Meet Justin

I'm Justin Steele, Kindora's co-founder and CEO. Before the product and the numbers, you should know who is asking for your trust.

I got here partly because programs aimed at kids like me worked. The National Society of Black Engineers helped make me an engineer. Management Leadership for Tomorrow helped carry me to Harvard for an MBA and MPA. I know what well-aimed opportunity feels like from the receiving end, and I've spent my career trying to aim it better: Bain/Bridgespan consultant, Year Up deputy director, then ten years at Google.org directing about $700M in philanthropy across the Americas. Today I serve as a trustee of The San Francisco Foundation. My wife Sally and I co-founded the family camping nonprofit she leads, Outdoorithm Collective, where Kindora was born. Funder, grantee, builder, beneficiary. I'm a mixed-race Black man who has sat in every seat at this table.

Google eliminated my role in 2024, after a decade. I've written publicly about that season and what it taught me: a mission that lives inside someone else's structure is only as safe as that structure's incentives. So this time I built the structure first. Kindora is that decision, incorporated. A Public Benefit Corporation with its mission locked in the charter, raising from the community it serves, on terms that can return your capital without ever demanding we sell.

I wrote the code, designed the data layer across 260K+ foundations, closed the first enterprise contracts, and answered the support tickets. That is the bet at the center of this company: AI has collapsed the cost of building, so the people closest to the problems can finally build the tools their fields need. Kindora has one full-time person today. This raise funds the team. But what one person built is your proof that the bet works.

Someone is going to prove there's a different way to build and own technology for the social sector. I've sat on every side of that problem. Here's where it started.

Why Kindora exists

It started late one night in our Oakland living room, after the kids were asleep. I was hunting for funders for the family camping nonprofit my wife and I run. We had about $33K in the bank and needed $9K for tent heaters. The platform I was paying thousands a year for had just handed me 2,400 funder "matches" that would have taken a year to sort through. I had spent ten years directing philanthropy at Google — and I could not find nine thousand dollars.

So I built an AI that reasons like a program officer instead of a search engine. In its first test, 90% of those matches vanished as false positives. Near the top of what remained was a foundation run by a professor I had known for years but had never connected to our work. Within 24 hours we had a meeting with its president. Other nonprofit leaders saw it and asked the same question: can I use this? Kindora is the answer.

The problem

Americans gave $617 billion to charity last year — a record. Getting to your share of it fails in two places.

First, you can't see the money. Most private foundations report they only fund organizations they have already chosen; about one in ten even has a website; the public grant records that do exist typically arrive eighteen months to three years late. And the fastest-growing pool isn't public at all — donor-advised funds now hold $328 billion and moved $64.6 billion out last year, disclosed only in aggregate across 1,512 sponsor filings covering 3.59 million accounts, not one of which names the donor behind a gift. You can research a foundation. You cannot research a DAF.

Then, you can't reach it. Money moves through relationship networks, and those networks are sorted by geography, race, class, and institutional power. For most nonprofits the capital is already somewhere inside their own network — they can't see who holds it, and even when they can, they have no credible way in. Proximity to the problem and proximity to the money are almost never the same thing.

The tools that could bridge either gap — AI grant platforms at $299 to $999 a month, donor-intelligence subscriptions starting around $4.5K and running to tens of thousands a year — cost more than most of these organizations can spend on anything. That's the gap Kindora was built to close: visibility into who holds the money, then a credible path to them.

What we built

Kindora does the work of a development office. Ask for an outcome in plain language and Kindora returns structured, sourced results: ranked funder matches, drafted letters of intent grounded in each funder's actual giving, warm paths from your board to the people who write checks, and prioritized donor lists with outreach plans. Every result cites its sources.

Proof from two real customers:

• For Outdoorithm Collective, the nonprofit my wife and I co-founded, one grant opportunity that used to take about 40 hours went from discovery to submission in about 90 minutes. We raised $100K in our first year from the prospecting list Kindora created and the application drafts it provided to us.

• In a $32K paid engagement for an unaffiliated Bay Area education fund, Kindora analyzed 33,762 donor contacts and evaluated 1,210 institutional funders, rating 110 a good or ideal fit, to produce a board-ready cultivation strategy.

