Real problems. Shipped answers.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

92% classification accuracy.

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

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

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

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

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

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

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

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

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

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

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

Currently running in production for 6 clients.

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.

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

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

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

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

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

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?

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

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

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

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

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