Not your grandma's
old RAG.
By combining hybrid search, graph traversal, and investigation replay, TierZero achieves significantly higher recall, precision, and accuracy vs RAG.
Learns knowledge and judgment.
Most AI tools ingest documents. TierZero captures structured knowledge and the reasoning traces behind every investigation — how your engineers diagnose, what they rule out, and why they reached their conclusion.
Multi-source capture
Incidents, Slack threads, code reviews, and post-mortems flow in automatically — no forms, no tagging.
Structured memories
Raw signals become typed records with confidence scores, tags, source attribution, and version history.
Reasoning traces
Every investigation path is captured — the branches taken, the branches ruled out, and the root cause found.
Retrieval that thinks, not just searches.
A query doesn't just match keywords. It walks the knowledge graph, expands through service dependencies, and replays past investigation trajectories to find answers the way your best engineers would.
Hybrid search
Vector, keyword, and summary indexes run in parallel, fused with reciprocal rank fusion for coverage and precision.
Graph traversal
Results expand through the knowledge graph — linked incidents, services, teams, and runbooks surface automatically.
Trajectory replay
Past investigation paths that match the current failure pattern are recalled, adapted, and applied to the new context.
The numbers don't lie.
We evaluated Context Engine across 12,847 real operational queries spanning incident triage, root cause analysis, and service dependency lookups.
Recall
Measures how effectively the system retrieves all relevant information without missing critical context. Graph traversal surfaces related memories that keyword and embedding search alone would miss.
Precision
Measures how well the system filters noise and returns only relevant results. Confidence scoring and contextual ranking reduce false positives that dilute answer quality.
Accuracy
Measures how often the system produces correct, contextually appropriate answers from retrieved information. Investigation replay and relationship-aware retrieval ground answers in real operational history.
Black boxes have no place in production.
Nothing. A thumbs up/down button. Maybe a confidence score with no explanation. You have no idea what the system knows, what it’s missing, or why it gave a particular answer.
Everything. Every memory is a structured record with type, source, confidence score, tags, linked services, and full version history.
You don’t even know the AI used a bad memory. Something wrong slips into the context, poisons the answer, and you have no way to trace it, fix it, or prevent it from happening again.
Tell the AI directly, or edit it in Context Engine. Every memory is a record you can inspect, update, or delete — with full version history and audit trail.
A pile of chunks. Documents get split, embedded, and dumped into a vector store. No structure, no relationships, no memory of past investigations. Every query starts from scratch.
A knowledge graph that compounds. Every investigation adds structured memories and the reasoning traces that connect them.
Not another agent memory that rots.
Every memory is visible, searchable, and editable. And every investigation makes the system smarter.
See everything
Search by keyword, service, team, or time range. Results ranked by relevance, recency, and confidence score.
Editable records
Review, correct, or delete any memory.
Survives team turnover
People might leave, but knowledge always stays.
Version history & audit trail
Every change is tracked with full provenance.
See how TierZero can help
Context Engine captures, structures, and surfaces your team's tribal knowledge.