Engineered
for Growth.
A production-grade AI platform built on Python, Azure, and industry-standard MLOps tooling — designed for enterprise reliability.
Request Flow
Python-First, Built for Speed.
Our backend is engineered on Python 3.11+ with async-first architecture. Every service is built for high-concurrency agent orchestration.
Python 3.11+
Primary language for all backend services and ML pipelines
FastAPI
Async REST APIs with sub-200ms p95 latency for agent endpoints
LangChain / LangGraph
Agent orchestration framework with stateful graph-based workflows
Pydantic v2
Strict data validation and schema enforcement across all services
Redis + PostgreSQL
In-memory caching and persistent storage for agent state
Celery + RabbitMQ
Distributed task queues for background agent execution
Full Model Lifecycle Management.
From experiment tracking to production deployment — every model version is reproducible, auditable, and reversible.
MLflow
Experiment tracking, model registry, and artifact management
Weights & Biases
Real-time training visualization and hyperparameter sweeps
DVC (Data Version Control)
Version control for datasets and model artifacts
Kubeflow Pipelines
ML workflow orchestration on Kubernetes
Great Expectations
Automated data quality validation before model training
Model Registry
Centralized model versioning with stage transitions (dev → staging → prod)
Enterprise Cloud Infrastructure.
Deployed on Microsoft Azure with multi-region redundancy, private networking, and SOC 2 compliant controls.
Azure Kubernetes Service
Managed Kubernetes for horizontal auto-scaling of agent workloads
Azure OpenAI Service
Private LLM inference with data processing agreements and regional residency
Azure AI Foundry
Model fine-tuning, RAG pipelines, and prompt engineering studio
Azure Cosmos DB
Globally distributed, multi-model database for agent memory
Azure Blob Storage
Scalable object storage for documents, embeddings, and artifacts
Azure Key Vault
Centralized secrets management with HSM-backed key storage
Ship Fast, Ship Safe.
Automated testing, security scanning, and containerized deployments with rollback capability.
GitHub Actions
Automated build, test, and deploy pipelines on every push
Docker + Helm
Containerized deployments with templated Kubernetes manifests
ArgoCD (GitOps)
Declarative, auditable infrastructure with Git as source of truth
Terraform
Infrastructure-as-code for all Azure resources
Snyk + Trivy
Automated dependency and container image vulnerability scanning
Canary Deployments
Gradual traffic shifting with automated rollback on error rate spikes
Full Visibility into Every Prompt.
Trace, evaluate, and optimize every LLM call — from token usage to hallucination detection — with production-grade profiling tools.
LangSmith
End-to-end LLM tracing with prompt versioning and evaluation scores
Custom Token Profiler
Real-time token usage analytics with per-model cost attribution
Guardrails AI
Hallucination detection, PII redaction, and output quality checks
Prompt Versioning
A/B test prompt variants with automated evaluation metrics
Latency Profiler
P50/P95/P99 latency tracking per model, per endpoint
Compliance Logging
Immutable audit trail for every LLM interaction with 90-day retention
Built to Scale.
Our stack is production-ready. Let us show you how it fits your infrastructure.