AI can reason. But it can't follow your rate card.
Revenue operations runs on logic that lives in people's heads: amended floors, custom deal structures, trafficking rules that depend on who the client is and what was negotiated six months ago. AI tools can't encode that. We can.
The gap between your systems and your actual process
Your revenue operations stack (your ad server, OMS, project tracker, and everything around them) handles the standard cases fine. But the high-value work, the 20% that eats your team's time, runs on logic that doesn't live in any of those systems.
It lives in:
- Custom deal structures that don't map to any template in your ad server
- Amended rate floors from previous negotiations that only the account team remembers
- Trafficking rules that change based on package type, client tier, or inventory pressure
- Approval chains that vary by deal size, product mix, and relationship history
- Cross-system dependencies: a change in your OMS that should cascade to your ad server and project tracker, but the rules for how it cascades depend on context
Your team bridges this gap manually. Every day. That's the problem Enmesh solves.
Why current tools can't close this gap
Can sync fields between your ad server, OMS, and project tracker as long as the mapping is straightforward. The moment a deal has a non-standard structure, a custom rate, or an exception to the trafficking template, it breaks or does the wrong thing silently.
Can draft a trafficking plan or summarize an IO. But can't enforce that the plan respects an amended floor rate, or that the creative specs match the contracted deliverables, or that the approval chain is correct for this deal type.
Can store your rate cards, deal history, and client relationships. But knowing the rate floor exists and enforcing it at the moment of campaign setup are two different things.
Your operational logic, made executable.
Enmesh encodes the rules your revenue operations actually run on (the constraints, exceptions, approval logic, and cross-system dependencies) into a structured layer that sits between your team's decisions and your execution stack.
When a campaign moves from your OMS to your ad server to your project tracker, Enmesh ensures:
- Rate constraints are enforced, including amendments, custom floors, and negotiated exceptions. Not retrieved for reference, but enforced at the point of action.
- Trafficking rules adapt to deal structure: non-standard packages route through the right logic, not a generic template that misses the nuance.
- Cross-system changes cascade correctly: a flight change in your OMS triggers the right updates in your ad server and the right tasks in your project tracker, based on the actual rules for this specific deal.
- Approvals route dynamically based on deal size, product mix, client tier, and exception history, not a static flowchart.
The AI interprets incoming requests and unstructured inputs. The business rules are enforced structurally. Your team intervenes only where genuine human judgment is needed.
The result for your operations team
The routine 80% flows through automatically
Standard campaigns, clean IOs, and template deals move from closed-won to trafficked to tracked without manual intervention. Your team stops being a translation layer between systems.
The complex 20% gets handled, not escalated
Custom deals, amended terms, and exception-heavy campaigns are processed through your actual business rules, not a generic automation that guesses. Genuine exceptions surface with full context for human review.
Every decision is traceable
When someone asks "why was this campaign set up this way", there's an answer: "this rate constraint was checked, this approval rule applied, this trafficking logic was followed." Full audit trail.
Getting it right comes first.
We start by going deep with one operations team at a time: your actual rules, your actual edge cases, your actual systems. We'd rather build something that works than something that demos well. That depth is what makes the system worth scaling.