Root cause in minutes.Not war rooms.
Nforgent Ops is an AI SRE platform for enterprise operations. Its agents investigate incidents across applications, Kubernetes, cloud and on-prem infrastructure, and the observability tools you already use. They test competing hypotheses, connect evidence across systems and recent changes, and deliver an evidence-backed root cause with a governed remediation plan your team can approve.
Runs in your environment · Bring your own LLM · Every action policy-gated and audited
The operations gap
Your stack got more observable. Incidents didn't get shorter.
Modern enterprises run thousands of services across clusters, clouds, and vendors. Every tool emits signals; none of them explain the incident. Engineers still stitch together dashboards, logs, and change history by hand, under pressure, while customers wait.
Signal overload
One failure fans out into hundreds of alerts across teams, hiding the trigger.
Context in silos
Evidence lives in a dozen tools. Correlation happens in people's heads and war-room calls.
Change velocity
Incidents often follow a change. Few teams can connect a symptom to the deploy or config that caused it.
Governance drag
Even with a fix in hand, approvals and change control slow every action during a Sev-1.
AI can now reason over operational data at machine speed. Nforgent Ops turns that capability into a governed operations teammate.
Agentic investigation
An investigator that never sleeps, and always shows its work.
Every investigation follows the evidence: parallel hypotheses, change correlation, cross-signal proof, and a remediation plan with a full audit trail.
Step 1 of 6: One symptom, forty alerts.
01 · Signal
One symptom, forty alerts.
Latency and errors spike on the payments API, and alerts fire across services and teams. Nforgent Ops opens an investigation automatically.
02 · Hypotheses
Every plausible cause, tested in parallel.
Agents branch across Kubernetes, database, cache, network, dependencies, and recent changes, collecting evidence for and against each one.
03 · Change intelligence
The change that lines up with the symptom.
A deploy landed eleven minutes before onset, and its config diff cut the database pool limit from 50 to 10.
04 · Evidence
Proof across logs, metrics, and traces.
Traces show requests waiting on a database connection, logs report pool exhaustion, and the database engine itself is healthy. Four branches are ruled out.
05 · Root cause
Connection-pool exhaustion from config in v2.14.0.
The evidence converges on a single cause, with a confidence label and every supporting signal attached.
- High confidence: 4 corroborating signals, 4 hypotheses ruled out
06 · Remediation
A fix your team approves, with the receipts.
Restore the pool limit or roll back the config, verify p99 and error rate recover, add a policy check on pool-size changes, and attach the evidence trail to the incident.
Autopilot runs pre-approved actions · Over-the-Shoulder pauses at every gate · Consultant delivers a guided runbook
Scenario is illustrative.
The platform
One AI operations layer across your entire stack.
Autonomous investigation & RCA
Agents open an investigation the moment an alert fires, test hypotheses in parallel, and converge on an evidence-backed root cause.
Observability correlation
Logs, metrics, traces, and events unified on one incident timeline, with service topology and ownership context.
Change intelligence
Deploys, config changes, and infrastructure drift correlated with symptom onset.
Kubernetes & cloud operations
Deep context for clusters, workloads, nodes, networking, and AWS / Azure / GCP control planes.
FinOps cost diagnosis
Spend anomalies traced to the owning service and misconfiguration, with low-risk fixes and forecast savings.
Human-in-the-loop remediation
Three operating modes.
- Autopilot runs pre-approved actions only.
- Over-the-Shoulder pauses for approval at every gate.
- Consultant produces a guided runbook with no execution.
Guardrails & audit
Policy gates, RBAC, approvals, and an immutable decision trail for every recommendation and action.
Operational memory
Evidence, decisions, and outcomes feed a retrieval-backed memory (RAG) that sharpens future investigations.
Design targets · early access
- First evidence-backed hypothesis in under 5 minutes
- 100% of agent actions policy-gated
- Customer-hosted by default: telemetry stays in your perimeter
FinOps agent
Cost is an operational signal.
Nforgent Ops treats spend anomalies like incidents: detect, attribute, explain, fix, and prevent recurrence with policy.
Illustrative example
An internal service is exposed through an external load balancer.
Detect
Spend anomaly: load balancer hours and data processing above baseline.
