AI Product & Platform Lab

Ambharii
Labs

Building AI That Ships

From research prototype
to production system —
this is the gap we close.

Ambharii Labs designs and builds production-grade AI products, open-source platforms, and agentic systems for enterprises where reliability is not optional. Founded by Anil Prasad — 28 years of shipping AI at ISRO, Fortune 100 energy, healthcare, and financial services.

View Open Source
Available for Engagements
Anil Prasad — Founder, Ambharii Labs
Anil Prasad Founder · Ambharii Labs
Focus Areas

What We
Build & Deliver

01
🤖
AI Agents & Agentic Systems

Multi-step autonomous agents that reason, act, and recover from failure — built for regulated industry constraints and production observability from day one.

LangChain LangGraph AutoGen Tool Use
02
📚
RAG Systems

Retrieval-Augmented Generation designed for groundedness, not just recall. We solve chunking failures, context drift, and the embedding gaps that kill production RAG.

Vector DBs Reranking Hybrid Search Evaluation
03
⚙️
Fine-Tuning & Model Alignment

Domain-specific LLM fine-tuning with RLHF, DPO, and LoRA. Optimized for latency, cost, and safety constraints that enterprise deployments actually require.

LoRA RLHF DPO Quantization
04
🔬
Open Source AI Platforms

Production-grade open-source frameworks for MLOps, LLMOps, and AI perception — built for enterprise adoption with real governance, not toy implementations.

PulseFlow CASPAR SAM3 G-ARVIS
05
🏗️
AI Product Development

End-to-end AI product design and build — from architecture to deployment. We ship systems that survive contact with real data, real users, and real compliance requirements.

Architecture API Design Cloud Native HIPAA/SOX
06
🧭
Enterprise AI Consulting

Strategic and technical advisory for companies building or transforming AI capabilities — from evaluation framework design to platform architecture and team upskilling.

LLMOps MLOps Strategy Due Diligence
Open Source

Production Platforms,
Free to Adopt

GitHub →
PulseFlow
MLOps Platform · Open Source
Enterprise MLOps framework for production model lifecycle management. Covers experiment tracking, model registry, automated evaluation pipelines, drift detection, and governance dashboards — built for regulated industries where audit trails are mandatory.
Python FastAPI Airflow MLflow
CASPAR
Cost Intelligence · Enterprise AI
AI-powered cost intelligence platform built at Fortune 150. Predictive cost analytics, anomaly detection, and explainable forecasting for capital-intensive infrastructure — with full auditability for SOX-compliant environments.
RAG Time-Series LLMOps AWS
SAM3
Perception Hub · Multimodal AI
Enterprise AI perception platform combining computer vision, NLP, and multimodal document understanding. Powers intelligent document processing, anomaly detection, and real-time data extraction across unstructured enterprise data at scale.
CV NLP Multimodal Real-time
ScalableGen
Database Scaling · Healthcare AI
Vitess-based horizontal MySQL sharding for healthcare and genomics. Enables zero-downtime migrations from monolithic MySQL to sharded clusters — with ETL pipelines, monitoring, and cloud portability. Inspired by the Ambry Genetics production migration.
Vitess MySQL AWS Healthcare
Evaluation Framework

The G-ARVIS
Framework

Six production dimensions of LLM health — distilled from building AI systems that govern billions in capital decisions across regulated industries. Not benchmarks. Not MMLU. The metrics that determine whether your AI holds up at 3am on a Tuesday.

Read the Full Paper →
G
Groundedness
Output traceability to source data. Hallucination prevention at the retrieval and generation layer.
A
Accuracy
MAPE + ECE metrics. Calibrated confidence, not just binary correctness on curated test sets.
R
Reliability
P99 latency and SLO adherence under real production load with traffic spikes and degraded dependencies.
V
Variance
Semantic stability across identical inputs. Consistency that business stakeholders can trust and audit.
I
Inference Cost
Cost per correct answer, not per API call. The unit economics that determine whether AI is viable at scale.
S
Safety
Guardrail calibration, bias audits, and the compliance audit trail that regulators actually require.
Founder

Anil
Prasad

I started my AI career at ISRO, training neural networks before transformers existed. Three decades later the belief that shapes every engagement at Ambharii Labs is unchanged: AI systems are only as valuable as their reliability in production.

I have led platform transformations that generated over $4B in measurable business outcomes — from the CloudmedAI integration behind a $4.1B acquisition at R1 RCM, to cost intelligence systems governing billions in capital decisions at Fortune 150 firm.

Ambharii Labs exists because the gap between demo and production is where most AI investments fail. I built this lab to close that gap.

Stanford University
IEEE Member
CAIO Circle Co-Founder
100 Most Influential AI Leaders
ISRO / IISc Research Background
BITS Pilani Alumnus
Anil Prasad — Founder, Ambharii Labs
Let's Build
Something That
Actually Works

Open to consulting engagements, AI platform architecture, technical advisory, and speaking. Especially interested in organizations where AI failures have real consequences.

AI Strategy Platform Architecture Agentic AI RAG Systems Fine-Tuning Open Source Speaking