HOW CRUSHBANK ORCHESTRATE IT WORKS
One lakehouse. Every source your service desk runs on.
CrushBank's proprietary ingestion layer pulls data continuously and normalizing it into a single, unified data lakehouse. Before any AI model, automation, or agent touches your data, CrushBank ensures it is clean, standardized, and structured for the type of work it needs to do.
Once ingested, data is stored in three purpose-matched formats:
Structured.
For analytics, computation, and operational reporting. How many tickets were reopened last month? What is the average resolution time for this client? These are structured questions that need structured data.
Unstructured.
The complete, original record is always preserved. Engineers can always go deeper, review the source, and verify any AI-generated output against the raw data it came from.
Vectorized.
For semantic and similarity search. When a technician asks a question in natural language, the vector layer finds the most relevant resolution history, documentation, and context — not just keyword matches.
CrushBank's orchestration layer determines which format — or combination of formats — best handles each incoming request, automatically routing it to the right search tool without requiring the user to know where the data originally lived or how it was stored.
Source system permissions are honored throughout. CrushBank leverages IBM watsonx's AI governance framework to ensure every result is traceable and auditable — giving your team and your clients the ability to trust but verify every AI-generated output.
What you can build from your data lakehouse.
Step 1 — Conversational Retrieval
Ask questions in plain language. Get answers from across your entire IT history.
With your data normalized and stored in the CrushBank lakehouse, every engineer on your team has instant access to your organization's complete institutional knowledge — without needing to know which system holds the answer or how to query it.
Ask how many times a client has called about the same issue. Ask what resolution worked last time for a specific error. Ask what documentation exists for a given environment. CrushBank's orchestration layer routes the question to the right format — structured for counts and analytics, vectorized for contextual retrieval — and returns a synthesized, accurate answer in seconds.
The practical impact: engineers spend less time searching and more time resolving. New hires get up to speed faster. Escalations drop because Level 1 technicians have access to the same depth of institutional knowledge as your most experienced engineers.
For IT Support Managers, the lakehouse becomes a real-time command center — ask which technicians are over or under their timesheet targets this week, which clients have the highest open ticket volume, where SLA breaches are trending, which engineers are resolving the most issues at Level 1, or where recurring problems are consuming disproportionate time across your team, and get a precise, data-grounded answer in seconds without pulling a single report.
Step 2 — Automations and Workflow
Build AI-driven automations grounded in your own ticket history — not generic AI inference.
Because CrushBank's automations are built on top of your normalized data lakehouse — not a generic AI model — every automated decision reflects your organization's actual patterns, client history, and operational standards. The result is automation that performs at over 90% accuracy, consistently, without the unpredictability of a model that has never seen your data.
Beyond CrushBank's built-in automations, your team can build and deploy custom automations and workflows tailored to your specific operational needs — using your normalized lakehouse data as the engine, so every automation you create is grounded in your actual ticket history, client context, and service delivery patterns from day one.
From your data lakehouse, here are some of the prebuilt automations:
- Ticket classification. Incoming tickets are automatically categorized using your historical resolution data — matching new requests to the type, category, and skill set that has resolved similar issues in the past.
- Ticket prioritization. Urgency and SLA alignment are determined automatically, based on client contract data, issue history, and real-time context — without dispatcher intervention.
- Time budgeting. Every ticket receives an AI-generated time estimate derived from how long comparable tickets have historically taken to resolve — giving dispatchers and engineers accurate expectations before work begins.
- Ticket summarization. Long ticket threads and complex issue histories are condensed into concise, structured summaries — giving the next engineer full context in seconds, not minutes.
Because automations run on your data and through IBM watsonx's governance framework, every classification, priority, and budget can be traced back to the data that produced it.
Step 3 — AI Agents
Deploy autonomous agents that monitor your operational data continuously — and act on what they find.
AI agents built on the CrushBank data lakehouse do not wait for a technician to ask a question. They run continuously against your normalized IT data — surfacing patterns, detecting problems, and triggering actions before issues escalate or go unnoticed.
Because agents operate directly on your lakehouse — not a live system of record — they can analyze the full depth of your historical and real-time operational data simultaneously, without impacting the performance of your PSA or other systems.
Beyond CrushBank's pre-built agents, your team can design and deploy custom agents trained on your own lakehouse data — defining the conditions they monitor, the thresholds that trigger action, and the workflows they initiate, so every agent you build reflects the specific operational priorities, client commitments, and service standards that matter most to your business.
Agents currently available on the CrushBank lakehouse:
- Ticket Auditor. Continuously reviews open and recently closed tickets to identify incomplete documentation, missing resolution notes, or tickets closed without a confirmed fix — ensuring your data quality stays high and your history stays trustworthy.
- Ticket Agreement Auditor. Monitors tickets against client SLA and contract data to flag breaches, at-risk agreements, and billing discrepancies before they become client escalations.
- Ticket Bundler. Identifies clusters of related open tickets — symptoms of the same underlying issue across multiple users or clients — and groups them for coordinated resolution, reducing duplicate effort and repeat escalations.
New agents can be deployed as your data lakehouse grows and your operational priorities evolve. The lakehouse is the foundation — agents are what you build on top of it.
When the answer is not in your data — search the internet without leaving your workflow.
CrushBank Orchestrate IT includes integrated internet search, allowing engineers to query the live web in natural language from inside the same interface they use to query the lakehouse. No tab switching. No copy-paste. No context lost.
When a technician encounters an error code, a vendor release note, or an unfamiliar configuration — anything not yet captured in your internal knowledge base — internet search surfaces synthesized, context-aware answers drawn from live web sources, returned in the same format as a lakehouse query.
The result is an engineer who can move fluidly between internal institutional knowledge and external technical intelligence — from a single interface, in a single workflow.
Structured.
For analytics, computation, and operational reporting. How many tickets were reopened last month? What is the average resolution time for this client? These are structured questions that need structured data.
Unstructured.
The complete, original record is always preserved. Engineers can always go deeper, review the source, and verify any AI-generated output against the raw data it came from.
Vectorized.
For semantic and similarity search. When a technician asks a question in natural language, the vector layer finds the most relevant resolution history, documentation, and context — not just keyword matches.
CrushBank's orchestration layer determines which format — or combination of formats — best handles each incoming request, automatically routing it to the right search tool without requiring the user to know where the data originally lived or how it was stored.
Source system permissions are honored throughout. CrushBank leverages IBM watsonx's AI governance framework to ensure every result is traceable and auditable — giving your team and your clients the ability to trust but verify every AI-generated output.