The Brain Of
Enterprise Cost.
CostIntel is a fully autonomous AI system that unifies 7 specialized agents into a single stateful pipeline — detecting cost leakage, predicting SLA breaches, performing root-cause analysis with Amazon Bedrock, and autonomously remediating low-risk issues while routing critical decisions to human reviewers.
Detected Leakage
₹0L
Per simulation run
Autonomous Actions
0
Self-remediated (P3)
AI Agents
0
LangGraph orchestrated
Inference Latency
0s
Bedrock Nova Lite
SLA Breaches Avoided
0%
Predictive guardrails
Audit Coverage
0%
Every decision traced
What Is CostIntel
An End-to-End AI Operating System for Financial Governance.
CostIntel replaces fragmented, manual procurement oversight with a unified autonomous pipeline that continuously monitors enterprise spending, detects anomalies the moment they occur, performs deep root-cause analysis using large language models, and takes corrective action — all in under 3 seconds.
Processes 28K+ procurement records per pipeline execution
7 specialized AI agents orchestrated by LangGraph.js state machine
Amazon Bedrock (Nova Lite) for real-time LLM reasoning with Mistral failover
Autonomous remediation for low-risk events, human approval for high-impact decisions
Complete audit trail — every decision fingerprinted and traceable
System at a Glance
Architecture
7-agent stateful pipeline (LangGraph.js DAG)
Inference Engine
Amazon Bedrock — Nova Lite → Mistral Large
Data Backplane
DynamoDB (Stream, Audit, Approvals tables)
Decision Model
P1/P2/P3 severity × financial impact ranking
Automation
P3 → autonomous exec · P1/P2 → HITL approval
Observability
Per-agent JSON trace with RunID fingerprints
Frontend
Next.js 14 + Framer Motion (real-time updates)
The Problem
Enterprises lose 3–5% of annual procurement spend to undetected cost leakage.
For a company processing ₹500Cr in annual procurement, that's ₹15–25Cr lost every year to overlooked anomalies, duplicate charges, and contract non-compliance. CostIntel was designed to eliminate this.
Manual Auditing
Finance teams spend 200+ hours per quarter manually reviewing invoices and contracts. By the time leakage is found, the money is already lost.
200+ hrs/quarter
Reactive Detection
Traditional tools catch anomalies after the fact. Without predictive models, organizations are always responding to yesterday's problems.
72hr avg detection lag
Siloed Data Systems
Procurement, billing, vendor, and logistics data live in separate systems with no unified intelligence layer connecting them.
4+ disconnected systems
Slow Remediation
Even when anomalies are detected, the approval and remediation process takes days — during which leakage continues unchecked.
5–7 day remediation cycle
How It Works
5 Steps from Raw Data to Remediation.
Every pipeline execution follows a deterministic sequence — but the AI reasoning at each step is 100% non-deterministic, producing unique analysis for every run.
Data Flows In
Enterprise procurement data — invoices, POs, contracts, cloud billing — streams into the ingestion layer. The prototype simulates this with a high-entropy synthetic data engine generating 28K+ realistic records per run.
AI Detects Anomalies
Statistical analysis identifies pricing outliers, duplicate charges, and contract violations. Each anomaly is scored for severity (Z-score deviation) and tagged with affected vendor, category, and financial impact.
LLM Reasons About Causes
Amazon Bedrock Nova Lite receives the anomaly context and performs root-cause analysis. It generates human-readable explanations, supporting evidence, and recommends specific remediation strategies.
System Decides & Acts
The Decision Engine classifies each issue as P1/P2/P3. Low-risk P3 issues are remediated autonomously. High-impact P1/P2 issues are routed to human reviewers with full context for approval.
Everything Is Audited
Every decision — from raw data to LLM reasoning to execution status — is fingerprinted with a unique RunID and stored in an immutable audit trail for compliance and post-mortem analysis.
7 Agents. One Orchestrated Pipeline.
A strictly sequential LangGraph.js state machine where every data packet flows through 7 specialized AI agents — from ingestion to autonomous remediation — in under 3 seconds.
Platform Capabilities
End-to-End Intelligence & Automation.
From detection to remediation to compliance — every capability is designed to operate autonomously while maintaining full human oversight for critical decisions.
Anomaly Detection
Identifies cost leakage patterns, duplicate charges, pricing outliers, and maverick spending against historical procurement baselines in real-time.
Detects 14+ anomaly types including duplicate invoices, price variance beyond contract terms, and unauthorized purchases.
SLA Breach Prediction
Forecasts delivery failures 14–30 days ahead using vendor history, seasonal demand, and fulfillment rate analysis.
