Use Cases - 12Dimension AI Labs

+8% over the client's existing baseline

01

Predicting Grape Production Mid-Season

Israeli agtech startup

PROBLEM No reliable way to estimate total field yield before harvest finished — making logistics, labor and buyer commitments a guessing game.

SOLUTION A spatio-temporal deep learning model combining historical yield data with in-season field measurements to forecast total production before the harvest window closes.

OUTCOME 8% average improvement over the client's existing baseline algorithm.

75.8% accuracy — beating the internal benchmark

02

Trash Email User Detection

Google

PROBLEM Junk-only email addresses were polluting user data and skewing engagement metrics — and they're nearly invisible from account data alone.

SOLUTION A behavioral-similarity model over each user's connection network — comparing how they behave relative to similar users rather than in isolation.

OUTCOME 75.8% accuracy — outperforming the client's internal data science team's own benchmark.

0 false positives at 67% accuracy — tunable to 85%

03

Predicting Necessity of Repeat Blood Tests

Clalit · Pediatric care

PROBLEM Children were re-tested in hospital even when a recent community test was still valid — adding cost, delay and unnecessary discomfort.

SOLUTION A low-data ML model — built to work despite limited pediatric-specific data — that predicts whether a repeat test is actually needed.

OUTCOME 67% accuracy with zero false positives; up to 85% when some false positives are tolerated — a tunable precision/recall tradeoff for clinical risk appetite.

78% accuracy with explanations — 4% past state-of-the-art

04

Emergency Room Recommendation System

Rambam Health Care Campus

PROBLEM ER staff had to route each patient into the correct one of 17 treatment categories — a high-stakes call made under time pressure.

SOLUTION A deep learning recommendation system with a pre-trained LLM layer for explainability — it doesn't just recommend, it explains its reasoning.

OUTCOME 78% accuracy with explanations — 4% better than the prior state-of-the-art.

Scenario stress-tests across critical supply chains

05

Pandemic Supply Chain Resilience Simulation

European crisis-response institute

PROBLEM Crisis planners needed to know how pandemic disruptions — lockdowns, labor shortages, transport bottlenecks — propagate through critical supply chains.

SOLUTION An agent-based simulation combined with a pandemic-spread model, used to stress-test supply chain scenarios and rank resilience investments.

OUTCOME A full resilience findings document with scenario results — showing where investment matters most before the next crisis.

92% accuracy from movement, not just appearance

06

Fruit Fly Larvae Classification

Israel Ministry of Agriculture

PROBLEM Manual visual sorting of healthy vs. unhealthy larvae was slow and inconsistent across inspectors.

SOLUTION Biological movement profiling via computer vision plus time-series classification — using how the larvae move, not just how they look.

OUTCOME 92% classification accuracy.

3 peer-reviewed publications — algorithms in active use

07

Social Rankings in Captive Chimpanzees

Los Angeles Zoo

PROBLEM Zoo staff needed an objective, non-invasive way to understand troop hierarchy and shifting relationships — with real welfare implications.

SOLUTION Deep learning computer vision over hours of video footage, combined with classical ML for classification and regression on the interaction data.

OUTCOME Several algorithms in active use by the zoo's care team — published in three peer-reviewed venues.

SOTA detection performance on coordinated attacks

08

Multi-User Attack Detection

UK fraud-detection company

PROBLEM Detecting coordinated attacks on financial networks — including attacks that don't fit any single known pattern.

SOLUTION Deep reinforcement learning with agent-based simulation for in-silico training, plus bio-inspired real-time optimization.

OUTCOME State-of-the-art detection performance across a wide range of coordinated attack types.

Live run-detection at emergence, not after the fact

09

Temporal Graph Anomaly Emergence Detection

Algotrading · later acquired

PROBLEM Detecting a “run” in an asset right as it begins — not after the fact, when the opportunity is gone.

SOLUTION Confidential per client agreement — the approach draws on our published time-series and graph-theory anomaly-detection research.

OUTCOME Supported live algotrading decision-making; the project was subsequently acquired.

6 clients running it in production today

10

Inner-Organization Chatbots

Multiple clients · in production

PROBLEM Off-the-shelf chatbot products weren't accurate enough — clients needed answers grounded in their own business data, not generic responses.

SOLUTION A custom LLM + RAG chatbot architecture, connected to each client's own data and tuned for grounded, accurate answers.

OUTCOME Currently running in production for 6 clients.

20+ analyses across 8+ verticals in 12 months

11

Market & Competitor Research — Multi-Vertical

Real estate, quantum, blockchain, energy, IoT, edtech, finance +

PROBLEM Founders across very different industries needed the same thing before committing budget: an evidence-based read on their market and where the real opening is.

SOLUTION Our CLEAR Discovery methodology adapted per vertical — sizing the market, mapping competitors, and identifying positioning gaps specific to each domain.

OUTCOME 20+ market and competitor analyses delivered in the last 12 months across 8+ distinct verticals.

~70% less manual invoice entry, end to end

12

Smart AI CRM & Backoffice

International shipping company

PROBLEM Shipment, warehouse and accounting ran on disconnected manual processes — invoices typed by hand, no real-time shipment visibility.

SOLUTION An integrated AI CRM and backoffice: AI-assisted accounting, real-time shipment and warehouse tracking, OCR invoice processing, and delivery-coordination automation.

OUTCOME Invoice processing automated end-to-end — manual entry down roughly 70%, with live shipment status replacing follow-up calls.

3 critical issues caught and fixed pre-launch

13

AI Product Audit — Hotel Tech Chatbot

Hotel technology startup

PROBLEM Before scaling, the startup needed an independent answer: is the chatbot secure, does it survive booking-season load, and does it work across guest languages?

SOLUTION A full audit — load and scale testing, multi-language testing, and a security review of the chatbot and its PMS/booking-engine integrations.

OUTCOME 11 issues surfaced — 3 critical — all resolved before launch, with a prioritized action plan for the rest.

60%+ of calls resolved with no human escalation

14

AI Voice Agent — Fintech Support

Fintech company · 3 languages

PROBLEM The support team needed to handle account verification and technical support across multiple languages — without scaling headcount with call volume.

SOLUTION A trilingual AI voice agent trained on the company's own data, connected to their CRM for account verification and historical context.

OUTCOME Over 60% of customer-service and technical-support calls resolved end-to-end with no human escalation, across all three languages.

Real problems. Shipped answers.