Business Workflows¶

Lerim is useful when a team runs repeated AI workflows and keeps losing the context between runs.

The pattern is:

an agent completes work inside a business process
the trace contains evidence, decisions, constraints, open questions, and handoffs
Lerim extracts the reusable signal
the next agent starts with compact, cited context instead of a raw transcript

Research and market intelligence¶

Research teams can preserve source trails, evidence strength, assumptions, rejected leads, client-specific brief constraints, and analyst handoffs across agent-assisted research cycles.

Example question:

lerim answer "What sources supported our last competitor-pricing assumption?"

Support operations¶

Support teams can preserve triage decisions, escalation evidence, policy references, known fixes, product behavior, customer constraints, and next steps.

Example question:

lerim answer "What do we already know about this customer escalation pattern?"

Operations and incidents¶

Operations teams can preserve incident timelines, owner decisions, inventory exceptions, supplier or carrier constraints, unresolved risks, and runbook lessons.

Example question:

lerim answer "What risks were still open after the last carrier-delay incident?"

Security and IT¶

Security and IT teams can carry forward investigation timelines, access-review rationale, policy exceptions, remediation evidence, and internal helpdesk handoffs.

Example question:

lerim answer "What evidence supports the latest access-review exception?"

Revenue and customer workflows¶

Revenue and customer teams can reuse account context, positioning decisions, campaign constraints, legal approvals, and follow-up commitments.

Example question:

lerim answer "What account constraints should the renewal agent know before outreach?"

Engineering automation¶

Engineering teams can retain architecture decisions, failed tests, repo conventions, release lessons, and operational constraints.

Example question:

lerim answer "What release constraints did previous agents discover?"

Current source boundary¶

The open-source package includes the trace-to-context foundation, supported source adapters, and custom clean-trace folders. Customer pilots can start by choosing one workflow, cleaning its traces into Lerim canonical JSONL, and registering that folder as a custom project.

For custom agents today, the practical path is:

lerim project add ~/lerim-traces/support-clean --type custom
lerim ingest --agent custom

If the source trace contains customer-specific noise or sensitive fields, run a customer-owned cleaner before the files enter that folder. Lerim filters for durable business signal, but pre-ingest cleaning is still the right boundary for secrets, regulated data, large raw tool outputs, and retention policy.