We are now building the funder side of the same graph, so foundations can find and diligence the organizations doing the work they fund. The same data layer serves both ends of the transaction.

The best diligence is to use it yourself: kindora.co is free to start.

Search is the wedge, not the moat

AI search is commoditizing, and anyone can put a basic chat box on a grants database. What is defensible today is the live data layer (260K+ foundations across the US, Canada, Europe and Australia, 9.6M grant records, 55K+ open opportunities including 1,500+ open federal and state government grants refreshed daily, and 90K+ funder sites monitored and re-crawled on a rolling schedule) and the workflow that carries a nonprofit from discovery through submission. That data layer is increasingly a product in its own right: third parties can reach it through metered API access, so the asset that powers the app is also becoming a business other builders pay for. What compounds from here is outcome data: organizations draft with us, then tell us which applications were funded and which were declined, so we learn what wins, where, and why. That exists in no public filing, and nonprofits rarely move once their history lives in one place.

Traction

As of this writing:

• 2,100+ organizations have joined Kindora; 118 pay directly. $482 spent on paid ads to date (LinkedIn boost experiments), and no paid channel drives growth.

• Recurring revenue annualizes to ~$105K: $79,404 direct self-serve, $24,120 from two signed enterprise contracts, and one $2,000 self-serve sponsor purchase. Services are counted separately.

• Month-end recurring revenue grew from $403 in December to $6,617 at the end of August. That is 16x in eight months, unannualized, from payment-verified subscriptions.

• $42K of donor-intelligence engagements at 75 to 80% gross margin, contracted through True Steele, our founder's consultancy, while the line moves into Kindora PBC. These are one-time engagements, so they sit outside the recurring figure.

• Anthropic featured Kindora as a customer story. Forbes covered us in June. AWS published our Social Entrepreneur Accelerator story in August, which also puts on the record that Deloitte is providing strategic and operational support as we scale.

• Funder-pays is proven three ways: 20 grantee seats with a major Bay Area philanthropy (annual prepay, reconciled in June), 30 fellow organizations with a national fellowship funder (signed in July, prepaid in August), and a family foundation that bought a grantee license through self-serve checkout in August. 51 seats committed in total, and all three contracts are paid.

How we make money

Funder-pays is the spine. Kindora acquires nonprofits free and self-serve, then monetizes at scale by selling sponsored access to the funders who already back them, and one contract onboards 20 to 80 nonprofits at once.

Three lines are live today, and we're honestly running a horse race between them. Self-serve subscriptions at $25 to $199 a month for nonprofits and $399 a month for consultants are the volume line and the funnel. Funder-pays enterprise contracts, currently $10K to $40K a year, are ahead today — three funders are paying, and the payer brings the users with them. Metered agent and data access at published per-lookup pricing is the newest and smallest line, near-zero marginal cost, and deliberately excluded from our operating model and our milestones: it's a door we haven't opened, not a number we're asking you to underwrite. We're pushing hard on two of the three and expect to know which is biggest inside eighteen months.

One economic note worth stating plainly: we sell credits, not seats. As inference gets cheaper, credit prices hold while cost-to-serve falls, so cheaper AI widens margins instead of compressing them.

Donor-intelligence engagements at $10K to $50K were a learning engine that funded product discovery: time-boxed, one-time, and never summed into recurring revenue.

The market underneath it is 1.9 million US nonprofits, roughly 80% of them priced out of professional fundraising tools by our estimate, plus about 22,000 foundations that grant $1M or more a year. That is the US alone. Our data layer already covers funders in Canada, the UK, Europe, and Australia, and about one in eight of the paying customers who list a country are outside the US, with no international marketing behind them. Our plan counts none of that. Two hundred foundation contracts at a $25K average is $5M of ARR and puts Kindora in front of roughly 10,000 nonprofits. That's the shape of this business: it gets large by reaching tens of thousands of small organizations, not by charging them more.

Our plan is built to reach $10M or more of recurring revenue by 2030, driven by free-tier distribution, customers moving up-market as they grow, and funder-pays cohorts. That is a projection, not a guarantee; our base case and every assumption behind it are in the pitch deck's appendix and the data room.

