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Ignite AI Agents · Fundraising

Prospect research that shows its evidence.

The Prospect Research agent qualifies donors with sourced briefs built from your CRM record, integrated wealth screening, and deterministic scoring. Fundraisers start with evidence instead of a blank page, and writes back to the record wait for a person.

Proposed prospect recordPending

The exact write, shown first. Waits for your approval.

Every brief carries

Sources used
Which records and screenings informed each finding
Identity confidence
How sure the agent is it found the right person
Deterministic scores
Four dimensions from the platform’s deterministic scoring engine, each tied to its evidence
Blockers, stated plainly
What the agent could not verify, and why it stopped

From a name to a qualified prospect.

Ask in plain language. Every capability reads from your live record and returns work you can check, with the evidence behind each answer. The agent’s guided workflows run in the background while you keep working.

Evidence-backed qualification

Ask whether a constituent belongs in major gift work, even when the internal history is thin. The brief states plainly what it could not establish, and closes with one of four recommendations: qualify, cultivate, monitor, or review.

Should the Thompsons move into major gift qualification?

Deterministic scoring

Capacity, affinity, propensity, and linkage are scored by the platform, not improvised by the model. The same evidence produces the same dimension scores, and each score shows what supports it.

Why did this prospect score high on affinity?

Wealth screening in context

The agent pairs wealth screening from WealthEngine, DonorSearch, and Kindsight (formerly iWave) with your own giving history, so external capacity signals land next to the relationship you already have.

What wealth signals do we have for Maria Alvarez?

Warm-path mapping

The agent reads relationships, households, and shared history to find the warmest visible route to a prospect. It flags which relationship signals it can stand behind, and names the gaps in your record it could not fill.

What is our warmest path to Robert Kim?

Prospect discovery

Surface major gift candidates from your constituent base, or corporate and institutional prospects from sponsorship, CSR, and workplace giving signals. Candidates come back as a scored table, and the ones you keep become profiles in your research queue.

Find corporate prospects from our sponsorship and workplace giving signals.

Approval-gated writeback

When research is worth keeping, the agent proposes a prospect record for your research queue. It shows you the exact write first, and the write sits at the approval gates your team configures.

Add this prospect to our research queue.

Who reaches for it

For the people who qualify prospects.

The brief is one request away. What is worth keeping goes to the record once a person approves it.

  1. 01
    When
    A new name enters the pipeline.
    You get
    A sourced qualification brief with the four dimensions scored by the platform.
    You decide
    Qualify, cultivate, monitor, or review.
  2. 02
    When
    Someone asks who knows this person.
    You get
    The warmest visible path in, through relationships and households.
    You decide
    Which introducer to ask.
  3. 03
    When
    You want corporate or institutional prospects from sponsorship or workplace giving signals.
    You get
    A scored shortlist of organizations, with the signals that surfaced each.
    You decide
    Which ones become profiles in your research queue.

How the agent works

Gather, enrich, score, brief.

A disciplined research workflow runs behind every request. The agent follows the same sequence, and stops when the evidence is not there.

  1. 01

    Gather

    Resolves the right constituent, then pulls giving history, engagement, household, and relationship context from the live record.

  2. 02

    Enrich

    Adds integrated wealth screening once the identity match is strong. Weak matches stop the workflow, with the reason stated so a person can resolve it.

  3. 03

    Score

    Runs the platform’s deterministic scoring model across capacity, affinity, propensity, and linkage. The agent is never permitted to author or edit a score. Run it again when new giving or engagement lands, and the score recomputes from the current record.

  4. 04

    Brief

    Presents the sourced brief with recommended next steps. Prospect records it proposes wait for a person.

Four dimensions, with the work shown.

Inside the qualification brief

Same shape. No mystery.

Every brief is laid out the same way, so your team knows where to look and what to check before acting on it.

  1. 01Prospect summaryWho this is and why they surfaced.
  2. 02Identity confidenceHow sure the agent is it found the right person.
  3. 03ScorecardFour dimensions, scored by the platform.
  4. 04Evidence by dimensionEach item carries its source.
  5. 05Recommended next stepQualify, cultivate, monitor, or review, and the action that follows.
  6. 06Ask bandA range only when the evidence supports one.
  7. 07Risks and blockersWhat would change the picture.

