Introduction

SeeMe.ai is a complete AI platform for building, training, deploying, and managing machine learning models—without giving up control of your data.

ℹ️

Sovereign AI: Your data never leaves your infrastructure. Deploy on-premise, in your cloud, or at the edge. GDPR compliant and EU AI Act ready.

What Makes SeeMe.ai Different

Traditional MLOps	SeeMe.ai
Data uploaded to third-party clouds	Your data stays on your infrastructure
Vendor lock-in to specific frameworks	Framework agnostic: PyTorch, TensorFlow, ONNX, spaCy
Separate tools for each ML task	Unified platform for all AI capabilities
Code-only interfaces	No-code, low-code, and full SDK options
Cloud-only deployment	Cloud, on-premise, edge, and mobile
Manual pipeline management	Automated workflows with visual builder

Platform Capabilities

SeeMe.ai handles the complete AI lifecycle:

graph LR
    subgraph "Data"
        A[Datasets] --> B[Labeling]
        B --> C[Versions]
    end

    subgraph "Train"
        C --> D[Finetuning]
        D --> E[Distillation]
        E --> F[Optimization]
    end

    subgraph "Deploy"
        F --> G[Cloud API]
        F --> H[On-Premise]
        F --> I[Mobile/Edge]
    end

    subgraph "Automate"
        G --> J[Workflows]
        J --> K[Post-Processors]
        K --> L[Graphs]
    end

AI Task Types

Build any type of AI application:

Task	Description	Use Cases
Image Classification	Categorize images into classes	Quality inspection, content moderation, medical imaging
Object Detection	Locate and identify objects	Inventory counting, security, autonomous systems
Text Classification	Categorize text documents	Sentiment analysis, spam detection, ticket routing
Named Entity Recognition	Extract entities from text	Contract analysis, medical records, compliance
OCR & Documents	Extract text from images/PDFs	Invoice processing, document digitization
Sound Classification	Classify audio signals	Voice commands, equipment monitoring, wildlife
Tabular/Structured	Predict from structured data	Fraud detection, demand forecasting, risk scoring
Language Models	LLM integration with Ollama/vLLM	Summarization, extraction, chat, RAG

Advanced Features

Feature	What It Does
Workflows	Chain multiple models into intelligent pipelines
Post-Processors	Automatically label uploads with AI models
Knowledge Graphs	Build and query entity relationships
Model Distillation	Train small models from large teacher models
Model Finetuning	Adapt pre-trained models to your domain
Multi-format Export	ONNX, CoreML, TFLite, TensorRT

How You Can Work

Choose the interface that fits your workflow:

No-Code (Web Platform)

Build AI models visually without writing code:

Drag-and-drop dataset management
Visual workflow builder
One-click training and deployment
Real-time monitoring dashboards

Best for: Domain experts, analysts, rapid prototyping

Python SDK

Full programmatic control for automation and integration:

from seeme import Client

client = Client()

## Train a model
from seeme.types import Job, JobType, JobItem, ValueType

my_job = Job(
    name="Train classifier",
    job_type=JobType.TRAINING,
    application_id="your-application-id",
    dataset_id="your-dataset",
    dataset_version_id="your-version",
    items=[
        JobItem(name="nb_epochs", value="20", value_type=ValueType.INT, label="Epochs"),
    ]
)
my_job = client.create_job(my_job)

# Deploy and predict
result = client.predict(model_id=my_job.model_id, item="image.jpg")
for item in result.inference_items:
    print(f"{item.prediction}: {item.confidence:.1%}")

Best for: Developers, MLOps engineers, CI/CD integration

REST API

Integrate from any language or platform:

# Make a prediction
curl -X POST "https://api.seeme.ai/api/v1/inferences/{model_id}" \
  -H "Authorization: myusername:my-api-key" \
  -F "file=@image.jpg"

Best for: Polyglot environments, microservices, serverless

Mobile SDKs

On-device inference for iOS and Android:

Offline capability
Real-time camera processing
Optimized for mobile hardware
Background sync when connected

Best for: Field applications, consumer apps, embedded systems

Deployment Options

Option	Latency	Data Location	Best For
Cloud API	~100-500ms	SeeMe.ai cloud	Quick start, variable load
On-Premise	~10-50ms	Your servers	Data sovereignty, compliance
Edge	~5-30ms	Edge devices	Real-time, offline
Mobile	~5-20ms	User devices	Consumer apps, field work

Security & Compliance

GDPR Compliant: Full data processing controls
EU AI Act Ready: Transparency and documentation built-in
SOC 2: Enterprise security standards
Air-Gapped: Complete offline deployment option
RBAC: Fine-grained access control with teams and roles

Getting Started

5-Minute Quickstart No-Code Path Python SDK REST API

Learn More

Guides Workflows Platform SDK Reference

Overview