LumrexAI Technologies ·Generative AI Lab. ·Pune, India

We Turn
Complex Domains Into
Intelligent Systems.

Finance. Pharma. Automotive. Manufacturing.
We architect production-grade AI that handles real data,
real compliance, and real consequences. Not demos.

Finance Pharma Automotive Manufacturing Medical
lumrexai.terminal — rag-pipeline v2.4 LIVE
lumrex init --domain finance --compliance SOX
Initializing RAG pipeline for Finance domain...
Loading constraints: SOX, SEC 17a-4
✓ PII masking layer active — 0 raw exposures
✓ Confidence gating threshold: 0.85
⚠ Query: "YoY APAC revenue Q3 2024?"
Retrieving: Annual_Report_FY2024.pdf...
✓ Source: Page 34 · Confidence: 94.2%
0% PHI Exposures
0% Classifier Acc.
0% Prod. Ready
RAG Pipeline Engineering Agentic AI Systems LLM Fine-Tuning PII & PHI Safety Architecture Document Intelligence Finance · Pharma · Automotive · Manufacturing RAG Pipeline Engineering Agentic AI Systems LLM Fine-Tuning PII & PHI Safety Architecture Document Intelligence Finance · Pharma · Automotive · Manufacturing

What we do

Most AI dies
at the production
boundary.

0%PHI Exposures
0%+Classifier Accuracy
0+Production Systems
0%Uptime SLA

We've seen it repeatedly — brilliant prototypes that crumble under real load, real compliance requirements, and real domain constraints. LumrexAI builds the systems that survive that boundary.

LLM pipelines with typed validation. Agentic systems with fallback paths. PII masking before any data reaches a model. Confidence gating before any output reaches a user. Every system. Every time.

—01

LLMs are non-deterministic. Your system cannot be. Every LLM call is wrapped in typed validation, retries, and schema enforcement.

—02

Guardrails are architecture, not afterthought. PII detection runs before any data reaches an LLM. Zero exposure by design.

—03

Every system must have a fallback path. DLQs, circuit breakers, human review queues. Systems degrade predictably.

—04

Observability from the first commit. Token cost, latency, confidence scores — logged from day one, not retrofitted.

—05

Build for the regulated domain, not the demo. Banking, pharma, automotive — these domains don't tolerate hallucinations.

Products

Shipped & in the lab.

📄
⚙ In Development

Infylr

infylr.app · Document Intelligence

Universal document intelligence. Upload any file — PDF, DOCX, XLSX, scanned, handwritten. Chat to fill any template. Download in seconds. No copy-pasting. No domain expertise required.

OCR Template Filling Multi-format Chat-Driven
📊
● Live — MVP-1

FinSight

Finance · PDF Intelligence

Production-grade financial intelligence. Analyze complex reports with enterprise-level security and compliance. Cited answers. Confidence scoring. Zero hallucinations by design.

Risk Analysis Compliance RAG
Building

More in the Lab

Pharma · Automotive · Manufacturing · Medical

CortexIQ, AutoSentinel, and more are in development. Each one built to the same production standard — typed validation, safety architecture, domain-specific accuracy.

🧬CortexIQ — Pharma
🚗AutoSentinel — Automotive
🏭ManuMind — Manufacturing
🏥MediParse — Medical

03  What we build

AI infrastructure
for regulated
industries.

We don't sell AI features. We architect systems that survive compliance audits, scale under load, and produce auditable outputs.

01
🔍

RAG Pipeline Engineering

Hybrid search, reranking, confidence gating. Vectorless PageIndex. Every answer cites its source page.

FinanceBankingLegal
02
🤖

Agentic AI Systems

LangGraph + CrewAI orchestration. State machines with typed validation, fallback paths, DLQ escalation.

PharmaManufacturing
03
🎯

LLM Fine-Tuning

Domain-specific model adaptation on SageMaker and Vertex AI. The 73%→92% accuracy jump that prompting alone can't deliver.

AutomotiveMedical
04
🔒

PII / PHI Safety Architecture

Presidio-grade guardrails. Mask before LLM, controlled unmask post-verification. Immutable audit logs.

PharmaBankingMedical
05
📄

Document Intelligence

Extract, classify, and query unstructured documents with full citation traceability to the source page.

FinanceLegalInsurance
06
🚀

AI Product Development

PoC to production-deployed AI product. Architecture, build, deploy, monitor. Full production boundary ownership.

All Domains

04  What powers us

Our technology arsenal.

Every tool battle-tested in production — chosen because it solves real problems in regulated domains. We don't chase hype.

AI & LLMs
Frameworks & AI IDE's
Cloud
Safety
Automation & CI/CD
Generative AI
Agentic AI Workflows/Systems
LLM Providers: OpenAI, Google, Anthropic, etc
LLMs: Llama, Qwen, Claude (Opus, Haiku, Sonnet), GPT-5.2, Codex, etc
LLM Fine-Tuning
SLM Fine-Tuning
RAG Pipelines
AI Agents / NLP
Python
LangChain
LangGraph
CrewAI
Autogen
TensorFlow
PyTorch
Hugging Face
Antigravity, Cursor, Copilot, Windsufr, Kiro, etc
GCP Vertex AI
GCP Workbench
GCP Cloud Run
AWS SQS
AWS Bedrock
AWS SageMaker
AWS Lambda
Google Model Armor
MS Presidio
PII Masking
Confidence Gating
Audit Logging
DLQ Escalation
Circuit Breakers
Custom Regex Pattern
Gemini Threat Pattern Detection
n8n Workflows
MCP Protocol
A2A Protocol
Cloud Run
Docker
GitHub Actions
Clean Code
Agile Methodologies

05  How we work

From problem
to production.

We don't start with tools. We start with your domain, your constraints, and your compliance requirements.

01

Discovery

Map your domain: data flows, compliance boundaries, latency requirements, and where AI creates real leverage.

02

Architecture

Design the system before a line of code is written. Guard layers, fallback paths, confidence thresholds — defined upfront.

03

Build

LangGraph pipelines, RAG systems, fine-tuned models. TypedDict schemas, not loose JSON.

04

Safety Layer

PII masking, output validation, audit logging, confidence gating. Not optional — part of every system we ship.

05

Deploy & Monitor

Token cost, latency, confidence scores — observable from day one. Not retrofitted when something breaks.

founder.profile Active
RK
Ravindra Kupatkar
Founder · Generative AI Engineer · Pune, India

I started LumrexAI because I saw too many AI projects fail at the production boundary — great demos, broken systems. I've spent years building in production, under real load, with real regulatory stakes.

About

Building AI that actually works in production.

RAG pipelines, multi-agent systems, and safety architectures for banking, pharma, and automotive domains. Not in a lab — in production, under real load, with real regulatory stakes.

Every product and system we build at LumrexAI is a direct response to that gap. We don't just ship code — we ship systems that survive the real world.

—01

LLMs are non-deterministic. Your system cannot be. Every LLM call is wrapped in typed validation, retries, and schema enforcement.

—02

Guardrails are architecture, not afterthought. PII detection runs before any data reaches an LLM. Zero exposure by design.

—03

Every system must have a fallback path. DLQs, circuit breakers, human review queues. Systems degrade predictably.

Let's build

Ready to ship AI
that stays running?

Whether you're starting from scratch or untangling a failing PoC — we'll architect something that survives production. Selectively taking on new projects now.