TeemLush
Help AI understand undocumented NetSuite customizations.
TeemLush captures application context from supported NetSuite environments and makes it available to compatible AI agents. Agents can investigate records, configuration, workflows, scripts and reports alongside existing task knowledge—even when documentation is incomplete or the original consultants are unavailable.
- NetSuite customizations
- Application context for AI
- Records, scripts & workflows
Where does the knowledge live when the implementation team has moved on?
Part of an implementation’s knowledge remains in the system: custom record structures, field definitions, approval transitions, scripts, saved searches and report logic. Reading these artifacts together can reveal how a process has actually been configured.
TeemLush supplies built-in capture for NetSuite. Once the application is connected and the required access is authorized, it builds a knowledge base that agents can search and read without your team developing a separate capture integration.
Records and configuration
Investigate business objects, fields and the account-specific structure behind a task.
Workflows and scripts
Trace approval paths, calculations, integration behavior and the rules implemented in custom code.
Searches and reports
Find the filters, joins and calculation choices behind existing operational reporting.
What did FleeceTide find in the recorded NetSuite example?
In a September 2026 interaction, the user first asked what TeemLush knew about time reporting, then asked about travel. FleeceTide searched the available collections and read relevant documents and workflow definitions to explain the account’s configuration.
For travel, searches in the guide and solution collections returned no dedicated guide. FleeceTide followed captured custom records, workflow definitions, integration scripts and searches. It connected travel requests to booking-platform data, monthly employee reconciliation, expense approvals and project cost allocation.
The time-reporting inquiry also retrieved earlier operational knowledge: distinctions between actual and planned time, the appropriate source for labor-cost rates, and differences between record fields and SQL query columns. These details gave the agent account-specific guidance for interpreting the system.
Start with a business question
The user asked about time reporting and travel in ordinary language, without listing the underlying record or script identifiers.
Follow the application evidence
The agent searched across sources, read key documents and workflow definitions, and related the discovered objects to the business process.
Explain what the evidence supports
The response mapped the configured process and pointed out important reporting conventions, giving the user a basis for further investigation.
This anonymized example summarizes interactions on September 19 and 21, 2026. Many retrieved sources were captured on August 7. It demonstrates knowledge retrieval and explanation. New memory proposals remained unsaved in the recorded interaction; current system state requires checking before execution.
Why can a field work in one NetSuite interface and fail in another?
One application’s record-operation dictionary, API record schema and SQL query data dictionary can differ—even contradict one another. TeemLush captures the dictionaries in layer 2, alongside the account’s application context.
The expert library in layer 1 supplies practical consultant knowledge for navigating these differences: which dictionary to use for an operation, how to interpret conflicting definitions, and when a workaround applies. In the recorded inquiry, existing time-reporting guidance also identified a difference between a record field and the column used for SQL querying.
How do agents distinguish captured behavior from business intent?
A workflow or script can explain implemented behavior. The reason someone chose that behavior may require additional organizational context. Authorized agents and people can add explanations, terminology and policies to the application-context layer.
Agents check the source, account scope, date and version assumptions before applying knowledge. When captured information conflicts with the current application, they check the relevant live source. A search with no result leaves an unanswered question for further investigation.
What should you bring to a TeemLush discussion?
Bring one process you want your agents to understand: a report no one can explain, an approval path with undocumented changes, or a calculation tied to custom records and scripts. Tell us your NetSuite environment, existing AI agents and deployment requirements.
We can discuss the relevant capture coverage, knowledge-library scope and private deployment. Your application context and agent-built memory share one private TeemLush host. Agents connect through CLI, MCP or REST.
TeemLush for NetSuite: key facts
- Product
- Enterprise Knowledge + Team Memory for compatible AI agents.
- Application capture
- Built-in NetSuite context capture; requires application connection and authorized access.
- Private deployment
- Application context and agent-built memory share a TeemLush-hosted private cloud or on-premise server.
- Subscriptions
- Expert library subscription for layer 1; software subscription license for layers 2 and 3. Exact pricing: [email protected].
FAQ
Can AI investigate NetSuite customizations without an implementation manual?
Yes, where the relevant records, configuration, workflows, scripts and reports can be captured. TeemLush makes that evidence searchable for agents. The organization may still need to explain business intent or policy that is absent from the system.
Does a TeemLush search prove what is running in NetSuite right now?
Captured knowledge has a source date and account scope. Agents check those details and use the relevant live source when freshness or a discrepancy matters to the task. Current system state must be confirmed before execution.
Do we need FleeceTide to use TeemLush with NetSuite?
TeemLush supports compatible AI agents through CLI, MCP and REST. FleeceTide was the agent in the recorded example and provides a governed enterprise Harness + Runtime. The knowledge service can also be used by other compatible agents.
Give your agents a clearer understanding of your business systems.
Tell us which applications you run and what your agents need to accomplish. We’ll discuss capture coverage, relevant knowledge libraries, private deployment and pricing.
Keep what agents learn across sessions and teams →