On the work of winning grants.

The funder universe is enormous and most of it is irrelevant to any given organization. The harder problem is not finding grants — it is finding the small subset of funders whose stated priorities, recent giving patterns, and organizational character would actually result in a yes.

Existing discovery tools optimize for breadth. Type "education" into a database and receive 4,000 results. The work of filtering 4,000 down to the 12 worth pursuing is left to the grant officer, performed manually, in spreadsheets. This is the wrong abstraction. The grant officer doesn't want a list — she wants a recommendation.

Mavenly's discovery engine works the way an experienced grant strategist would. It maintains a profile of your organization (mission, geography, capacity, history, recent outcomes) and matches that profile against the active funder universe — not by keyword, but by fit. It accounts for recent giving: a foundation that hasn't funded a new grantee in two years is deprioritized. It accounts for capacity match: an organization with a $400K budget should not be matched against a funder whose smallest recent grant was $5M. It accounts for narrative alignment: a funder whose recent giving emphasizes systems-change framings will not be a match for an organization whose strongest narrative is direct service.

The output is not 4,000 results. It is twelve, ranked, with an explanation of fit and an estimated probability of award.

• 1.1
  Federal, foundation, and corporate sources One unified search across the three universes that matter.
• 1.2
  AI-scored mission alignment Match scores grounded in your organization's actual narrative, not keywords.
• 1.3
  Recent-giving intelligence Funders are scored by what they've actually given to recently, not what their website says.
• 1.4
  Capacity-aware ranking Opportunities matched against your organization's grant size and reporting capacity.

The drafting workflow is where the most ground is given to generic AI tools — and where the most quality is lost. Open ChatGPT, paste a funder's RFP, and ask for a draft. The result is a competent generic proposal. It does not sound like your organization, because the model has never seen anything your organization has written. It does not address the funder's actual evaluation rubric, because no one told it what the rubric is. It is, in the most literal sense, generic.

Mavenly's Compose engine starts from a different position. It has ingested your prior winning applications, your most recent annual report, your theory of change, and your most recent program outcomes. It has separately ingested the funder's recent grant announcements, the language of their published evaluation criteria, and the tonal characteristics of the proposals they have funded. It generates a draft that sounds like your organization writing for that specific funder — not like ChatGPT writing about your organization.

The output is a draft, not a finished document. The practitioner remains the author. But the work that begins with "stare at a blank page" begins instead with "edit a credible draft" — and that single shift compresses what was a two-week effort into a two-day effort. Three to five times more applications submitted, with the same team.

• 2.1
  Voice-trained on your prior work Drafts in the tone, rhythm, and vocabulary of your previously successful applications.
• 2.2
  Funder-rubric awareness Maps every section of the draft to the funder's published evaluation criteria.
• 2.3
  Theory-of-change synthesis Constructs logic models and outcomes frameworks from your program data.
• 2.4
  Reviewer-perspective revision A second-pass review that critiques the draft as the funder's reviewer would.

For most nonprofits, the grant pipeline is the largest line item on the revenue forecast and the least visible part of the operation. Boards ask, in good faith, "what are we likely to land this quarter?" — and development teams answer with the optimism of people who have not yet had enough coffee. The forecast is wrong, the budget breaks, the executive director discovers the gap on the call with the auditor.

This is not an integrity problem. It is a tooling problem. The information needed to forecast accurately exists — historical conversion rates by funder, response timelines, board cycles, the current stage of every active grant — but it lives in spreadsheets that go stale, in email threads that nobody re-reads, in the heads of program officers who left in March. There is no instrumentation.

Mavenly's pipeline forecasting works the way a sales team forecasts revenue. Probability-weighted. Stage-based. Updated continuously. It tells you that the $500K Walmart Foundation submission has a 30% probability of landing in Q3, with a 60% probability of falling into Q4, with a baseline historical conversion rate against this funder of 40%. The board hears a number. The CFO can plan against it. The executive director knows what to escalate.

• 3.1
  Probability-weighted forecasts Revenue projections grounded in your organization's actual historical conversion rates.
• 3.2
  Stakeholder relationship tracking Every interaction with every program officer, captured automatically, surfaced when relevant.
• 3.3
  Workflow automation Deadline reminders, board cycle alignment, and approval routing without manual coordination.
• 3.4
  Executive reporting Board-ready pipeline reports that can be generated in 60 seconds, not 6 hours.

The compliance reporting burden is the silent tax on a successful grant. Every awarded grant arrives with a reporting requirement, and most organizations under-resource this work because they have to. The result, across the sector, is reports filed late, filed in the wrong format, or filed against the wrong framework. Renewals suffer. Trust erodes. The grant officer who closed the deal in March is the one writing the impact report in November, on a Saturday, against a deadline that has already slipped twice.

The reporting workflow has the cleanest structure of the four. The funder specifies a framework, the organization runs its program, and the report describes the relationship between the two. Yet generic reporting tools treat reports as document templates to be filled in — when in fact the reports are syntheses of program data that already exists somewhere in the organization.

Mavenly's reporter works differently. It maintains the funder's specific reporting framework as a structured object. It pulls from your program data — outcome measurements, beneficiary records, financial actuals — and constructs the report as a synthesis. The grant officer's job becomes review and refinement, not authorship from scratch. The reporting burden compresses from days of effort per report to hours.

• 4.1
  Funder-specific frameworks Reports drafted against the exact framework each funder requires — not generic templates.
• 4.2
  Outcomes-data integration Pulls program outcomes data directly into the report; eliminates the spreadsheet round trip.
• 4.3
  Compliance-checked drafts Validates against the funder's published reporting requirements before the report is submitted.
• 4.4
  Renewal-readiness signals Surfaces the data points most likely to influence the funder's renewal decision.