From idea to deployment — we design, build, and scale AI systems that automate workflows and improve business efficiency.
4-8w
typical MVP delivery
24/7
automation-ready systems
API
first architecture
Cloud
native deployments
NeuroFlow OS
LiveRAG
ready
APIs
ready
Cloud
ready
Services
NeuroFlow AI helps teams move from scattered AI experiments to business-critical systems with clean architecture and measurable outcomes.
Automate high-volume operational workflows with intelligent assistants, routing logic, and human-in-the-loop safeguards.
Design and build internal AI systems, API-first products, recommendation engines, and marketplace-style AI platforms that are secure, observable, and scalable.
Ship AI systems into production with cloud-native infrastructure, monitoring, lifecycle management, and integration support.
Portfolio
A track record across internal AI PaaS, RAG assistants, LLM deployment, MLOps, voice AI, and predictive intelligence for business-critical workflows.
Architected a Netflix/Uber-style internal AI PaaS on Kubernetes, powering 100+ simultaneous ML and GenAI projects with self-serve GPU workspaces, plugin-based RAG, inference, vector database, and training capabilities.
Designed an AI workflow for complaint intake, classification, priority detection, policy-aware response drafting, and escalation routing so support teams can resolve customer complaints faster with consistent quality.
Built an internal AI support assistant that reduced manual platform support by combining FAISS-based RAG, fixed Q&A flows, and a GitHub agent that can propose YAML config changes through pull requests.
Hosted LLaMA 3 with vLLM and Ray Serve, then integrated LlamaIndex and Weaviate for HR policy Q&A using semantic chunking, BM25, and cosine similarity retrieval.
Delivered a legal AI system with BERT classification across 75 document categories, BiLSTM and spaCy NER, plus end-to-end MLOps on GKE with ZenML, Seldon, Kubeflow, and MLflow.
Built a multi-stage AI interviewer that scores resumes against job descriptions and conducts automated voice screening with Azure OpenAI, LLaMA 3.1, and bidirectional speech services.
Developed XGBoost and ensemble models for churn risk and customer lifetime value prediction, supporting product strategy with A/B testing and projected revenue uplift opportunities.
Case Studies
Production-grade AI systems built for healthcare operations, enterprise governance, predictive intelligence, and document automation.
Enterprise AI Delivery
Client: Private Healthcare Organization (Satara)
RAG
intelligence layer
Vector
semantic retrieval
Agent
assisted workflow
Challenge
Healthcare staff needed faster identification of government healthcare schemes applicable to patients.
Solution
Built AI-powered RAG platform leveraging vector database architecture and retrieval systems.
Capabilities
Technology
Business Impact
Enterprise AI Delivery
100+
AI initiatives
POC -> Prod
lifecycle
Secure
AI operations
Challenge
Large organization required secure enterprise AI platform supporting large-scale GenAI and ML workloads.
Solution
Built Kubernetes-based AI platform supporting production-grade AI lifecycle management.
Capabilities
Technology
Business Impact
About
NeuroFlow AI works with founders, operators, and enterprise teams to turn AI opportunities into reliable systems: discovery, architecture, model integration, product development, deployment, and scaling.
We build beyond demos: typed APIs, deployment strategy, observability, security boundaries, and maintainable codebases.
Each engagement ties AI features to operational metrics: cycle time, support load, throughput, accuracy, and cost per task.
We design AI systems that can evolve into internal platforms, SaaS products, or reusable business infrastructure.
Contact
Share the workflow, product, or deployment challenge you want to solve. We will respond with a practical next step.
hello@neuroflowai.net
Response
Within 1 business day
Coverage
Remote-first, global delivery