Core v3.2//Systems Active

Build. Train. Deploy.Machine Learning.

End-to-end ML platform with first-class Mongolian language support. From pre-trained models to production pipelines — cloud or on-premise.

SYS.01Platform Capabilities

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Train, deploy, and monitor models at scale. Cloud or on-premise — your choice.

Custom ML Models

Vision, NLP, predictions — trained on your data, built for your workflows.

Real-Time Inference

Sub-100ms predictions. Fast enough for the things that can't wait.

Enterprise Security

SOC 2 compliant. Encrypted, access-controlled, and fully auditable.

Observability

One dashboard for performance, drift detection, and usage analytics.

Cloud & On-Premise

Your cloud, your servers, or ours. VPC, air-gapped, hybrid — you pick.

Data Sovereignty

Your data stays yours. GDPR, HIPAA, and local regulations — covered.

DEV.02SDK Interface

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Pre-trained models, pipeline builder, and deployment tools — with native Mongolian language support.

RAPTORA SDK
from raptora import Pipeline, models

# Load a pre-trained Mongolian NLP model
nlp = models.load("mn-ner-v3")

# Run named entity recognition
result = nlp.predict("Улаанбаатар хотод ...")
print(result.entities)

# Build an end-to-end pipeline
pipe = Pipeline([
    models.load("mn-tokenizer"),
    models.load("mn-ner-v3"),
    models.load("mn-sentiment"),
])

# Deploy to cloud or on-premise
pipe.deploy(target="cloud", region="ap-east-1")
Python 3.11+|pipeline.py
READY

Mongolian Language Support

First-class Mongolian NLP — tokenization, NER, sentiment analysis, and text classification built for Cyrillic Mongolian.

Simple API

Three lines of code to load a model, run inference, and get results. Python, Node.js, and REST.

Pre-trained Models

Ready-to-use models for common tasks — OCR, speech-to-text, translation, and classification.

Pipeline Builder

Chain models into production pipelines. Data in, predictions out. No glue code.

OPS.03Field Deployments

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Real models solving real problems. These are just examples — we build custom AI for any industry.

99.2%

Accuracy

License Plate Recognition

Automated vehicle ID for parking, tolls, and law enforcement. Reads plates faster than you can squint.

ALPRComputer VisionReal-time
87%

Fraud Caught

Insurance Fraud Detection

Spots suspicious claims before they become expensive mistakes.

Claims AnalysisRisk ScoringAnomaly Detection
0.3mm

Precision

Manufacturing QC

Visual inspection that catches defects your best human inspectors would miss.

Defect DetectionVisual InspectionIoT
98.4%

Field Accuracy

Document Intelligence

Extracts data from invoices, contracts, and forms — no more manual entry headaches.

OCRData ExtractionClassification
SEQ.04Deployment Protocol

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Four steps. No fluff. Just results.

Step01

Discover

We dig into your workflows and find where AI makes the biggest difference.

Step02

Build

Custom models trained on your data. Tested until they pass your bar.

Step03

Deploy

Goes live on your infrastructure. Zero downtime. Zero drama.

Step04

Optimize

We keep watching, retraining, and fine-tuning. It only gets better.

DISCOVERBUILDDEPLOYOPTIMIZECONTINUOUS IMPROVEMENT
PIP.05ML Pipeline

Build. Clean. Train. Deploy.

A battle-tested pipeline that takes raw data to production models — no gaps, no guesswork.

Stage01

Build

Data Assembly

Assemble your dataset and define the model architecture. Configure inputs, outputs, and evaluation criteria.

ConfigLocked
SchemaValid
READY
Stage02

Clean

Data Processing

Automated validation, deduplication, and normalization. Bad data out before training begins.

Quality99.7%
Rows2.4M
PROCESSING
Stage03

Train

Model Training

Distributed training across GPU clusters. Real-time metrics, checkpointing, and early stopping built in.

Epochs120
Loss0.003
ACTIVE
Stage04

Deploy

Production Release

One-click deployment to production. Canary rollouts, A/B testing, and instant rollback if anything drifts.

Uptime99.9%
Latency<50ms
STANDBY
COM.05Open Channel

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Whether you need ML models with Mongolian language support, a full cloud platform, or on-premise deployment — let's figure it out together.

Or transmit directly to founders@raptora.io