Production Agent Engineering · AI-native Lab

Ambharii
Labs

Building Agentic AI That Ships to Production

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 and across Fortune 100 / Fortune 150 large enterprises in energy, healthcare, and financial services.

View Open Source
28+
Years shipping AI
$4B+
Outcomes shipped
6
Open-source projects
ISRO
Origin · pre-transformer era
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.

Bulwark PulseFlow TorchForge ARGUS-AI CLARIFY 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 →
Featured Writing
The Web Is Now Weaponized Against Your AI Agents
Why prompt injection is the SQL injection of the agent era — and what shipped this week to defend against it. The launch essay behind Bulwark.
→
Bulwark
Agent Security Framework · Open Source
Five-layer defense for production AI agents against prompt injection — input sanitizer, ML-based injection detection, compartmentalized RBAC, encrypted audit trail, and human confirmation gates. MCP-native, vendor-neutral, HIPAA / SOC 2 / NERC CIP ready. Built because the web is now weaponized against agents.
Python Agent Security Prompt Injection MCP pip install bulwark-agent-security
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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
→
TorchForge
PyTorch Production Framework · Open Source
Production-grade PyTorch framework that bridges the gap between research and deployment. Standardized training loops, model registry, experiment tracking, and one-command deployment — built because 78% of PyTorch models never reach production.
PyTorch Python MLOps Production
→
ARGUS-AI
LLM Observability · Open Core
Autonomous LLM correction and observability engine. Scores AI outputs across 6 quality dimensions (G-ARVIS framework), detects hallucinations, and self-corrects with max 3 iterations. The open-core backbone of GenomixIQ and ARIA platforms.
Python LLM G-ARVIS pip install argus-ai
→
CLARIFY
AI Explainability · Open Source
Explainable AI framework for regulated industries. Generates human-readable explanations for model decisions with audit-grade traceability — built for healthcare, finance, and compliance environments where black-box AI is unacceptable.
Python XAI HIPAA Compliance
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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 real-world genomics platform migrations from monolithic to sharded production environments.
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 healthcare revenue-cycle integrations behind multi-billion-dollar M&A outcomes, to cost intelligence systems governing billions in capital decisions at Fortune 150 enterprises in energy and financial services.

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
🌐 Personal Portfolio anilsprasad.com 💼 LinkedIn in/anilsprasad ✍️ Field Notes: Production AI Newsletter Medium ⌨️ GitHub — Open Source Projects anilatambharii
Compliance Posture

Ambharii Labs is a consulting and open-source studio, not a SaaS — no customer data is stored, processed, or transmitted on our infrastructure.

Engagements run on the client's infra under the client's existing compliance regime. We align with your auditors; we don't replace them. Open-source products ship as code you run; we have no operational access to your deployment.

→ Read the full trust posture
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.

DM on LinkedIn View Full Portfolio
AI Strategy Platform Architecture Agentic AI RAG Systems Fine-Tuning Open Source AI Security Speaking