Why now

AI changed what one person can build, and AI marketplaces changed how software reaches customers. Kindora ranked #5 in Claude's connector directory at launch, and our MCP infrastructure has handled more than 155,000 external calls since April, now running over 1,000 a day, and connectors are now live in both the Claude and ChatGPT directories. Until recently those calls were pure top-of-funnel: usage we convert to paid accounts on kindora.co. We have begun monetizing them directly. Third-party developers can now reach our data layer through metered API access, priced per lookup, which makes the data itself a product and not just the engine behind the app. To be precise about what that means: API revenue is still nascent, brand-new as of this writing, but the direction is deliberate.

Why a community round

A grant funds a program; equity funds ownership. Last September I sat at my desk late at night, fifteen clicks deep into Vanguard's withdrawal screens, and moved $100,000 out of our retirement to keep going. I had spent a decade directing philanthropy at Google.org, yet when I went looking for $350K to build technology for the sector I had served, its capital stack had no instrument for a company like this. VCs and private equity came knocking instead. The first believe-in-you check came from Camelback Ventures, a $50K SAFE.

This round is the resolution of that story. Mission tech has been stuck between venture capital that needs unicorn economics and grants that rarely fund durable products. Kindora is a Delaware public benefit corporation built for nonprofits and funders, and this round invites that community to own a piece of the infrastructure it uses.

If you're an investor who lives in the venture world: there's a direct lane for accredited investors and funds, on the same terms as everyone else. Same paper, same caps. And the cap table stays clean: Wefunder pools community investors into a single SPV that sits as one line on it, so a future priced round — if we ever choose one — looks no different than any other startup's. If you've ever thought there should be more than one way to build and own a company like this, this round is how that gets built. We'd be glad to have you.

If your giving runs through a donor-advised fund: Most people don't know a DAF can invest, not just grant. It can. Charitable dollars can hold the same agreement as every other investor in this round, through your sponsor if it allows direct investments, or through an intermediary nonprofit that can do it from any sponsor, at any size. Returns on those dollars never come back to you personally; they flow back to charity, including your own DAF, so your charitable capital gets to work twice. The mechanics vary by sponsor, so if this is your lane, ask me through the Q&A and I'll map the exact path for yours.

Where I've said this out loud

If you'd rather hear it than read it, these are the conversations where the case for Kindora got tested by people who know the sector.

1. People in Common with Jama Adams (September 2026). Seventy minutes with the person who helped grow the Giving Pledge, on conviction, belonging, why Google's 1% was a founder's letter and not a law, and why this round is open to anyone. The most complete version of the story I've told anywhere.
2. The Do One Better Podcast with Alberto Lidji (August 2026). Why better information won't change what funders fund, and why trust is the layer we're building next.
3. Forbes: Grants fund programs. Ownership funds the future. (June 2026). Why the social sector should own the AI tools being built for it.
4. Anthropic customer story (August 2026). How one person built the platform with Claude Code, and what the case study leaves out.

The terms, in plain English

We are raising on a standard post-money SAFE — the same agreement most startup investors already know — with one added feature: a capped revenue-share right. The first $1M invested on Wefunder receives a $10M valuation cap, the same terms as our anchor investors; investments after that are at a $12M cap. A post-money SAFE fixes your ownership at signing: $10,000 at the $10M cap is 0.1% of the company, and other investors in this round do not dilute that. A later priced round or option pool does.

Most startup investments have one way to pay you. This one has two. The SAFE converts per its terms in a future priced financing and carries the liquidity-event rights stated in the contract. Separately, once Kindora passes $1M in annual recurring revenue, 3% of adjusted gross revenue is distributed quarterly, pro rata across the investor pool, until the pool has collectively received 1.5x its investment. That is a second possible path to a return, not a promised yield: for scale, at $5M of annual revenue the whole pool would receive $150K that year, subject to the annual caps described in the Agreement.

