Case study
Private Self-Hosted AI Teammate for DevOps and PM
Built a self-hosted AI teammate that turns scattered engineering logs, tickets, docs, and meetings into instant, private answers — with zero data ever leaving the organization's network.
Overview
As engineering operations scaled, DevOps teams faced growing difficulty managing operational knowledge scattered across logs, tickets, documentation, and meetings — and public AI tools were off the table, since nothing internal was allowed to leave the network.
The Challenge
Engineers had to manually search multiple systems to reconstruct the context behind an infrastructure issue, which slowed incident resolution and wore down productivity. Public AI assistants couldn't even be evaluated, since sending internal logs or tickets to a third-party API wasn't something security would sign off on.
The business impact:
- Incident diagnosis dragged on while engineers pieced together context from disconnected sources by hand.
- Meeting decisions and discussion context routinely got lost the moment the call ended.
- Institutional knowledge lived in individual engineers' heads, not in anything searchable.
Approach
How do you get AI help without sending data outside your network?
Public AI tools were disqualified by policy before they could even be evaluated. We engineered a secure, self-hosted AI knowledge assistant using open models (Llama 3, vLLM, Ollama) running entirely on private infrastructure, so nothing ever leaves the organization's own network.
How do you unify logs, tickets, docs, and meetings into one searchable source?
Operational knowledge was scattered across four different systems with no shared index. We built automated ingestion pipelines pulling from server logs, documentation, issue tickets, and meeting transcripts (via Zoom and Microsoft Teams), indexing everything with semantic embeddings in a vector database, orchestrated with Apache Airflow.
How do engineers get an answer, not just a search result?
A pile of indexed documents doesn't help an engineer mid-incident who needs a direct answer. We built a Retrieval-Augmented Generation (RAG) pipeline behind a conversational interface, plus recurring daily and weekly summaries, so engineers and PMs get contextual answers grounded in the organization's own operational history.
How do you catch deployment risk before it reaches production, not after?
Risk signals lived scattered across logs and metrics with nobody synthesizing them before release. We extended the platform with a signal-driven deployment decision engine — Build → Signals → Risk Score → Decision → Deploy/Block — that aggregates logs and metrics into a weighted risk score and automatically gates releases that cross the risk threshold.
How do you find the signal in thousands of daily logs and alerts?
Real incidents got buried under alert volume that was mostly noise, and engineers spent incident time filtering signal from noise instead of fixing the actual problem. We added a decision intelligence layer — Events → Signals → Patterns → Decisions — that ranks signals and detects patterns to surface critical anomalies first, cutting the time to a working diagnosis.
Tech Stack
Outcome
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