Knowledge Engine for Marketing

Turn customer signal into pipeline—not another dashboard.

Connect campaign, revenue, customer, product, and market context to sharpen audiences, coordinate launches, and show what creates pipeline.

KEP

Pipeline and campaign review

Explain the quarter’s pipeline gap by segment and recommend where marketing should adjust.

I connected campaign performance, account engagement, stage conversion, win-loss context, product usage, and targets. I’m preparing the review now.

Pipeline quality reviewReport

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.

Marketing owns the narrative and a number built across many systems.

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

Connect the context behind the work.

Agents learn the language of the work, create the data flows that connect its systems, and build a living context graph for each request.

Agents learn your language

They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.

Agents connect your systems

They create the data flows that bring the right information together for each task, without manual setup for every source.

Agents build the context graph

They connect people, records, decisions, and outcomes while preserving a clear path back to the source.

From domain language and source data to a context graphThe context model resolves jargon, synonyms, abbreviations, and intent into shared entities and relationships. Source mappings connect those concepts to tables and keys while agentic data pipelines prepare durable data. The resulting context graph can be virtualized over its sources or materialized.NATURAL-LANGUAGE REQUEST“Why is pipelinebehind target?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTMQL→qualified leadintent→buying signalintent: explain pipeline gapCanonical modelENTITIES · RELATIONSHIPSCampaignActivityDealDRIVESTOUCHESSemantic bindingsCONCEPT → SOURCE DATACampaign→HubSpotActivity→AnalyticsDeal→Salesforcetables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHHubSpotAnalyticsSalesforceCampaignsEngagementPipelineJOINCampaign contextQ2 launchCAMPAIGNMid-marketSEGMENT$1.5MGAPPartnersCHANNELVIRTUALIZEDquery sources in placeMATERIALIZEDpersist selected subgraphs
The context model interprets a domain’s vocabulary, synonyms, abbreviations, and intents, then resolves natural-language requests to shared entities and relationships. Source mappings connect those concepts to tables, columns, keys, dialects, and execution integrations. KEP can query the resulting context graph virtually over source data or materialize selected graph data.

Connected systems

Explore 1,000+ integrations →
  • Salesforce
  • HubSpot
  • Google Analytics
  • Sprinklr
  • Canva
  • Google Workspace

Workflows

Change the work, not just the interface.

01

Refine ICP and account intelligence

Combine firmographic, engagement, product, customer, pipeline, and win-loss context into prioritized audiences and account briefs.

02

Coordinate campaigns and launches

Build briefs from approved positioning and customer evidence, track cross-functional dependencies, and keep launch and campaign work aligned to the same plan.

03

Explain pipeline performance

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

From disconnected campaign data to a pipeline decision.

  1. 01

    Connect

    Gather campaign results, web and account engagement, CRM history, customer conversations, product signals, targets, and approved positioning.

  2. 02

    Analyze

    Normalize definitions, compare conversion and velocity by segment, identify performance drivers, and distinguish evidence from attribution assumptions.

  3. 03

    Deliver

    Create the pipeline-quality report, prioritized account list, campaign brief, and leadership summary from the same prepared data.

  4. 04

    Control

    Keep publishing, audience changes, CRM updates, and external communications behind the team’s review and approval policies.

What the team gets

Outcomes people can inspect and use.

ICP and account briefs

Prioritized audiences and accounts with the fit, intent, customer, and revenue evidence behind the recommendation.

Campaign and launch plan

Approved positioning, dependencies, owners, milestones, source material, and review gates in one working artifact.

Pipeline performance report

Conversion, velocity, contribution, open assumptions, and recommended changes presented in business terms.

The Accrete product

Context, work, and control in one path.

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.

Review governance →Open the Trust Center →

Start with one workflow

Define the outcome before expanding the system.

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