Extracting what agents
need to know

Enmesh extracts the rules, exceptions, and decision logic that govern your workflows from the sources where they actually live: process docs, communication records, ticket histories, templates, and the people who do the work. We structure that knowledge into a decision architecture that AI agents can follow at runtime.

Why extraction is hard

The operational knowledge that governs your workflows isn't sitting in one place, in one format, ready to be structured. It's fragmented across systems, encoded in different languages, and shaped by years of practice that diverge from what's documented.

Knowledge lives in different modalities

The refund policy is in an SOP, the exception for enterprise accounts is in a Slack thread from last October, and the rule about looping in legal for mid-contract refunds only exists in three people's heads.

The same concept has different names

What the CRM calls "deal stage" might be "revenue recognition status" in Finance and "project phase" in the client portal, all referring to the same decision point with nothing linking them.

Written rules and actual practice diverge

The handbook says contracts over $50k need legal review, but in practice renewals with no term changes skip it, enterprise clients get a streamlined process, and account lead escalations go straight to senior counsel regardless of size.

Context is implicit

"Use standard pricing" means something different for a startup client than for an enterprise client in their second year of a three-year commitment. The operator knows the difference. The system doesn't.

Prioritization is invisible

Client preference says monthly reporting, internal policy says quarterly, regulatory says annual with monthly on request. Someone who's done this for three years knows which wins, but that priority logic has never been written down.

Knowledge is interconnected

A decision about which metric to use affects what data source you pull from, which affects whether you need a methodology footnote, which affects whether the account lead needs to review before sending.

And none of these exist in isolation. A single rule extracted without its downstream dependencies gives you something incomplete.

The sources we extract from

We start by ingesting your existing operational sources. The knowledge that governs your workflows typically lives across process documentation, communication records, ticket histories, templates, and the people who do the work.

Process documentation

SOPs, runbooks, internal wikis, policy handbooks, onboarding guides

The official rules: approval thresholds, escalation criteria, step-by-step procedures.

Communication records

Slack threads, email chains, meeting transcripts, shared notes

Where exceptions get granted and procedures get corrected without the documentation catching up.

Templates and forms

Client reporting templates, intake forms, output formats, dashboards

How the same concept gets represented differently depending on who it's for.

Domain expert interviews

Recorded walkthroughs with process owners and experienced operators

The priority logic, contextual triggers, and workarounds that haven't been written down anywhere.

Case and ticket histories

CRM records, support tickets, project management logs, audit trails

What actually happens versus what's supposed to happen, and how often.

Extracted knowledge on its own isn't operational. Enmesh turns it into structured decision logic through three core capabilities.

01Knowledge Workspace

Review, correct, and manage extracted decision logic.Extracted knowledge needs to be organized as explicit decision logic, not stored as flat documentation or dropped into a prompt. The workspace gives your team a human-readable interface to inspect every decision point, its governing rules, priority ordering, and downstream dependencies.

02Continuous Extraction Pipeline

Keep decision logic current as operations evolve.Rules change, templates change, new clients arrive, internal systems get swapped out. Operational knowledge isn't static, so extraction can't be a one-time event. The pipeline continuously updates your decision architecture as your operations evolve.

03Agent Context Harness

Surface the right knowledge to agents at each decision point.Structured knowledge sitting in a repository doesn't help an agent mid-execution. At each decision point, the agent needs the right subset of knowledge surfaced: which rules apply, which take priority, what context is needed, and when to escalate instead of act.