Always-On Root Cause Intelligence for Mission-Critical Systems

We explain why complex systems fail with audit-grade, causal AI

Data Quality, Cleansup, Lineage First, Perfected AI !

Efficient Data Cleanup with Causal Intelligence
Cost-Effective Solution

“SweepAI transformed our logs, metrics, traces into knowledge graphs, thus accelerating RCA and provided us with audit-grade evidence”

Layla Perez

Layla Perez

Causal Intelligence for Enterprise

From Reactive Troubleshooting to Autonomous Operations

SweepAI is foundation for preemtive and predictive failure detection, rosk-aware deployments, automated remediation and Compliance ready operations intelligence.

From Telemetry to Causality

Ingest data from any environment, extract semantics, build a living causal graph linking services, transactions, errors and users, explain RCA with evidence for faster resolution, predictable operations and defensible decisions.

<span>From Telemetry to Causality</span>

Cost Reduction

Save on unnecessary expenses associated with processing irrelevant data, leading to improved cost-efficiency.
Cost Reduction

Enhanced Productivity

Streamline your business operations by focusing on relevant data, increasing overall productivity.
Enhanced Productivity

Built for Secure, Governed, Mission-critical Systems

SweepAI.tech is built for systems where failure is not an option, be it Telecom & Network operations Banking & Financial services, Large scale SaaS or Healthcare and any regulated industries.

AI you can trust -MTTR, PII/PHI, Knowledge graphs, causal intelligence which is evidence proof and audit-grade.

<span>Built for Secure, Governed, Mission-critical Systems</span>
squares

"Sweep.AI has been a game-changer for us, allowing us to focus on qualified data and improve our decision-making processes."

Testimonial
Abigail Thomas

Optimize Your Data Processes with SweepAI.

SweepAI has significantly reduced our data processing costs and improved the accuracy of our data analytics. It's a must-have tool for any business dealing with data sprawl. - Anna Taylor