Most “AI for business” tools in 2025 are wrappers around a few cloud LLM APIs. BrainPredict went in the opposite direction: 445 specialized ML models across 16 business domains (commerce, supply chain, finance, HR, ops, etc.), running 100% on your own infrastructure, with zero ChatGPT or external LLM dependency.
What it does
445 pre‑built models for things like churn, demand, pricing, inventory, fraud, contract risk, SLA breaches, cash‑flow, maintenance, etc., organized into 16 platforms (Commerce, Supply, People, Sales, Marketing, Legal, Risk, Finance, Innovation, Controlling, Communications, Data, Strategy, Sourcing, Operations, Customer).
Everything runs on‑prem: training, inference, and logging stay inside your infra (Linux/Windows servers, VMs or private cloud); the vendor’s servers only handle license checks, docs and updates, not data.
Models are “classic” ML/DL (XGBoost, RandomForest, Prophet, ARIMA, PyTorch, TensorFlow, BERT, spaCy) tuned for specific KPIs; field tests show ~92–95% accuracy and lower false positives vs single general models.
An “Intelligence Bus” coordinates everything: >570 event types let models share signals across platforms (e.g., a demand spike prediction from Commerce can trigger Supply, Finance and Operations decisions automatically).
Optional federated learning: customers can opt‑in to share encrypted model weights (never raw data) with differential privacy; aggregated weights are redistributed, so everyone benefits from better models without sharing data.
What’s intentionally not included
No calls to OpenAI, Anthropic, Gemini, etc.
No need to upload data to a vendor cloud or “AI API gateway”.
No chat front‑end pretending to be a decision system.
The whole stack is designed to keep regulated enterprises comfortable: GDPR native, EU AI Act‑oriented, zero‑knowledge architecture, and the option to run in offline or air‑gapped environments.
Why this might be interesting to YOU:
If you work in a large company, you’ve probably seen AI pilots die because security/compliance blocked sending data to LLM vendors. This is built specifically to avoid that conversation entirely.
Architecturally, the Intelligence Bus is a bet that many small, specialized models, orchestrated with explicit events, beat “one big model with prompts” for structured business decisions – especially when you need explainability and stable behavior.
It’s also an experiment in “old school” ML at scale in an LLM‑obsessed moment: the platform leans heavily on structured data, time series and tabular ML rather than generative text.
brainpredict•1mo ago
What it does
445 pre‑built models for things like churn, demand, pricing, inventory, fraud, contract risk, SLA breaches, cash‑flow, maintenance, etc., organized into 16 platforms (Commerce, Supply, People, Sales, Marketing, Legal, Risk, Finance, Innovation, Controlling, Communications, Data, Strategy, Sourcing, Operations, Customer).
Everything runs on‑prem: training, inference, and logging stay inside your infra (Linux/Windows servers, VMs or private cloud); the vendor’s servers only handle license checks, docs and updates, not data.
Models are “classic” ML/DL (XGBoost, RandomForest, Prophet, ARIMA, PyTorch, TensorFlow, BERT, spaCy) tuned for specific KPIs; field tests show ~92–95% accuracy and lower false positives vs single general models.
An “Intelligence Bus” coordinates everything: >570 event types let models share signals across platforms (e.g., a demand spike prediction from Commerce can trigger Supply, Finance and Operations decisions automatically).
Optional federated learning: customers can opt‑in to share encrypted model weights (never raw data) with differential privacy; aggregated weights are redistributed, so everyone benefits from better models without sharing data.
What’s intentionally not included
No calls to OpenAI, Anthropic, Gemini, etc.
No need to upload data to a vendor cloud or “AI API gateway”.
No chat front‑end pretending to be a decision system. The whole stack is designed to keep regulated enterprises comfortable: GDPR native, EU AI Act‑oriented, zero‑knowledge architecture, and the option to run in offline or air‑gapped environments.
Why this might be interesting to YOU:
If you work in a large company, you’ve probably seen AI pilots die because security/compliance blocked sending data to LLM vendors. This is built specifically to avoid that conversation entirely.
Architecturally, the Intelligence Bus is a bet that many small, specialized models, orchestrated with explicit events, beat “one big model with prompts” for structured business decisions – especially when you need explainability and stable behavior.
It’s also an experiment in “old school” ML at scale in an LLM‑obsessed moment: the platform leans heavily on structured data, time series and tabular ML rather than generative text.
Live site: https://brainpredict.ai
Would love feedback from people who:
Have tried (and struggled) to deploy AI behind strict firewalls and DPAs
Believe in (or are skeptical of) many‑models‑plus‑bus vs “just use GPT‑4 for everything”
Have war stories about getting real predictive systems into production in enterprise settings