8879C15.8182 19.5146 16.8494 18.9106 17.7206 18.1315L19.9515 19.4195L22.6342 14.7729L20.4048 13.4858C20.5225 12.9192 20.5843 12.3322 20.5843 11.7308C20.5843 11.1294 20.5225 10.5424 20.4048 9.97577L22.6342 8.68867L19.9515 4.04211L17.7206 5.33012C16.8494 4.55093 15.8182 3.94698 14.6824 3.57365V1Z" stroke="currentcolor" stroke-width="2"/> <path d="M6.5 11.7305C9.55556 11.7305 12 9.28603 12 6.23047C12 9.28603 14.4444 11.7305 17.5 11.7305C14.4444 11.7305 12 14.1749 12 17.2305C12 14.1749 9.55556 11.7305 6.5 11.7305Z" stroke="currentcolor" stroke-width="2"/> </svg>
AI training overview
Research-grade annotation, training, and evaluation.
<svg width="20" height="20" viewBox="0 0 64 64" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M18.561 39.3328V22.2207H40.5499V39.3328H18.561Z" stroke="currentcolor" stroke-width="5.33"/><path d="M29.5618 16.1094V22.2208" stroke="currentcolor" stroke-width="5.33"/><path d="M25.895 28.332V33.2212" stroke="currentcolor" stroke-width="5.33"/><path d="M33.2288 28.332V33.2212" stroke="currentcolor" stroke-width="5.33"/><path d="M2.66504 7.55273V19.7757H13.6657" stroke="currentcolor" stroke-width="5.33"/><path d="M56.4459 51.5549V39.332H45.4453" stroke="currentcolor" stroke-width="5.33"/><path d="M56.446 29.5545C56.446 14.7033 44.4067 2.66406 29.5556 2.66406C18.1541 2.66406 8.40997 9.75981 4.49829 19.7761" stroke="currentcolor" stroke-width="5.33"/><path d="M2.66504 29.5547C2.66504 44.4058 14.7043 56.4451 29.5555 56.4451C40.957 56.4451 50.7011 49.3495 54.6127 39.333" stroke="currentcolor" stroke-width="5.33"/></svg>‍
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Where the real world meets reinforcement learning.
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Demand forecasting

Unified data. Reliable forecasts.

Forecasting for businesses that don’t fit a template.
Request a demo
A screenshot of Invisible's platform demonstrating data ingestion and unification, processing POS, e-Commerce, ERP/Inventory, Labor, and Promotions.

Designed around your data, complexity, and operational reality.

A trusted, custom view

Designed to reflect how your business actually operates, even when signals and data aren’t uniform.

Accuracy that improves over time

Forecasts adapt as conditions change, increasing confidence as the system learns from real outcomes.

Insights built for action

Move beyond projections to guidance that informs real operational decisions.

Accurate forecasts.
Better decisions everywhere.

Most forecasting tools produce generic predictions. Invisible delivers actionable forecasts shaped around your business.

Data cleaning and unification
Custom forecast models
Decision-ready insight dashboard

Unify ERP, POS, e-comm, labor, ops, and external signals into a single demand foundation.

A screenshot of Invisible's platform showing data ingestion and unification across POS, e-Commerce, ERP, Labor, and Promotions.
GTN Executive Summary
Gross-to-net performance and deduction bridge for the latest monthly close.
Type
Month
Current
May 2026
Compare
Apr 2026
GTN Rate
48.6%
↗ +6.6 pp vs Apr 2026
Gross Sales
$55,710,911
↗ +40.7% vs Apr 2026
Total Deductions
$28,657,986
↗ +24.7% vs Apr 2026
Net Revenue
$27,052,925
↗ +63.0% vs Apr 2026
Gross-to-Net Waterfall (May 2026 Close)
% OF GROSS WAC
100.0%
−20.2%
−7.0%
−6.1%
−4.0%
−14.1%
48.6%
Gross WAC
Managed Care Commercial
Coverage Gap
Wholesaler Fees
Patient Assistance
Other Deductions
Net Sales
Gross WAC
Deductions
Net sales

Select and train forecasting models that best align with your data and business model.

