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AVX-CS-001 / Enterprise Operations SaaS

OrionOps Intelligence Suite

An AI operations SaaS that converts fragmented service tickets, sensor events, and technician notes into governed, real-time decision intelligence.

Client

OrionOps

Year

2026

Build

14 weeks

OrionOps Intelligence Suite

Abstract / Executive Summary

Business impact, condensed.

OrionOps needed a command layer for operational teams managing high-volume service incidents across distributed assets. Avrixo designed a multi-tenant intelligence suite that unified ticket history, telemetry, customer context, and procedural documentation into one searchable reasoning interface.

The resulting SaaS product reduced manual triage effort, improved escalation accuracy, and gave leadership a measurable view of recurring failure patterns across regions, asset classes, and support queues.

The Core Problem

The bottleneck beneath the business goal.

The client had valuable operational knowledge, but it was scattered across CRM notes, PDF runbooks, IoT event streams, and historic issue logs. Support managers relied on senior engineers to interpret incidents, which created queue bottlenecks and inconsistent resolution paths.

Existing search returned documents rather than answers. The platform needed to preserve source traceability, avoid hallucinated remediation steps, and support thousands of concurrent queries from regional teams without slowing ticket workflows.

The Technical Solution

Architecture designed for accuracy, scale, and trust.

Avrixo implemented a retrieval-augmented generation architecture with tenant-aware vector partitions, semantic chunking, document lineage metadata, and strict answer citation rules. Incoming tickets were embedded, matched against prior incidents, and enriched with asset-specific telemetry before being passed into the reasoning layer.

The engineering team added an event-driven ingestion path for new documents and sensor batches, allowing the knowledge base to update without interrupting active support sessions. A queue-backed inference layer handled burst traffic while preserving low-latency responses for critical incidents.

The UI was shaped like a research console: every recommendation surfaced evidence, confidence bands, related incidents, and suggested workflow actions so operators could act quickly without losing governance.

Architecture Notes

System decisions that made the product viable.

Tenant-isolated vector namespaces for secure retrieval boundaries.

Streaming incident enrichment pipeline with retryable workers.

Answer provenance layer mapping recommendations back to source records.

Role-based dashboards for agents, managers, and executive stakeholders.

Tech Stack Matrix

Infrastructure behind the outcome.

Discuss a similar build

Experience Layer

Next.jsReactTailwind CSSFramer Motion

AI and Retrieval

RAGVector DatabaseEmbeddingsPrompt Guardrails

Data Platform

PostgreSQLPrismaRedis QueuesObject Storage

Infrastructure

AWS RDSServerless WorkersCI/CDObservability

Metrics and Outcomes

40%

lower median triage latency

Incident summaries and recommended next actions reached agents before manual knowledge-base lookup was required.

10k/sec

event stream capacity

The ingestion architecture sustained burst telemetry without blocking support workflows.

31%

higher first-response accuracy

Cited recommendations improved alignment between agent response and historical resolution patterns.