Data Liberation

Your data is being
held hostage.

Years of donor records, gift histories, and relationships are locked inside a system that can barely search them, let alone understand them. We get them out, clean them up, and make them genuinely useful.

You know the feeling.

You open your CRM and search for a donor. Three records come back: “John Smith,” “J. Smith,” and “Jonathan Q. Smith.” Same person. Different data in each. One has the email, another has the giving history, the third has an address from 2019.

You need to know which gala donors haven’t given this year. That’s a 15-minute report-builder exercise if you remember which filters to stack. And the answer you get back is a spreadsheet, not an insight.

The data is valuable. The system holding it is not.

Works with the CRM you’re stuck in
BlackbaudSalesforceHubSpotDonorPerfectBloomerangLittle Green LightNeon CRM

Four verbs.

The same pipeline works for any legacy system.
The source changes. The pattern doesn’t.

1

Extract

Pull everything out.

We connect to your CRM's API and walk every record: contacts, gifts, campaigns, funds, relationships. The complete picture, preserved exactly as-is in staging tables you own. No CSV exports. No consultants. No six-month timeline.

Paginated, rate-limited, incremental. We track progress per entity type so extraction can pause and resume. Your source system stays untouched.

2

Clean

AI does the scrubbing.

Duplicate contacts get detected by embedding similarity, not just exact-match. Phone numbers normalize to E.164. Addresses standardize. Names parse into structured components. Dates become consistent. Currency converts to integers for safe math.

The hard part: deduplication. An AI reviews candidate pairs and suggests merges. Households get inferred from shared addresses. Corporate relationships surface from employer fields. Lapsed donors get flagged automatically. You confirm, the pipeline executes.

3

Embed

Data becomes knowledge.

Clean records transform into rich text documents that capture the full picture of each donor, each campaign, each relationship. These documents get chunked, embedded, and indexed. Your data stops being rows in a table and becomes something an AI can understand.

"John Smith has been a donor since 2015. Lifetime giving: $45,000 across 23 gifts. Primary fund: Annual Fund. Lapsed from monthly giving in 2024. Household includes Jane Smith. Board member's spouse." That's not a database row. That's knowledge.

4

Advise

Ask anything. Get a real answer.

Your expert knows your donor base. Not because it queried an API, but because it absorbed the full picture. Ask for lapsed major donors, campaign ROI, retention trends, re-engagement strategies. One sentence instead of fifteen clicks.

This is the end state: a conversational AI that's a genuine strategic advisor for your organization. Not a chatbot stapled to a database. An expert that understands your data deeply enough to surface insights you didn't know to look for.

Traditional migration vs. liberation

Before

Export CSV, hire a consultant, clean in Excel for 6 months

After

Connect once, AI cleans continuously

Before

Value comes after the migration is complete

After

Value comes as soon as data is embedded

Before

New CRM is another vendor lock-in

After

Your data lives in Postgres and Pinecone, both portable

Before

Querying means learning a new UI

After

Querying is a conversation

Before

Relationships are whatever the schema supports

After

Relationships are inferred by AI from actual data

Before

The system stores your data

After

The system understands your data

The thesis

The right response to a bad CRM isn’t a better CRM. It’s a system that understands the data well enough to advise on it.

Your CRM becomes a read-only source that feeds an intelligent layer. New data entry happens in Portable. The sync job pulls deltas to keep knowledge current. Eventually, the intelligent layer is the source of truth.

No “migration day” with downtime and crossed fingers. No six-month consulting engagement. You connect, we extract, AI cleans, and the expert starts advising. Value on day one.

What it sounds like when your data is free

Y

Who are our lapsed major donors?

You have 14 donors who gave $5,000+ in the past three years but haven't given in the last 18 months. The largest lapsed gift was $25,000 from the Morrison Family Foundation, last gift dated March 2024 to the Annual Fund. Want me to draft a re-engagement strategy for each?

Y

How did the spring gala compare to last year?

This year's gala raised $142,000 from 89 donors, up 12% from last year's $127,000 from 74 donors. Average gift size dropped slightly ($1,596 vs. $1,716), but you gained 15 new donors. The Morrison table alone accounted for $35,000. Your donor acquisition cost was effectively zero since the event was sponsor-funded.

Y

Which campaigns should we retire?

Three campaigns haven't received a gift in over two years: Legacy Circle (last gift 2023-11), Young Alumni Fund (last gift 2024-01), and the Building Expansion drive (last gift 2023-08, $4,200 raised against a $500,000 goal). I'd recommend retiring Building Expansion and folding Legacy Circle into your planned giving messaging.

Good, healthy, living data.

The best CRM is the one that understands your data,
not the one that stores it.