Agents learn your language
They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.
Knowledge Engine for Marketing
Connect campaign, revenue, customer, product, and market context to sharpen audiences, coordinate launches, and show what creates pipeline.
Pipeline and campaign review
I connected campaign performance, account engagement, stage conversion, win-loss context, product usage, and targets. I’m preparing the review now.
58%
lower token cost
when using Knowledge Engine
2x
more tasks
vs. frontier models
32%
more accurate with open source
vs frontier model
19 of 34
tacit-knowledge facts captured
vs. RAG’s 1
Source: “Knowledge Engine Platform for Long-Horizon Professional Agents” and “Context Graphs for Recovering Tacit Organizational Knowledge”, Accrete AI, August 2026.
Pipeline quality depends on who marketing reaches, what message resonates, how sales follows up, what customers say, and where accounts convert or stall. Those signals arrive in different tools with different definitions and reporting cycles.
Marketing cognitive fabric
Agents learn the language of the work, create the data flows that connect its systems, and build a living context graph for each request.
They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.
They create the data flows that bring the right information together for each task, without manual setup for every source.
They connect people, records, decisions, and outcomes while preserving a clear path back to the source.
Connected systems
Explore 1,000+ integrations →Workflows
01
Combine firmographic, engagement, product, customer, pipeline, and win-loss context into prioritized audiences and account briefs.
02
Build briefs from approved positioning and customer evidence, track cross-functional dependencies, and keep launch and campaign work aligned to the same plan.
03
Join campaign and revenue data, analyze conversion and velocity, and generate a leadership-ready view of what changed and where action is needed.
One end-to-end example
01
Gather campaign results, web and account engagement, CRM history, customer conversations, product signals, targets, and approved positioning.
02
Normalize definitions, compare conversion and velocity by segment, identify performance drivers, and distinguish evidence from attribution assumptions.
03
Create the pipeline-quality report, prioritized account list, campaign brief, and leadership summary from the same prepared data.
04
Keep publishing, audience changes, CRM updates, and external communications behind the team’s review and approval policies.
What the team gets
Prioritized audiences and accounts with the fit, intent, customer, and revenue evidence behind the recommendation.
Approved positioning, dependencies, owners, milestones, source material, and review gates in one working artifact.
Conversion, velocity, contribution, open assumptions, and recommended changes presented in business terms.
Context that compounds
Teams should not have to pause the work to document every lesson. The decisions they review, correct, approve, and reuse can make the next workflow better informed.

01
Audience response, creative performance, sales feedback, stage movement, and customer outcomes reveal which assumptions held.
02
Relate those signals to positioning, accounts, campaigns, conversations, pipeline, and product activity.
03
Teams can reuse the evidence behind prior audience, message, and investment decisions instead of restarting the debate.
Source records remain the authority. The Knowledge Engine keeps extracted evidence and inferred relationships distinguishable so people can inspect what the organization recorded and what the system derived.
The Accrete product
The Knowledge Engine connects research, campaign work, customer evidence, revenue analysis, and finished artifacts without pretending that a single attribution model is the whole customer journey.
Agent Lab and Custom Skills
Refresh performance data, explain material changes, and prepare the next review from the team’s approved definitions.
Preserve the brief structure, required evidence, owners, review gates, and reporting format for future launches.
Start with one workflow
Bring the workflow, its information sources, its approval requirements, and the result your team needs. We’ll map a focused first deployment and the path to reuse.
Discuss this workflow