Which path pays depends on which future arrives. If we raise a priced round or are acquired, the SAFE is your return. If we succeed by staying independent — our stated plan — the revenue share is your return, and the SAFE isn't dead paper: from year three on, any investor can choose to convert it into shares at their cap, trading any remaining revenue-share payments for real equity in that independent company. Neither path nor its timing is guaranteed — and if Kindora never passes $1M ARR, the revenue share simply never activates, leaving the SAFE as your only path to a return. The final contracts and the Form C, not this summary, control the terms.

If we raise, you convert to equity at your cap. If we're acquired, you're paid out at your cap. If we stay independent past $1M ARR, 3% of gross revenue repays the pool to 1.5× — equity intact. Not a guarantee.

Why a $10M cap

Fair question at ~$105K of recurring revenue. For context, SAFE caps at this stage are priced by stage and raise size, not revenue multiples: Carta's 2025 data puts the median valuation cap for rounds raising $250K to $1M at $10M, mostly for pre-revenue companies. That makes this cap the market median for a raise our size, and roughly half the median of a priced AI seed round. The cap prices what has already been de-risked, not today's subscriptions: a production product the company owns outright, the live data layer, 2,100+ organizations and 118 direct payers acquired with no paid acquisition channel, two signed funder contracts plus a self-serve sponsor purchase, and distribution inside both Claude and ChatGPT. What remains unproven is repeatable selling, and that is exactly what this raise funds. The first $1M also invests at the same cap as our anchors; the community does not pay a premium over the insiders.

Use of proceeds

On a $1M raise, Wefunder's 7% fee leaves roughly $930K before legal, accounting, and other offering costs; the Form C will show the final numbers. The core product and data layer are live, so we are not raising to find out whether the product can be built. This capital funds go-to-market: roughly 44% to two BD/AE hires and an account manager, roughly 24% to the cost of serving customers (AI inference, data pipelines, hosting), roughly 23% to founder and operations, and roughly 10% to legal, compliance, and buffer, plus working capital to serve enterprise contracts well. Product stays founder-led, so the modeled plan needs no engineering hire. And to answer the question every founder gets asked here: yes, operations includes a modest salary for me, so this company has a full-time founder.

Put plainly, this raise is how Kindora stops running on one person.

Team

I built the core product, and the company owns its intellectual property. My co-founder Karibu Nyaggah, a Harvard MBA sectionmate who led AI transformation for the Meta Ops team, has moved to a strategic advisor role and advises on strategy, go-to-market, and operations. Karibu and I first met in 2003, at Harvard Business School's Summer Ventures program, a week-long immersion designed to increase diversity in business leadership.

Data stewardship

Our customers trust us with donor data. Every organization's data is isolated to its own tenant. We do not pool customer data across organizations, and we do not train shared models on it.

If you invest

Expect quarterly investor updates, an annual financial summary, and our annual public benefit report as a public benefit corporation.

The risks, honestly

Company-specific first:

• Free-to-paid conversion is still being proven at scale.

• Claude is an important marketing channel we do not control.

• Our grant-outcome data needs time to mature.

• The team is currently concentrated in me.

• If AI simply helps everyone submit more, funders get buried and the sector is worse off. We built the brake before the accelerator: Kindora tells an organization when it isn't a fit and suppresses low-fit matches instead of maximizing applications. Our bet is that the problem was visibility rather than volume, and we will be measuring how funders actually respond as usage grows.

Structural:

• This is patient, long-horizon, illiquid capital. There is no secondary market.

• A return depends on revenue growth or a liquidity event that may not occur.

• Invest only what you can comfortably afford to lose.

And one risk is a choice rather than an accident. Our first check came from a venture fund, and venture investors are in this round. What we've built away from is dependency on traditional high-growth venture capital — growth funded by revenue, no Series A on the roadmap unless scale genuinely requires one. Know that a crowdfunded cap table can narrow traditional financing options later. We accepted that trade for independence and community ownership. Weigh it before you invest.

What I can tell you is that the problem is real, I have lived it, and early customers show that nonprofits and foundations will pay. The product is built. We're raising to sell it.

Other Helpful Resources

Anthropic Customer Story

Forbes - Ownership Funds The Future

AWS Social Impact - Kindora and the Social Entrepreneur Accelerator

People in Common Podcast - Coding Belonging with Justin Steele