Architecture
The AI-native relationship graph is the moat.
Gratona's AI is useful because donors, gifts, sponsorships, campaigns, events, mission trips, communications, tasks, documents, and engagement live on one connected record. Grants is in private beta. AI teammates work from the same permitted facts your team does.
Tali is available. Source context, permissions, and human review remain part of the workflow.
Why bolted-on AI is not enough
Any tool can add a chat box. A chat box bolted onto a CRM cannot see the sponsorship in the workaround, the grant deadline in the spreadsheet, or the pledge in someone's inbox. It drafts from a partial view, cannot cite its sources, and has no review workflow between its output and your donors.
Gratona is built the other way around: the CRM, fundraising workflows, sponsorships, documents, tasks, and communications live on one connected graph. Tali can cite the facts behind prepared work, and your team reviews donor-facing or external action.
The moat is not AI. The moat is what the AI can stand on.
What lives on the graph
Current fundraising records are first-class and connected. Events and mission trips are available on this model. Grants is in private beta, with access and workflow coverage confirmed directly.
Donor
Person, household, or organization, with giving history, engagement, and relationships. The central record every other object connects to.
Gift
Every transaction tied to its donor, designation, campaign, and tax receipt. The system of record finance and your board report from.
Sponsorship
Commitment, sponsor to recipient, message thread, and status: the depth of Gratona Sponsorships, first-class on the graph.
Recipient
Beneficiary record, child, missionary, project, or community, with status and media that ground sponsor stewardship.
Grant
Private betaPrivate-beta funder, deadline, award, outcome, and reporting context connected to the relationship behind it.
Event
Registration, attendee, table-host, ticket, gift, auction, check-in, communication, and follow-up context connected to the donor record.
Campaign
Appeals, match pools, peer-to-peer, and recurring asks, with every gift, pledge, and follow-up attributed back to the donor.
Communication
Emails, messages, and mailings logged to the relationship, so the next touch knows what the last one said.
Task
Owned follow-up with due dates, review states, and outcomes: the Work Graph layer where context becomes assigned work.
Document
Receipts, waivers, grant reports, and field stories, versioned and searchable, with the source attached for every reviewed action.
Engagement
Opens, portal logins, replies, and attendance signals logged to the donor record, so nothing about the relationship is lost.
AI activity
Every Tali summary, draft, and recommendation recorded with its source context, reviewer, and outcome on the same audit trail.
Why one graph beats six syncs
A stitched stack holds the same data in six places and trusts sync jobs to keep them honest. One graph holds it once, so context, reporting, permissions, and AI all read from the same record.
Tali cites the records behind every recommendation: a follow-up draft grounded in the actual gifts, conversations, and tasks on that donor record.
Board-ready reports compose across donors, gifts, sponsorships, and campaigns without exporting and merging spreadsheets.
Compliance and finance trust one source of truth: every action lands in one audit history, not scattered across systems.
Events are available on the same connected graph. Grants is in private beta, so access and current workflow coverage are confirmed directly.
Multi-entity coordination extends the same shared records, permissions, and reporting model across a custom-scoped Network plan.
Migration off a scattered stack happens once: several systems collapse into one platform, with mapping, not forever.
What makes AI safe to help
The relationship graph is the context layer. The Work Graph is the execution layer on top of it: tasks, owners, due dates, approvals, source context, outcomes, and audit history. Together they give AI teammates the grounding and the guardrails to help with real development work.
Source-backed AI
Tali only works from permitted records on the graph, and available source references stay attached: the gift history, the last meeting note, or the campaign deadline. Your team can verify the context before acting.
Permissions
Donor, gift, sponsorship, task, and communication data follow the same identifiers and access rules. People and AI teammates only see what the role allows.
Human-in-the-loop review
AI-prepared work moves through review states. Your team edits, approves, or sends back, and nothing reaches a donor without approval. Every decision is logged.
Outcomes and audit
What was drafted, approved, revised, and sent is recorded on the same record, so the next recommendation starts from what actually happened, and auditors see one trail.
Explore Gratona by fundraising goal
See the graph and the work it powers, live
Book a demo and we'll walk through both layers: the donor record with everything connected to it, and how Tali prepares reviewed, source-backed work your team approves.