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
}
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.
Build smarter . Scale seamlessly .
Accelerate AI development with production-ready infrastructure. Integrate LLMs, deploy ML models, and scale effortlessly with our enterprise platform.
Automated ML Pipelines
Train, evaluate, and deploy models with CI/CD automation. Includes version control, experiment tracking, and rollback capabilities.
Microservices & APIs
Build distributed systems with REST/gRPC APIs, message queues, and event-driven architecture for maximum scalability.
Data Protection & Compliance
End-to-end encryption, IAM roles, audit logging, and compliance-ready infrastructure (SOC 2, HIPAA, GDPR).
Multi-Cloud Deployment
Deploy across AWS, GCP, and Azure with Kubernetes orchestration, auto-scaling, and global CDN distribution.
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.
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")
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.
Empowering innovators, shaping the future
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
VP Engineering at DataScale
"They migrated our monolith to microservices seamlessly. We saw a 40% cost reduction and significantly improved scalability."
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
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
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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Sign up here. Our team will promptly get in touch with you, providing an enablement pack tailored to your specific requirements.