Your agents handle 10,000+ cases monthly. Legacy CRM integrations are brittle. You need headless architecture that won't lock you in.
Integrate AI case routing and knowledge retrieval into existing CRM systems using Python SDKs and REST APIs. Connect to BMC telemetry feeds, train custom models on historical cases, and deploy headless architecture that avoids vendor lock-in while reducing agent handle time.
Black-box platforms force you into proprietary workflows and make it impossible to customize AI behavior when models misclassify server failure modes or storage issues. You cannot retrain, tweak prompts, or migrate if the vendor pivots.
Agent copilots need BMC telemetry, warranty databases, and parts inventory in real time. Your current CRM pulls from six systems across IPMI endpoints, Oracle databases, and custom SAP tables with no unified API layer.
Your contact center stack runs on SOAP APIs from 2012 that break with every schema update. Adding AI-powered triage or knowledge retrieval requires rebuilding entire integration layers or living with data sync failures.
Bruviti provides REST APIs and Python SDKs that connect to your existing CRM, ticketing system, and telemetry infrastructure without requiring platform migration. Ingest BMC logs via IPMI endpoints, train case classification models on historical Salesforce or ServiceNow data, and deploy AI-assisted triage that routes server failures, storage alerts, and cooling issues to the right teams with diagnostic context already attached.
The platform exposes all AI model training pipelines as code. Use TypeScript or Python to customize how the system interprets RAID controller logs, correlates PUE anomalies with thermal events, or recommends part replacements based on failure rate data from your own warranty database. Deploy on your infrastructure, retain data sovereignty, and swap components when better models emerge. No proprietary runtimes. No vendor APIs for basic retrieval-augmented generation.
Autonomous case classification parses BMC alerts, correlates IPMI telemetry, and routes drive failures, power anomalies, and thermal issues to specialized teams with pre-attached diagnostic context.
Generate instant case summaries from email threads, chat logs, and call transcripts so agents understand server failure history, RAID rebuild attempts, and past RMA requests without manual review.
AI analyzes drive failure modes, server age, warranty coverage, and replacement part costs to recommend whether to RMA, replace in field, or defer based on customer SLA requirements.
Data center OEMs manage hyperscale customers running thousands of servers with 99.99% availability targets where every minute of unplanned downtime costs six figures. Agents need instant access to BMC logs, IPMI sensor data, RAID controller status, and thermal profiles to diagnose drive failures, memory errors, or cooling anomalies before issuing RMAs or dispatching technicians.
Integrate AI case routing with existing IPMI monitoring infrastructure and warranty databases. Parse BMC telemetry streams to auto-classify server failures by subsystem, correlate power supply alerts with historical failure patterns from your parts database, and surface recommended actions based on similar cases from the past 12 months. Deploy using Python SDKs that connect to SAP for parts availability, Oracle for contract entitlements, and custom data lakes storing thermal trend data.
Bruviti provides official SDKs in Python and TypeScript with full support for model training pipelines, data ingestion, and API orchestration. REST APIs support any language that can make HTTP calls. All integrations use standard OAuth 2.0 authentication and JSON payloads with OpenAPI documentation for code generation.
Yes. Bruviti exposes model training as code using standard ML frameworks. Ingest your historical case data, warranty claims, and BMC telemetry logs to fine-tune classification models on your equipment's specific failure signatures. Models remain in your infrastructure with no data sent to external training services.
Bruviti uses headless architecture with API-first design, open model training pipelines, and standard data formats. You own the trained models, control where they run, and can export all configuration as code. No proprietary runtimes or black-box AI services that trap your investment.
Connect via standard IPMI endpoints or vendor-specific APIs like iDRAC, iLO, or IMM. Bruviti parses sensor data, event logs, and hardware inventory without requiring agents on servers. Deploy collector services in your network that stream telemetry to the AI platform using webhook or message queue patterns.
Typical deployment spans 6-8 weeks including CRM integration, historical data ingestion, model training on past cases, agent pilot rollout, and production cutover. Start with one high-volume case type to validate accuracy, then expand to additional workflows incrementally using the same API infrastructure.
Understanding and optimizing the issue resolution curve.
Part 1: The transformation of IT support with AI.
Part 2: Implementing AI in IT support.
Talk to our technical team about API architecture, SDK capabilities, and custom model training for data center support workflows.
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