Enterprise AI Capabilities Built for Privacy & Performance

Production-ready solutions that keep your data sovereign while delivering measurable business value

Core Differentiator

Privacy-First AI Architecture

Your data never leaves your environment. Our on-device inference and edge computing solutions ensure complete data sovereignty while delivering enterprise-grade performance.

Ollama Integration: Deploy open-source LLMs locally with zero data exposure
Federated Learning: Train models across distributed data without centralization
Synthetic Data Generation: 40% better model performance through privacy-preserving techniques
Edge Computing: Millisecond response times without cloud dependencies

99.5%

PII Detection Accuracy

Zero

Data Exposure Risk

100%

Compliance Ready

# On-Device Inference Example
from ollama import Client

client = Client(host='localhost:11434')
response = client.generate(
    model='llama2-7b',
    prompt='Analyze customer sentiment',
    context=local_data,
    stream=False
)

# Your data never leaves your servers
# Full control over model deployment
# GDPR, HIPAA, SOC2 compliant by design
                    

# Multi-Agent Orchestration
from langgraph import StateGraph
from autogen import AssistantAgent

# Create specialized agents
research_agent = AssistantAgent(
    name="Research",
    system_message="Deep analysis"
)
analysis_agent = AssistantAgent(
    name="Analysis",
    system_message="Data insights"
)
decision_agent = AssistantAgent(
    name="Decision",
    system_message="Strategic recommendations"
)

# Orchestrate collaboration
workflow = StateGraph()
workflow.add_node("research", research_agent)
workflow.add_node("analyze", analysis_agent)
workflow.add_node("decide", decision_agent)
                    

Advanced Architecture

Multi-Agent Systems

Specialized AI agents that work together like expert teams, each handling specific tasks while collaborating to solve complex business challenges.

LangGraph Orchestration: State-of-the-art agent coordination and workflow management
AutoGen Framework: Self-organizing agents that adapt to your business processes
Task Specialization: Domain-specific agents for legal, financial, and operational tasks
Model Context Protocol: Seamless integration with existing systems

10x

Faster Processing

85%

Task Automation

24/7

Continuous Operation

Infrastructure Optimization

PostgreSQL + pgvector Excellence

Leverage your existing PostgreSQL infrastructure for vector operations. Get cutting-edge AI capabilities without the complexity and cost of dedicated vector databases.

Seamless Integration: Works with your existing PostgreSQL deployment
Cost Optimization: 60-80% savings vs. dedicated vector databases
Performance Tuning: 9× faster queries through expert optimization
RAG Applications: Production-ready retrieval augmented generation

100B+

Vectors Handled

9×

Query Speed

80%

Cost Reduction

-- pgvector optimization example
CREATE EXTENSION vector;

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(1536)
);

-- Create optimized index
CREATE INDEX ON documents 
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

-- Semantic search query
SELECT content, 
       1 - (embedding <=> query_embedding) AS similarity
FROM documents
ORDER BY embedding <=> query_embedding
LIMIT 10;

-- 100× better relevance
-- Uses existing PostgreSQL skills
-- No new infrastructure needed
                    

# Custom LLM Fine-Tuning Pipeline
from transformers import AutoModelForCausalLM
from datasets import Dataset

# Load and prepare your domain data
dataset = Dataset.from_json("your_data.json")

# Fine-tune for your specific needs
model = AutoModelForCausalLM.from_pretrained(
    "base-model",
    device_map="auto",
    load_in_8bit=True
)

# Evaluation framework
evaluator = HallucinationDetector()
factuality_score = evaluator.check(
    model_output,
    ground_truth
)

# Deploy with confidence
# 95% accuracy on domain tasks
# Zero hallucination tolerance
                    

Custom Intelligence

LLM Engineering & Fine-Tuning

Transform foundation models into specialized experts that understand your industry's unique language, compliance requirements, and business logic.

Custom Training: Fine-tune models on your proprietary data and processes
Prompt Engineering: Optimize interactions for consistent, accurate outputs
Evaluation Frameworks: Hallucination detection and factuality metrics
Memory Systems: Persistent context for long-term interactions

95%

Domain Accuracy

50+

Models Deployed

0.01%

Hallucination Rate

Our Technology Stack

Battle-tested tools and frameworks for production AI

Languages

Python, SQL, JavaScript, TypeScript

LLM Frameworks

LangChain, LangGraph, AutoGen, MCP

Models

GPT-4, Claude, Llama 2/3, Mistral

Databases

PostgreSQL, pgvector, Redis, MongoDB

ML/AI

PyTorch, TensorFlow, Scikit-learn

Deployment

Docker, Kubernetes, Ollama, Edge

Analytics

Pandas, NumPy, Plotly, Chart.js

Security

OAuth, JWT, SSL/TLS, Encryption

Ready to Deploy Enterprise AI That Respects Your Data?

Let's explore how our capabilities can transform your business

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