Deterministic scoring plus LLM investigation. Autonomous resolution on the majority of findings. Blumira's 24/7 SecOps team on the rest. The AI story your clients already want to hear, backed by 8 years of detection data.
Kindling is Blumira's AI findings intelligence engine. It combines deterministic math scoring with LLM-based investigation to autonomously resolve security findings where the evidence is clear and to escalate findings that require human judgment to Blumira's 24/7 SecOps team. The autonomous resolution rate is held to a 90% standard. Kindling is included in standard MSP pricing at no additional charge. For MSP partners, Kindling is both an operational improvement (less triage work, fewer false alarms in client environments) and a sales differentiator. Read How MSPs Win Security Deals They Couldn't Before for the full pitch structure built around AI-powered threat detection as the lead message.
Deterministic scoring and LLM investigation work in sequence. The combination is what makes autonomous resolution reliable.
Math-based scoring runs against every finding: correlation across sources, severity weighting, organizational signature matching, and a 14-day behavioral baseline per client environment.
This is the first filter. It's fast, deterministic, and reproducible. Findings that score clearly benign resolve here. Findings that score clearly malicious promote to the investigation stage.
Ambiguous findings go to the LLM investigation layer, where context is gathered across related events, endpoint telemetry, identity activity, and 8 years of Blumira detection corpus for comparable cases.
The LLM produces an explainable verdict with reasoning, recommended actions, and escalation notes for the SecOps team if human judgment is required.
The 90% autonomous resolution rate means your team sees the findings that need human attention, rather than drowning in the ones that didn't.
Clients want to hear about AI-powered threat detection. Kindling is the capability you can describe honestly, with specific architecture and specific outcomes.
Clients experience fewer false alarms and faster response on real threats. That experience compounds over renewal cycles.
Blumira has been running detection engineering since 2018. Kindling reasons against that 8-year detection corpus plus a 14-day behavioral baseline per client environment. Context is what makes autonomous resolution reliable instead of reckless.
Competing AI-SOC vendors with newer detection stacks do not have equivalent historical context yet. The data depth is time-locked. It cannot be acquired quickly.
Start with a Free NFR license. Deploy in hours. Watch autonomous findings resolution on real telemetry.