Fine-tuned on YOUR technical documentation. Private, on-premise, under your control.
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Runbooks scattered across wikis. Error codes buried in Slack threads. The one engineer who knows how that legacy system works just left. Every incident becomes an archeological dig through documentation.
What if AI could answer: "Why is this deployment failing in prod but works in staging?" — and reference your exact network topology, deployment scripts, and the ticket from three months ago when someone hit the same issue?
We deploy a private LLM inside your environment and fine-tune it on your runbooks, error code databases, architecture diagrams, deployment procedures, and resolved tickets. It learns your systems the way a senior engineer would — then answers questions 24/7.
ChatGPT knows generic best practices. This AI knows your runbooks, your error codes, your deployment checklists, your network architecture. When asked about error 0x80040e14, it references the exact database timeout configuration you documented last quarter.
Knows your production topology differs from staging. Understands why deployments fail at 3am. References your specific firewall rules, load balancer configs, and certificate chains. Suggests fixes that actually work in your environment.
Fine-tuned on resolved incidents. When you hit an error, it searches similar historical cases and suggests what worked before. "Three months ago, Jenkins had the same issue — fixed by increasing heap size to 4GB per the runbook."
Runs on-premise or in your VPC. Never touches the internet. Your runbooks, topology diagrams, and incident data stay inside your network. Fine-tuning happens on your infrastructure. Zero data leakage.
Query via Slack, Teams, or CLI. Pull live data from monitoring systems. Reference current configs from Git. Can trigger approved automation workflows for known fixes. Works with your existing incident response process.
As you resolve new issues and update runbooks, the AI learns. Monthly fine-tuning cycles keep it current with your evolving infrastructure. Your knowledge base becomes increasingly intelligent over time.
25 years of infrastructure troubleshooting across thousands of environments. We know what documentation actually matters. We know which error codes are red herrings and which indicate real problems. We've built the runbooks, resolved the incidents, and written the post-mortems.
When we fine-tune an AI on your technical docs, we're not just feeding text into a model. We're applying two decades of operational experience to structure your knowledge in a way AI can actually use for troubleshooting.
"Why does this app deploy to staging but fail in prod?" — AI checks your deployment runbook, compares environment configs, identifies the missing DNS record that only exists in staging.
"AD authentication works for everyone except finance users." — AI references your group policy docs, finds the conflicting OU settings you applied during the last security audit.
"What does error 0x800f0922 mean?" — AI pulls from your documented cases: "Windows Update failure, usually certificate trust issue, check KB5011543 remediation steps on page 47 of your patch management guide."
"Why is the database slow on Tuesdays?" — AI correlates with your maintenance schedule: "Backup job runs 2am-4am, reindexing runs 3am-5am, overlap causes lock contention per incident #2847."
"How do I provision a VPN certificate for remote workers?" — AI walks through your exact procedure, references the specific certificate template, includes the PowerShell snippet from your automation repo.
On-call engineer at 2am: "Server X is unreachable." — AI references your infrastructure map, identifies it's behind the firewall that gets patched on weekends, suggests checking firewall logs first.
ChatGPT knows the internet. Our AI knows YOUR environment. We fine-tune on your runbooks, error codes, deployment procedures, network topology, and historical tickets. When you ask why a deployment failed, it references your specific application architecture, not generic best practices. Plus it runs entirely on-premise — your data never leaves your network.
We typically ingest runbooks, deployment procedures, configuration documentation, error code databases, architecture diagrams, resolved tickets, and monitoring alert definitions. Everything stays in your environment under your control. We never train on your data for other customers. The fine-tuning process happens entirely on your infrastructure.
Both. For known issues with documented procedures, it can suggest exact remediation steps from your runbooks. With approval, it can integrate with your automation tools to execute fixes (restart service, clear cache, reset credentials, etc). For novel issues, it provides diagnostic guidance and suggests investigations based on similar historical cases. You always control what level of automation is allowed.
Typical deployment is 2-4 weeks: Week 1 is data ingestion and initial fine-tuning, Week 2 is pilot testing with your team, Weeks 3-4 are refinement and production rollout. You'll see value in the pilot phase as the AI starts answering real questions from your docs. Continuous improvement happens monthly as we retrain on new documentation and resolved tickets.
The entire system runs on-premise or in your private cloud. Nothing touches the internet. Fine-tuning happens locally using your compute resources. We support air-gapped deployments for ITAR/classified environments. All data stays under your access controls. The AI inherits the same security posture as your existing documentation systems. We can deploy in HIPAA, SOC 2, and FedRAMP environments.
AI in a Box starts under $7K for hardware and base deployment. AI troubleshooting fine-tuning is typically $15K-$30K depending on documentation volume and integration complexity. Monthly retraining and support runs $2K-$5K/month. Compare that to the cost of one midnight troubleshooting session with your entire ops team, or one deployment rollback because nobody knew about the prod-only dependency.
Request a demo with your actual runbooks and error scenarios. We'll show you what AI fine-tuned on your documentation can do.
Every runbook. Every resolved ticket. Every "oh yeah, I remember that happened once" conversation. Captured, indexed, and available 24/7 to your entire team.