Not your grandma's
old RAG.
TierZero extracts investigation patterns from your operational history and replays them in-context on every request. Hybrid search, graph traversal, and trajectory replay deliver measurably higher recall, precision, and accuracy than 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.
Service dependency mapping
Maps service dependencies, team ownership, and blast radius across your infrastructure
Team language mapping
Learns your internal names, abbreviations, and slang. When someone says 'payments' or 'checkout', the agent knows they mean svc-payments-v2 and checkout-service-prod.
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.
Natural language translation
Ask in plain English. The engine resolves team slang to real services and builds the right Datadog, Grafana, or kubectl queries automatically. No PromQL or query syntax required.
MCP-native tool access
Connect custom MCPs, APIs, internal tools, and proprietary systems.
Your agent gets smarter.
When an engineer corrects the agent, that correction becomes a structured memory in the Context Engine. Next time a similar incident occurs, the agent starts from the corrected understanding, not from scratch.
Corrections are structured, not lost
Every correction becomes a versioned memory record with source attribution, confidence score, and linked services.
Patterns compound across incidents
Investigation playbooks are extracted from past resolutions and replayed when the failure pattern recurs.
Visible and auditable
Every learned memory is inspectable, editable, and deletable. No black-box retraining.
Measurably better for your environment.
This is not RAG and not fine-tuning. TierZero extracts investigation patterns from your operational history and replays them in-context on every request. The learning loop is mechanical, not magical.
Capture investigation playbooks
Every investigation records the full reasoning path: queries run, hypotheses tested, dead ends ruled out, root cause confirmed. These become reusable playbooks, not document chunks.
Track what actually helped
Memory attribution links each outcome to the specific memories that influenced it. The system learns which knowledge is foundational to your environment and which is noise.
Incorporate engineer feedback
Corrections, reactions, and direct edits to the knowledge graph take effect immediately. Every correction updates the memory the agent references next time.
Replay and adapt patterns
When a new incident matches a past failure pattern, the agent replays the successful investigation trajectory adapted to the current context. It starts from your team's best prior work, not from scratch.
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.
Every action. Every reason. Every source.
TierZero's Context Engine maintains a complete audit trail of all AI decisions. Every investigation cites its sources — specific log lines, metrics, commits, and past incidents. Evidence chains are inspectable, editable, and exportable to your SIEM.
Evidence chains with cited sources
Every conclusion links back to specific Datadog logs, GitHub commits, and past incidents
Full version history
Track every change to organizational memory with complete edit history
SIEM export
Export audit logs to Splunk, Elastic, or Sumo Logic for compliance workflows
SOC 2 Type II certified
Enterprise-grade security and compliance controls built in from day one
See how TierZero can help
Context Engine captures, structures, and surfaces your team's tribal knowledge.