Consensus-Driven Evaluation
Running parallel queries across multiple LLM providers to verify threats before taking action.
AI & MACHINE LEARNING
Multi-LLM threat engines & local vector AI.
We engineer practical artificial intelligence systems - from multi-LLM threat evaluation engines containing security incidents in under 5 minutes to local-first vector engines operating on-device with sub-12ms latency.
AI PRACTICE SPECS< 5MIN SLA
SIEM Threat EngineBharat Shield Multi-LLM
Local Vector SearchNeoNote IndexedDB
Vector Latency< 12ms On-Device
FrameworksPyTorch / FastAPI / LangChain
Bengaluru Senior AI Engineers
< 5 min SLAReal SLA
Bharat Shield automated threat containment
< 12msSub-12ms
NeoNote local-first vector search
3 LLM ModelsZero Hallucination
Consensus-driven parallel evaluation
100% PrivacyZero Cloud
On-device vector storage option
01 / AI CAPABILITIES
Production artificial intelligence capabilities.
On-Device Semantic Search
Embedding text locally inside IndexedDB for instant, private semantic search without cloud transmission.
DOM Visual-Diff & Inspection
Playwright automated DOM visual snapshot auditing catching site defacement or malvertising.
02 / AI PHILOSOPHY
AI should solve deterministic engineering problems, not fabricate plausible answers.
We ground AI outputs with strict consensus evaluation, deterministic verification code, and offline fallback execution.
03 / AI TOOLCHAIN
Curated AI & ML matrix.
01 // LLM ENGINES
- Multi-LLM Consensus Pipeline
- FastAPI Microservices
- LangChain & LlamaIndex
- Local Ollama Inference
02 // VECTOR STORES
- IndexedDB Local Store
- Qdrant & pgvector
- ChromaDB Vector Store
- HNSW Vector Indexing
03 // VISION & ASR
- Playwright Visual-Diff
- Whisper Speech-to-Text
- OpenCV Image Analytics
- YOLO Micro Inspection
04 // DASHBOARDS
- Streamlit SIEM Dashboards
- Grafana Metric Monitors
- React 19 Real-Time Telemetry
- Audit Trail Log Export
04 / AI WORKFLOW
Our 4-step AI implementation lifecycle.
Use-Case Risk Audit
Evaluating accuracy requirements, latency thresholds, and data privacy boundaries.
Multi-LLM & Vector Build
Building parallel API runners and local vector embedding pipelines.
SLA Verification
Testing containment response times under live threat scenarios (<5min SLA).
Production Operations
Deploying Streamlit SIEM dashboards and on-device vector engines.
05 / INSPECTABLE CODE
AI & ML code snippets.
ai/multi_llm_evaluator.py
Consensus-driven evaluation pipeline calling 3 LLMs in parallel to verify threat vector validity without single-model hallucination.
1import asyncio2from typing import List34async def evaluate_threat_consensus(payload: dict) -> dict:5 # Query 3 independent LLM providers in parallel6 results = await asyncio.gather(7 call_llm("claude-3-5-sonnet", payload),8 call_llm("gpt-4o", payload),9 call_llm("llama-3-70b-local", payload)10 )11 # Require 2/3 model consensus before executing containment12 consensus = calculate_consensus(results)13 return {"consensus_reached": consensus.valid, "threat_level": consensus.level}06 / GUARANTEES
AI project guarantees.
100% IP Transfer
Complete model pipelines, vector schemas, and prompt templates transferred.
Contractual SLA Commitments
Contractual incident response SLAs of <5 minutes for SIEM threat engines.
Upfront Mutual NDA
Protecting your training datasets and internal processes with formal NDAs.
PRODUCTION PROOF
Bharat Shield Threat Intelligence Engine
Multi-LLM threat evaluation pipeline containing critical security vulnerabilities in under 5 minutes with Playwright DOM visual-diff change detection and Streamlit SIEM monitoring.
BHARAT SHIELD SPECCONTAINED
• 3 LLM Model Consensus Evaluation
• 184s Response SLA (<5min SLA)
• Playwright DOM Visual-Diff Snapshot
• Streamlit Real-Time Threat Feed
07 / AI ML FAQS
Questions about our AI practice?
How does multi-LLM consensus work in Bharat Shield?
Bharat Shield evaluates security threats by querying three independent LLM models simultaneously. The system executes automated containment only when at least two models reach consensus on the threat classification.