A screenshot of Invisible's platform showing evaluation of a forecast model, displaying outcomes and accuracy.
GTN Executive Summary
Gross-to-net performance and deduction bridge for the latest monthly close.
Type
Month
Current
May 2026
Compare
Apr 2026
GTN Rate
48.6%
↗ +6.6 pp vs Apr 2026
Gross Sales
$55,710,911
↗ +40.7% vs Apr 2026
Total Deductions
$28,657,986
↗ +24.7% vs Apr 2026
Net Revenue
$27,052,925
↗ +63.0% vs Apr 2026
Gross-to-Net Waterfall (May 2026 Close)
% OF GROSS WAC
100.0%
−20.2%
−7.0%
−6.1%
−4.0%
−14.1%
48.6%
Gross WAC
Managed Care Commercial
Coverage Gap
Wholesaler Fees
Patient Assistance
Other Deductions
Net Sales
Gross WAC
Deductions
Net sales

Deliver forecasts, recommendations, and performance visibility through flexible dashboards and planning views.

A screenshot of Invisible's platform showing demand insights, forecasts, recommendations, and performance visibility across stores.
GTN Executive Summary
Gross-to-net performance and deduction bridge for the latest monthly close.
Type
Month
Current
May 2026
Compare
Apr 2026
GTN Rate
48.6%
↗ +6.6 pp vs Apr 2026
Gross Sales
$55,710,911
↗ +40.7% vs Apr 2026
Total Deductions
$28,657,986
↗ +24.7% vs Apr 2026
Net Revenue
$27,052,925
↗ +63.0% vs Apr 2026
Gross-to-Net Waterfall (May 2026 Close)
% OF GROSS WAC
100.0%
−20.2%
−7.0%
−6.1%
−4.0%
−14.1%
48.6%
Gross WAC
Managed Care Commercial
Coverage Gap
Wholesaler Fees
Patient Assistance
Other Deductions
Net Sales
Gross WAC
Deductions
Net sales
Data cleaning and unification

Unify ERP, POS, e-comm, labor, ops, and external signals into a single demand foundation.

Custom forecast models

Select and train forecasting models that best align with your data and business model.

Decision-ready insight dashboard

Deliver forecasts, recommendations, and performance visibility through flexible dashboards and planning views.

case study

“”

FAQ

Do we need to replace our entire stack to start with AI demand forecasting?

No. Many organizations begin by unifying data from existing systems, like ERP, POS, and e commerce, into a common model, comparing AI demand forecasts against current forecasts on a few categories, and expanding once they see accuracy and operational impact.
How can AI help us manage forecasting across thousands of SKUs, stores, or customers?
AI can automatically model demand at a very granular level (for example SKU–store–day) and group similar patterns so each segment gets an appropriate forecasting approach. This lets you maintain accurate, scalable forecasts across thousands of SKUs, stores, or customers without manually tuning models for every single item.
How fast can AI demand forecasting react to sudden demand shifts or new product launches?
AI demand forecasting can update much more frequently than traditional planning, because it continuously ingests new sales, channel, and external signals. That means it can detect emerging patterns (a spike after a promo, a slow start on a new SKU, a regional surge) in days or even hours, and refresh forecasts so planners can adjust orders, production, and allocation before issues show up as stockouts or excess.
How can AI help improve inventory planning and reduce stockouts?
AI demand forecasting models detect patterns in sales, seasonality, and promotions at SKU/location level, so you can set smarter reorder points and safety stock instead of relying on averages. That tighter alignment between inventory and real demand reduces stockouts on fast movers while cutting overstock and markdowns on slower items.
How can I use AI to improve labor scheduling and staffing decisions across locations?
AI demand forecasting can translate predicted demand by store, channel, and time of day into expected workload, giving workforce planners a clearer view of when and where they’ll need people. You can feed those demand curves into scheduling tools to set smarter staffing levels, reduce overstaffing during slow periods, and prevent understaffing during peaks—improving service levels without blindly adding labor.

Book a demo

Take us through your history. We’ll show you where your forecast breaks, and how to fix it.
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A screenshot of Invisible's platform demonstrating demand insights and forecasts vs actuals with AI summary insights.
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