from fastapi import FastAPI, HTTPException
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from pydantic import BaseModel

app = FastAPI()

class QueryRequest(BaseModel):
    question: str
    top_k: int = 3

@app.post("/query")
async def query_documents(request: QueryRequest):
    """RAG-powered document query endpoint"""

    # Initialize embeddings and vector store
    embeddings = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"))
    vectorstore = Pinecone.from_existing_index(
        index_name="documents",
        embedding=embeddings
    )

    # Create RAG chain
    qa_chain = RetrievalQA.from_chain_type(
        llm=OpenAI(model="GPT-5", temperature=0),
        chain_type="stuff",
        retriever=vectorstore.as_retriever(
            search_kwargs={"k": request.top_k}
        ),
        return_source_documents=True
    )

    # Execute query
    result = qa_chain({"query": request.question})

    return {
        "answer": result["result"],
        "sources": [
            doc.metadata.get("source", "unknown")
            for doc in result["source_documents"]
        ],
        "confidence": 0.95
    }
                 
Enterprise AI Solutions

Intelligent Systems. Built to scale.

Transform your operations with production-grade AI infrastructure. Build intelligent systems, scalable backends, and automated workflows with cutting-edge technology—where innovation meets reliability.

Enterprise-grade capabilities

Build smarter . Scale seamlessly .

Accelerate AI development with production-ready infrastructure. Integrate LLMs, deploy ML models, and scale effortlessly with our enterprise platform.

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Production-Ready

Automated ML Pipelines

Train, evaluate, and deploy models with CI/CD automation. Includes version control, experiment tracking, and rollback capabilities.

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Scalable Architecture

Microservices & APIs

Build distributed systems with REST/gRPC APIs, message queues, and event-driven architecture for maximum scalability.

Enterprise Security

Data Protection & Compliance

End-to-end encryption, IAM roles, audit logging, and compliance-ready infrastructure (SOC 2, HIPAA, GDPR).

Cloud-Native

Multi-Cloud Deployment

Deploy across AWS, GCP, and Azure with Kubernetes orchestration, auto-scaling, and global CDN distribution.

Enterprise-grade capabilities

Build smarter Scale seamlessly

Accelerate AI development with production-ready infrastructure. Integrate LLMs, deploy ML models, and scale effortlessly with our enterprise platform.

Seamless LLM Integration

Effortlessly connect GPT-5, Claude 3, and other AI models with our plug-and-play solutions.

Custom AI Pipelines

Build tailored ML workflows with fine-tuning, RAG systems, and custom model deployment.

Real-Time Analytics

Monitor model performance, API usage, and costs with comprehensive dashboards and alerts.

Cloud Infrastructure

Deploy on AWS, GCP, or Azure with autoscaling, load balancing, and global CDN distribution.

Enterprise-grade capabilities

Build smarter. Scale seamlessly.

from fastapi import FastAPI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from pydantic import BaseModel

app = FastAPI()

class QueryRequest(BaseModel):
    question: str

@app.post("/query")
async def query_rag(request: QueryRequest):
    """RAG-powered document query"""

    # Initialize embeddings
    embeddings = OpenAIEmbeddings()

    # Connect to vector store
    vectorstore = Pinecone.from_existing_index(
        index_name="documents",
        embedding=embeddings
    )

    # Search similar documents
    docs = vectorstore.similarity_search(
        request.question,
        k=3
    )

    return {
        "answer": "Generated response...",
        "sources": [d.metadata["source"] for d in docs]
    }
           
import jwt
from datetime import datetime, timedelta
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2
from cryptography.hazmat.backends import default_backend

class AuthService:
    def __init__(self, secret_key: str):
        self.secret_key = secret_key

    def generate_token(
        self,
        user_id: str,
        expires_in: int = 3600
    ) -> str:
        """Generate JWT token"""
        payload = {
            'user_id': user_id,
            'exp': datetime.utcnow() + \
                   timedelta(seconds=expires_in),
            'iat': datetime.utcnow()
        }
        return jwt.encode(
            payload,
            self.secret_key,
            algorithm='HS256'
        )

    def verify_token(self, token: str) -> dict:
        """Verify JWT token"""
        try:
            payload = jwt.decode(
                token,
                self.secret_key,
                algorithms=['HS256']
            )
            return payload
        except jwt.ExpiredSignatureError:
            raise Exception("Token expired")
        except jwt.InvalidTokenError:
            raise Exception("Invalid token")
           
Proven impact

Data that drives change, shaping the future

Decentralized, secure, and built to transform industries worldwide. See how our platform enables sustainable growth and innovation at scale.

Our platform not only drives innovation but also empowers businesses to make smarter, data-backed decisions in real time. By harnessing the power of AI and machine learning, we provide actionable insights that help companies stay ahead of the curve.

Empowered users
Funds recovered
Failures overcome
System efficiency
Trusted by Industry Leaders

Empowering innovators, shaping the future

David Gutierrez

David Gutierrez

CTO at TechFlow AI

"The RAG system they built for us reduced our support tickets by 60%. Their expertise in LLM integration is unmatched."

Pierluigi Camomillo

Pierluigi Camomillo

VP Engineering at DataScale

"They migrated our monolith to microservices seamlessly. We saw a 40% cost reduction and significantly improved scalability."

Ella Svensson

Ella Svensson

Founder at MediSort Health

"Their ML-powered patient triage system transformed our operations. 70% faster triage with 94% accuracy—sim incredible results."

Alexa Rios

Alexa Rios

Chief Product Officer at ShopMax

"The recommendation engine they built increased our AOV by 32%. Highly recommended for any e-commerce business looking to leverage AI!"

Simple, Transparent Pricing

Choose the plan that fits your needs. All plans include access to our core AI and backend development platform.

  • Starter

    Perfect for exploring AI development.

    $0

    • Access to basic AI models
    • Community support
    • 5K API requests per month
  • Most Popular

    Professional

    50% off for the first 3 months!

    $999/month

    • Access to GPT-5 and Claude 3
    • Vector database integration
    • Advanced analytics dashboard
    • 100K API requests per month
    • Priority support
  • Enterprise

    For large-scale AI applications.

    Custom

    • Unlimited API access
    • Custom model fine-tuning
    • Multi-cloud deployment
    • Dedicated infrastructure
    • 24/7 premium support
  • MVP

    One-time project fee.

    $5K-$15K

    • Custom LLM chatbot
    • REST API development
    • Basic cloud deployment
    • Documentation & handoff
Faq

Frequently Asked Questions

If you can't find what you're looking for, email our support team and if you're lucky someone will get back to you.

    How do you ensure data security when processing with LLMs?

    We implement enterprise-grade security with end-to-end encryption for all data in transit. Sensitive data is anonymized before sending to LLM providers, and we offer self-hosted models for clients with strict data residency requirements. All credentials are managed using AWS KMS or equivalent encryption standards.

    What happens if an AI model produces incorrect results?

    We implement comprehensive evaluation frameworks with human-in-the-loop validation. Our systems include confidence scoring, fallback mechanisms, and automatic alerts when quality drops below thresholds. We also maintain version control for prompt templates and model parameters for quick rollbacks.

    Can I deploy AI models on my own infrastructure?

    Yes, we support hybrid deployments where sensitive workloads run on your infrastructure (AWS/GCP/Azure) while non-sensitive operations use managed APIs. We provide Terraform templates and Kubernetes manifests for self-hosted deployments, ensuring you maintain full control over your data and compute resources.

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