Covers on-time delivery, quality compliance, and contractual penalty exposure across all active vendor relationships.
LLM Root-Cause Analysis
Bedrock-powered reasoning that produces human-readable explanations of why anomalies occurred and what remediation steps to take.
Each analysis includes confidence scores, supporting evidence citations, and alternative hypotheses for transparent decision-making.
Autonomous Remediation
Low-risk actions (P3) execute automatically — vendor blocks, payment holds, and contract flags — with zero human latency.
Autonomous execution reduces mean-time-to-remediate from days to seconds for low-risk, high-frequency cost leakage events.
Human-in-the-Loop Approvals
High-impact decisions (P1/P2) are routed to designated human reviewers with full context before any action is taken.
Each approval request includes the anomaly summary, financial impact, confidence score, and recommended action with one-click approve/reject.
Full Audit Trail
Every agent decision is traced end-to-end — from raw data ingestion through LLM reasoning to final execution status — for complete compliance.
Supports regulatory requirements including internal audit, external compliance reviews, and forensic investigation of any historical decision.
Financial Impact Scoring
Quantifies the exact rupee value of every detected anomaly and projects total annual savings from autonomous remediation.
Impact scores drive priority classification and help executives understand ROI in board-ready financial terms.
Real-Time Observability
Live system telemetry showing pipeline health, agent throughput, error rates, and inference latency across every execution.
Operators can monitor every pipeline run in real-time and drill down into individual agent execution traces.
Vision vs Reality
Production Vision.
Prototype Execution.
The prototype implements the complete pipeline end-to-end. Here's a detailed comparison of how each system layer maps from today's working demonstration to the full enterprise product.
Full-Scale Production System
Data Ingestion
Multi-region Kinesis streams at 50K events/sec with schema-on-read
Intelligence Layer
Fine-tuned BERT clusters with domain-specific procurement knowledge graphs
Automation
Direct SAP/Oracle ERP webhooks for contract enforcement and payment blocks
Observability
OpenTelemetry + Datadog APM with distributed tracing across all agents
Scale
10K+ concurrent supplier feeds, sub-100ms P99 latency, multi-AZ
Compliance
SOC 2 / ISO 27001 automated compliance with immutable audit ledger
Approvals
Enterprise workflows via Slack, Teams, Email with SLA-based escalation
Current Working Prototype — Live
Data Ingestion
DynamoDB stream simulation with 28K+ synthetic records per batch
Intelligence Layer
Amazon Bedrock (Nova Lite) with automatic Mistral Large failover
Automation
Autonomous API remediation with DynamoDB-backed approval queue
Observability
Real-time audit trace with per-agent JSON payloads in DynamoDB
Scale
Single-region serverless (ap-south-1), ~2.4s end-to-end inference
Compliance
Full JSON trace per run with RunID fingerprinting and timestamps
Approvals
Dashboard-based approve/reject with real-time DynamoDB status updates
Technology Foundation
100% Serverless. Unified TypeScript Stack.
A single language from UI rendering to AI inference. No Python microservices, no cold starts, no state management headaches. LangGraph.js orchestrates all 7 agents as a single directed acyclic graph with persistent state across every node.
LangGraph.js
Stateful multi-agent orchestration with directed acyclic graph execution
Amazon Bedrock
Nova Lite inference with automatic Mistral Large failover
DynamoDB
Serverless NoSQL — stream, audit, and approval tables
Next.js 14
App Router with server-side rendering and API routes
Framer Motion
Production-grade scroll-driven animations
TypeScript
End-to-end type safety from UI to inference layer
Why LangGraph.js
Changes Everything.
Traditional multi-agent systems lose state between agent calls. LangGraph.js maintains a persistent, typed state object that flows through every node in the pipeline — meaning the Audit agent has full visibility into what every upstream agent decided and why.
This enables features that are impossible with stateless architectures: cross-agent reasoning, automatic rollback on failure, and deterministic replay for debugging. The entire 7-agent pipeline is defined as a single TypeScript function.
State Persistence
Typed state flows through all 7 agents — zero data loss between nodes
Fault Tolerance
Automatic retry with exponential backoff and Mistral Large failover
Deterministic Replay
Any historical run can be replayed with identical inputs for debugging
Single Language
TypeScript end-to-end — no polyglot deployment complexity
Live Demonstration
Ready to Witness
Autonomy?
Launch the Simulation Lab to trigger a complete 7-agent LangGraph pipeline run on AWS Bedrock. Every execution is unique — zero hardcoded outputs, 100% real-time AI reasoning with full audit traceability.
Launch Simulation Lab