Attribute
Mapped to
namespace: payments-internal→Service type=LoadBalancer→ owning team.Explain
External scheme on internal-only traffic, plus cross-AZ data transfer amplification.
Recommend + guardrail
Switch to an internal LB / ClusterIP, preserve listeners, validate endpoints. A policy blocks external LBs in internal namespaces unless approved.
Forecast
Savings estimate with its assumptions attached.
Illustrative estimate: $1.4k–$2.1k / month
- Load balancer hours and processed data at list pricing
- Current traffic volume and cross-AZ share held constant
- Actual savings depend on provider and contract
Built for every enterprise
Wherever uptime is revenue.
Financial services
Example investigation
Payment authorization latency spikes after a config rollout. Agents tie the symptom to the change, propose a rollback, and produce an evidence trail for change-control review.
- payments
- core banking APIs
- change review
Retail & e-commerce
Example investigation
Checkout errors during a peak-traffic event. Agents isolate a saturated cache tier from noisy autoscaling alerts, recommend a scoped fix, then flag post-peak overprovisioning spend.
- checkout
- CDN / cache
- peak scaling
SaaS & technology
Example investigation
A deploy regression hits one tenant tier. Agents correlate traces with the release, identify the noisy-neighbor path, and stage a canary rollback for approval.
- multi-tenant
- CI/CD
- SLOs
Telecommunications
Example investigation
CCS / charging request timeouts across Diameter (CCR/CCA), REST charging APIs, and Kafka event streams. Agents trace the cross-interface symptoms to worker-node network degradation in the 5G core platform.
- CCS / OCS
- 5G core
- Diameter · Kafka
Healthcare
Example investigation
Clinical integration queues back up and HL7/FHIR interface latency climbs. Agents locate the failing dependency while running fully inside the provider's environment.
- HL7 / FHIR
- integration engines
- customer-hosted
Manufacturing & logistics
Example investigation
Telemetry ingestion from plants and warehouses lags. Agents trace consumer lag in the streaming backbone to a partition imbalance and recommend a safe rebalance.
- IoT / edge
- Kafka / MQTT
- WMS
Public sector
Example investigation
Citizen-facing services on sovereign or air-gapped infrastructure. Agents run with a self-hosted LLM and no outbound data path.
- sovereign cloud
- air-gapped
- self-hosted LLM
Integrations
Works with the stack you already run.
- Kubernetes
- AWS
- Microsoft Azure
- Google Cloud
- OpenShift
- Prometheus
- Grafana
- Datadog
- OpenTelemetry
- Elastic
- Splunk
- PagerDuty
- ServiceNow
- Jira
- Slack
- Microsoft Teams
- Kafka
- PostgreSQL
- Redis
- MCP-based connections
- REST / webhooks
Connector availability varies during early access. Trademarks belong to their respective owners.
Security & trust
Enterprise-grade control, by design.
Deployed in your environment
Customer-hosted by default: VPC, on-prem, sovereign cloud, or air-gapped. Your telemetry stays inside your perimeter.
Bring your own model
Self-hosted LLMs supported, or connect enterprise providers with your own keys and logging controls.
Read-only by default
Agents investigate with least-privilege access. Write actions require an explicit policy and approval.
Policy gates, RBAC & approvals
Role-based access for every user and agent. Every proposed change carries scope, risk, policy reason, and expiry, approved once or per session.
Immutable audit trail
Evidence, reasoning, approvals, and actions recorded immutably for post-incident review and compliance teams.
Product preview
Built for the people on call.
Roadmap
Now onboarding design partners.
- 01Delivered
Core platform
Incident model, policy gates, approvals, audit trail, baseline integrations
- 02Now
Design partners & controlled pilots
- 03
Hardened beta
- 04
General availability
Timeline subject to change.
Design partner benefits
- Direct line to the founding team
- Influence on roadmap and connectors
- Private deployment in your environment
- Early-access commercial terms
See Nforgent Ops on your incidents.
Private demos available. Customer-hosted by default.
Enterprises & design partners
Investigation walkthrough on a scenario from your stack, deployment and security review, pilot scoping.
Investors
Product narrative and deck, roadmap and differentiation, private demo.