Want to see a qualification brief built on your own data?

Book a working session

Governed by design

Built for work this sensitive.

Prospect research touches wealth data and personal context. So the hard rules live in the platform: scoring runs in a deterministic engine, and CRM writes go through the approval gates your team configures.

See the governance model

It refuses to guess

No score without a strong identity match. No ask band without at least three independent pieces of evidence, including your own CRM record. When the agent is blocked, it says why and stops.

Writes wait for a person

The write the agent proposes is a prospect record in your research queue, and it sits at the approval gates your team configures until someone says yes.

Scores it does not author

The score of record comes from the platform’s deterministic engine. The agent must present scores and evidence exactly as returned, and is never permitted to author, edit, or reclassify them.

Research you can trace

Finished research lands in the research record and the research queue with its sources and research date, beside the notes your team writes by hand.

Start narrow. Widen the gates as trust grows. The pace is your team's call.

Works from the record you already keep.

No exports, no side tool, no research doc to paste back in. The agent reads the same live record your team maintains, and finished research lands back in it.

  • Giving history
  • Engagement record
  • Households and relationships
  • Wealth screening

Available in the Intelligence Hub, embedded in constituent records, and in the chat panel inside Ignite. The research queue filters by score, recommendation, and research date.

Proven foundation

Built from the ground up on a modern, AI-first architecture.

Ignite is a new platform, built by the team nonprofits have trusted with their constituent and financial data for over 15 years.

G2 reviews

4.5/5

Average on G2.

20M+

Constituent profiles managed across the StratusLIVE platform family.

$1B+

In annual giving processed across the StratusLIVE platform family.

“A powerful tool that empowers our team members to not only enter new data but also see a historical perspective of our donors and corporate partners.”

Maria M.

Director of Investor Management

Verified review · G2

“One source of the truth about our contacts, donors, and partners that is easily kept up to date.”

Robert W.

Director of Fundraising Operations & Digital Solutions

Verified review · G2

Honest questions

The questions teams actually ask about this agent.

Straight answers on trust, control, and your data. The full security posture, providers included, is on the Trust & Security page.

Can we trust an AI agent with donor research?

Trust here is checkable rather than assumed. Every finding in a brief is cited to its source, scoring runs in the platform’s deterministic engine rather than in the model, and the agent refuses to guess: no score without a strong identity match, and no ask band without at least three independent pieces of evidence including your own CRM record.

Is this just ChatGPT pointed at our donor database?

No. A general chat tool answers from general knowledge, cannot run wealth screening, and cannot cite your records. The Prospect Research agent has one defined job inside Ignite: it reads your CRM record and integrated wealth screening, assembles a sourced brief for review, and presents the platform’s deterministic scores exactly as returned.

What happens when the agent gets something wrong?

The brief itself changes nothing in your record. It lands for review with its sources attached, so a wrong finding is visible where you read it. The one write it proposes, a prospect record for your research queue, waits at the approval gates your team configures until someone says yes.

Will our donor data be used to train AI models?

Prompts and outputs are not used to train foundation models by default, and each task routes to a model matched to the sensitivity of its data. Providers, hosting, and the full data flow are documented on the Trust and Security page for your reviewer.

I raise major gifts. What does this agent do for me before I pick up the phone?

It qualifies the name. Ask whether a constituent belongs in major gift work and you get a sourced brief: the four dimensions scored by the platform, the evidence behind each score, the warmest visible path in, and one of four recommendations. What it cannot establish, it says. You decide whether the call happens.

I manage the database. What will this agent write to my records?

Nothing on its own. Finished research lands in the research record for review. When a brief is worth keeping, the agent asks before creating a prospect record or adding it to your research queue, and that write sits at the approval gates your team configures. Scores come from the platform’s deterministic engine, not from the agent.

Watch a qualification brief come together.

Book a working session and see the Prospect Research agent read a live record, evidence and all.