Data + Systems Integration
AI models are only as effective as the data infrastructure feeding them. We build robust ETL/ELT pipelines, set up vector search databases, clean unstructured data repositories, and connect isolated enterprise systems to create a unified data foundation for intelligent automation.
Best for: Companies with data siloed across legacy databases, cloud storage, and SaaS applications that need a clean data layer to power AI applications.
Clean Data Plumbing Powers High-Accuracy AI
Artificial intelligence models and retrieval systems are fundamentally constrained by the structure, cleanliness, and latency of the data pipelines feeding them. Scattered, unstructured, or stale data leads to inaccurate AI responses and broken search experiences. We design robust ETL/ELT data pipelines, event-driven Change Data Capture (CDC) webhooks, and high-performance vector databases (Pinecone, PGVector)—transforming messy enterprise databases into real-time, semantically indexed context sources.
What's included
Enterprise system & SaaS integrations
Building high-throughput connectors between SQL/NoSQL databases, cloud buckets, CRMs, ERPs, and internal business tools.
Vector database & hybrid search architecture
Deploying and optimizing vector search stores (pgvector, Pinecone, Qdrant) with hybrid keyword and semantic retrieval.
Data cleaning, chunking & embedding pipelines
Automated data parsing, HTML/PDF extraction, semantic chunking, and embedding generation pipelines to keep context fresh.
Real-time event streaming & webhook sync
Setting up event-driven architectures with Kafka, RabbitMQ, or serverless webhooks to reflect data changes instantly in AI context.
Data governance & access control security
Enforcing role-based access controls (RBAC), data masking, and PII anonymization before data enters LLM retrieval pipelines.
How we deliver
A phase-gated engineering process designed for transparency, zero compliance surprises, and rapid velocity.
Task Scoping & Data Audit
We isolate the specific operational task to automate, audit source data quality, and define strict evaluation benchmarks for accuracy and speed.
Real-Data RAG Prototyping
We build a rapid working prototype against your actual data, testing vector retrieval, embeddings, and prompt strategies before full build.
Guardrails & Human-in-the-Loop
We install input/output firewall proxies, hallucination controls, automated evaluation benchmark suites, and fallback human approval gates.
Production Deploy & Monitoring
Deployment to production with continuous token expenditure tracking, latency monitoring, and automated knowledge base update webhooks.
Questions people ask
What if our data is messy, unstructured, and scattered across tools?
That's the standard starting point. We clean, deduplicate, structure, and link data from disparate sources into clean pipelines built for AI consumption.
Do you set up retrieval infrastructure (RAG and vector search) for our AI?
Yes. We design and optimize vector databases (pgvector, Pinecone, Qdrant) with semantic chunking and embedding pipelines for sub-second context retrieval.
How do you maintain real-time data sync between our systems and AI models?
We implement event-driven CDC (Change Data Capture) pipelines and webhooks so system updates reflect instantly in vector context.
What security and role-based permissions apply to vector database search?
We mirror your application's tenant isolation and RBAC rules in the vector store so users only search context they have permission to access.
How do you handle rate limits, retry logic, and network failures in data pipelines?
Our pipelines use exponential backoff, dead-letter queues, and atomic transactions to guarantee zero data loss during upstream API outages.
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- hello@lumyte.com
- Phone
- +91 72330 30040
- Studio
- Patel Nagar, NeelmathaLucknow, Uttar Pradesh 226002