Enterprise Knowledge Intelligence v0.3.0 · Live on Azure

Inference is replaceable.
Governance is not.

Upload your documents. Ask questions in plain English. Get cited, grounded answers from your own content — with confidence scoring, refusal enforcement, full audit trail, and multi-tenant isolation.

"Squirro returns document lists. RAGGR™ returns cited answers. Same connectors. 1/10th the cost. Built by someone who used Squirro at International Bank."

RAGGR™ Competitive Position · March 2026
Try Enterprise Demo → 📊 LLM Benchmark 📋 API Docs
RBAC Pre-Filter Maker-Checker Workflow Full Audit Trail PII Detection Citation Enforcement Multi-Tenant Isolation Confidence Scoring KaaryaaKairo Routing 4 Weeks to Production Azure Central India RBAC Pre-Filter Maker-Checker Workflow Full Audit Trail PII Detection Citation Enforcement Multi-Tenant Isolation Confidence Scoring KaaryaaKairo Routing
The Problem

Why generic AI fails regulated industries

Every GCC and bank in India sits on thousands of documents. Employees spend 30–40% of their time searching for information that already exists inside the organisation. Generic AI makes it worse — not better.

Stage 01 · 2022–2024 🤖
Generic AI / ChatGPT

Answers from internet knowledge, not your documents. No source control. No access control.

Compliance rejected it
Hallucinations on policy questions
No audit trail
Stage 02 · 2024–2025 🔍
DIY RAG (LangChain / LlamaIndex)

Grounded in documents — but 6–12 months to build production-ready. No governance layer.

RBAC built from scratch
No maker-checker
No PII scanning
Stage 03 · 2025 → Now 🛡️
RAGGR™ — RAG with GuardRails

The governance layer built for compliance-first organisations. Deploy in 4 weeks, not 12 months.

RBAC pre-filter · maker-checker
Full audit trail · PII detection
Citation-enforced answers
Platform Features

Everything compliance demands

Six governance layers built directly into the engine — not bolted on as afterthought plugins.

🔐
RBAC Pre-Filter

Users only see documents they're authorised to access. Role-based filtering happens before vector search — not after.

→ section_level_acl
Maker-Checker Workflow

No document goes live unreviewed. Four-stage approval: draft → review → approve → publish. Full status tracking.

→ maker_checker_pipeline
📋
Full Audit Trail

Every query logged permanently — user, document pool, model used, answer, confidence score, timestamp. Exportable for regulators.

→ kairo_logs table
🛡️
PII Detection

Aadhaar, PAN, Passport, IFSC, UPI, Voter ID flagged at ingestion and pre-delivery. Microsoft Presidio engine with India-specific recognisers.

→ KaaryaaPII module
📎
Citation Enforcement

Every answer must cite its source page. Answers without citations are rejected by the guardrail layer before delivery.

→ citation_enforcer
🏢
Multi-Tenant Isolation

One engine, many organisations. Complete pool isolation. Per-tenant API keys, model overrides, and usage dashboards.

→ TenantAuthMiddleware
Model Benchmark

GPT-4o vs Llama 3.3 70B —
130 hard questions

GoldenEval V3: 130 questions across MCQ, fill-blank, keyword, and open-ended formats. Run on the GenAI with LangChain corpus. KaaryaaKairo routes by query type — not by preference.

MetricGPT-4oLlama 3.3 70B
Citation Rate82%96%
Faithfulness94%96%
Avg Latency3.8s1.2s
Cost / Query~₹0.57₹0.00 (free)
OOS Refusal100%100%
Hallucinations0%0%

Llama 3.3 70B via Groq outperforms GPT-4o on citation rate, faithfulness, latency, and cost — for structured queries. GPT-4o reserved for compliance and complex open-ended.

Run Benchmark →
Faithfulness96%
Citation Rate (Llama)96%
OOS Refusal Rate100%
Zero Hallucination100%
Questions Answered97%
Competitive Position

RAGGR™ vs the alternatives

Alternative
Squirro / Elasticsearch
Returns document lists, not answers
~RBAC available but costly
No citation enforcement
No PII detection layer
10x+ cost at enterprise scale
No confidence scoring
RAGGR™ · Recommended
RAGGR™ Engine
Cited answers from your documents
RBAC pre-filter built-in
Citation enforced on every answer
PII scan at ingestion + delivery
SaaS / cloud / on-prem tiers
High / Medium / Low confidence
Alternative
MS Copilot / Custom GPT
No document-level RBAC
No maker-checker workflow
Hallucination risk on edge cases
Data leaves your control
M365 E5 licence dependency
No custom audit log export
Deployment Tiers

Three ways to deploy

From shared SaaS for proof-of-concept to air-gapped on-premise for GCC compliance requirements.

Shared SaaS
Pilot
Start for free · Pay as you scale
  • Up to 3 document pools
  • Shared RAGGR™ infrastructure
  • Full guardrail stack included
  • KaaryaaKairo model routing
  • Standard audit logging
  • 4-week onboarding
Start Pilot →
On-Premise / Air-Gap
Custom
Your infrastructure · Ollama on-prem
  • Deploy inside your Azure VNet
  • Ollama sidecar for local inference
  • Zero data leaves your environment
  • Full white-label option
  • Custom PII recogniser library
  • Compliance documentation
Contact Us →
Ready to deploy

Your documents deserve
a governed answer engine

GCC pilot targeting June 2026. Chennai banking network. Demo available now — no login, no setup.

Try Enterprise Demo → ✉ Contact Raja Raman, CEO
0%
Hallucination rate on governed queries
4w
Weeks to production deployment
51
API endpoints live today
1/10
Cost vs Squirro enterprise pricing