Practical AI,
built quietly.
KG Intelligence builds machine-learning products for operations teams. Our first one — MLmargin — tackles demand forecasting and replenishment for retail and ecommerce.
We craft AI products that earn their keep.
We start from the operational problem, not the model. Our work is driven by two goals: a transparent user experience that respects the team's time, and reliable AI recommendations the team can act on without second-guessing the output.
MLmargin.
Demand forecasting, replenishment recommendations, and price optimization in one tool — built for teams that want the leverage of ML without hiring a data science group to operate it.
Four principles.
Many applications, one stack.
Our framework is designed for fast adaptation across retail, energy, cybersecurity, finance, insurance, pharma, IoT, manufacturing, and ecology — wherever operations data is dense enough for ML to help.
In-house, end to end.
We don't depend on third-party ML platforms. Our software is built in-house, which keeps us nimble and lets us adapt quickly when a problem doesn't fit the textbook.
Scalable by default.
Cloud-hosted and built to handle both small datasets and large data volumes. Decisions land on the spot — not in a queue.
Affordable for SMEs.
Quick implementation and onboarding. Built for teams that want the leverage of ML without standing up a data science org to get there.
Probability theory meets neural nets.
Reinforcement learning
Our own RL framework, applied to complex optimization problems where the right answer is the one that wins over many small choices.
Transfer learning
Continuous model evolution and versioning — so the system gets sharper as your data accumulates, instead of stale.
Graph theory & SIMD
CPU-parallel algorithms (Locality Sensitive Hashing, Bloom Trees) tuned for the access patterns operations data actually shows up with.
Have a problem worth applying ML to?
Tell us about it. We're a small team — emails reach a person, not a queue.