One Platform.
Zero Surprises.

From Reactive Measures to Predictive Intelligence

We find the critical "AI Fingerprints" of failure hidden in your digital noise—isolating fraud, outages, and data breaches with forensic precision—so you can act before it's too late.

MIT Professional Education: Capstone Impact Project

Architects of Resilience.

Aegisight AI is a comprehensive predictive risk platform developed to transform disparate enterprise data into a "Single Source of Truth." Born as an MIT Capstone initiative, our methodology is built upon 6 core milestones—from framing the initial problem to pitching the final solution—designed to enable leaders to intervene surgically before a failure occurs.

Our Vision

To transition the global enterprise from reactive crisis management to Predictive Risk Orchestration. We verify the world's data through a Universal Forensic Pipeline that acts as the central nervous system of your business.

Our Mission

To identify the AI Fingerprints of failure before they manifest. We transform raw signal into Verified Signatures—providing the specific "What, Why, and When" needed to ensure operational resilience.

The Force Behind the Vision: Meet Our Leadership

With decades of expertise at MIT and beyond, we are bridging the gap between academic theory and enterprise execution.

Marek Nerko

Founder & Data Science
"We don't just predict risk; we mathematically prove its existence before it happens."

Marek Nerko

Founder & Data Science

Michaeline Albright

Marketing & Research
"Translating deep forensic signals into business strategy. Data without a narrative is just noise."

Michaeline Albright

Marketing & Research

Neelesh Pathak

Product Lead
"A product isn't just a tool; it's the interface between human decision and AI foresight."

Neelesh Pathak

Product Lead

Daniela Alvia

Legal & Marketing
"In the age of AI, legal integrity is as critical as code integrity."

Daniela Alvia

Legal & Marketing

Pramoth Chandrasekar

Head of Engineering
"Ensuring zero-latency execution in the most hostile environments."

Pramoth Chandrasekar

Head of Engineering

The Engineering Challenge: Fragmented Telemetry

Disparate Data Streams

You cannot model systemic enterprise risk using fragmented tools. IT, Finance, and Operations systems generate massive amounts of telemetry in isolation. Without a unified ingestion layer, critical cross-domain failures hide in the noise.

Opaque Anomaly Detection

Standard behavioral analytics generate too many false positives. An alert without cryptographic traceability is just noise, leaving incident response teams without actionable root causes.

Lack of Forensic Context

Generic AI models hallucinate when applied to precise enterprise workflows because they lack domain-specific ground truth. We needed a verifiable 'Physics of Failure' layer to process proprietary data securely.

Architecting the Aegisight Platform

Universal Forensic Pipeline

To solve the hallucination problem, we built a dedicated data fabric that structures and normalizes disparate telemetry before it reaches the reasoning engine.

Root-Cause Traceability

We engineered the platform to map anomalies back to the exact code commit, transaction, or configuration change, transitioning operations from correlation to causation.

Unified AI Engine

Instead of deploying fragmented agents, we centralized the analysis within a single, secure intelligence layer that evaluates IT, Finance, and Security holistically.

The Forensic Architecture

A deterministic framework that functions as a Verified Registry for your enterprise. We replace statistical guessing with 'Physics of Failure' logic. By filtering agnostic data streams through a rigorous six-stage process, we eliminate noise and codify every event into a non-repudiable forensic record.

01
INGESTION

The Universal Forensic Pipeline

Agnostic ingestion of enterprise ecosystems. Deployed securely within your infrastructure (On-Prem or Cloud), we harmonize fragmented data streams into a single, conflict-free timeline, ensuring absolute data sovereignty.

TECHNICAL SPECS Deployment: On-Prem / Cloud | Input: Agnostic Data Streams
02
DETECTION

Synthesizing Multi-Domain Tensors

Identifies the subtle precursors of risk within complex data streams. By isolating 'Silent Drift' (Latent Concept Drift), the system detects emerging failures before they escalate into critical liabilities.

TECHNICAL SPECS Status: Silent Drift Detected | Metric: Similarity > 0.75
03
VALIDATION

The Symbolic Logic Gate

Transforms probabilistic alerts into deterministic facts. A 'Hard Logic' verification layer filters out statistical noise, ensuring that only verified anomalies are promoted to a Signature.

TECHNICAL SPECS Threshold: Confidence > 85% | Method: Deterministic Hardening
04
OUTPUT

The Verified Signature

The definitive forensic record. Using Explainable AI (XAI), we generate a 'Digital Death Certificate' that codifies the Root Cause and Blast Radius with deterministic certainty.

TECHNICAL SPECS Output: Digital Death Certificate | Certainty: Deterministic
05
INTEGRATION

Adaptive Integration via API

Human-in-the-Loop Governance

We route the "Digital Death Certificate" to your command center workflows, enabling precise decisions without disrupting operations.

TECHNICAL SPECS Output: Universal API Schema | Control: Human-in-the-Loop Governance
06
CONTINUOUS LEARNING

The AI Core Lifecycle

Managed Edge Evolution

We aggregate verified signatures to retrain the core model. Updates are staged, validated, and released via strict version control, ensuring predictable behavior across all Sentinel nodes.

TECHNICAL SPECS Control: Strict Versioning | Deployment: Managed Edge Release
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