The Checkr for the rental market. Know who will rent. Know who will stay.
Credit, or any underwriting tool, tells you who can pay. Gida tells you who will stay, through behavioral intelligence and AI — predicting demand before a unit is listed, commitment before a lease is signed, and retention after move-in.
National competition for housing-tech solving real placement problems for military and veteran families.
Chicago Booth's Startup New Venture Challenge — a national showcase of top early-stage founders.
Vetted by Estonia's startup ecosystem body as a model built to scale across borders, not just one market.
Checkr made verification infrastructure standard for hiring. Gida is building the intelligence infrastructure for rentals.
Before listing, before signing, after move-in.
Before listing
Identify likely renter demand before it reaches a listing platform: who needs housing, where, when, and what they're likely to choose.
Before signing
Score which qualified renters are most likely to commit to a specific unit — reducing dead leads, wasted showings, and vacancy days.
After move-in
Forecast who is likely to stay, renew, or churn — so owners can protect retention and anticipate the next vacancy.
Most rental tools answer a narrow question: can this applicant pay?
Owners also need to know who is about to enter the market, who will actually sign, and who will make them list again. Vacancy, failed conversion, and renter churn are behavioral problems. Credit, income, applications, and showing activity see only part of the picture — usually after the opportunity has already appeared.
- 46 million rental units are occupied in the US today.
- Median rent is $1,348 a month — roughly $16,000 a year per household.
- About 1 million tenants are evicted every year, at $3,500–$10,000 lost per case.
- Roughly half of all renters turn over every year, at about $4,000 lost per unit, per turnover.
- Eviction and churn alone are a multi-billion-dollar annual loss — before counting tenants who were never matched at all.
Three intelligence engines, one rental lifecycle.
Demand Intelligence
Finds future demand using relocation, enrollment, employment, organizational, and behavioral signals.
Commitment Intelligence
Scores likelihood to sign for a particular property or unit.
Retention Intelligence
Predicts renewal likelihood, churn risk, and renter lifetime value.
Screening checks eligibility. Listings create visibility. Property management systems record operations. Gida predicts renter behavior across the moments that determine revenue: demand, commitment, and retention. The data advantage compounds with every renter interaction, placement, renewal, churn event, and owner outcome.
Priced around outcomes, not leads or listings.
Pay nothing to use Gida
The commitment gap has historically landed hardest on the side with the least power to fix it. Tenants are never charged to search, apply, or get placed through Gida.
Confirmed placements and outcomes
Gida can price around confirmed placements and successful outcomes — not empty leads or listings.
Portfolio intelligence
Gida becomes portfolio intelligence: score demand, improve conversion, reduce vacancy, and forecast retention.
See the full pricing breakdown, including how and when each charge is billed →
Gida has shown that informed, targeted matching can move faster with less operational drag.
Gida helps property owners stop guessing who will rent, who will stay, and who will make them list again.
Tell us your biggest struggle as an operator.
Book 20 minutes with the Gida team. We want to listen first, then see if Gida can help ease that stress.
Book a demo →Whether you're a tenant on the move, an operator, or an org placing people at scale — Gida was built for you.
One engine, two very different paths in — depending on the size of the operator.
Circe runs the same underlying prediction — demand, commitment, retention — but small operators experience it as a marketplace, and large operators experience it as infrastructure licensed into their own systems.
Same engine, same automated commitment prediction, two different interfaces. A small landlord doesn't want new software to learn — they want tenants who won't flake, handed to them. A portfolio operator wants that same prediction running quietly inside the stack they already have. Neither path adds headcount as Gida grows; the AI does the work in both.
Under 50 doors
Two-minute intake, no dashboard to learn
Operator submits a unit and a one-take video from their phone. No software to configure — the intake is the entire onboarding.
Circe's demand and commitment engines do the work
The same prediction models used on the licensing side score and rank every tenant in the pipeline automatically — no human matching, no manual review.
Operator receives a short, pre-scored list
Only tenants Circe has already predicted as commitment-ready reach the operator's inbox — the output of the model, not a lead someone sourced by hand.
Billing runs on the outcome, automatically
The commitment outcome charge is calculated and invoiced by the system once a placement is confirmed — see the Pricing page for exact timing.
50–500+ doors
Operator signs a contract, not a listing
Circe is licensed across the full portfolio under a fixed-term contract, negotiated once up front.
The same engine runs inside their stack
Demand, commitment, and retention models — identical to the small-operator side — score every unit in the existing pipeline and property management system via API.
Leasing staff see scores, not a new tool
Commitment scores and renewal-risk flags surface inside the operator's own dashboard — no new inbox, no new workflow to adopt.
Operator pays the contract fee
Billed under the license term, independent of any single placement — Gida becomes infrastructure, not a lead source.
Neither path is a managed service — both are the same model, exposed through a different surface. What scales is the prediction engine, not a team doing matching by hand. See why Gida can't be copied for how demand, commitment, and retention prediction actually works.
One platform. Five personas. One broken system solved.
International intern moving for a tech internship with a hard start date and no time to tour. Circe reads her offer letter and visa approval, completes one intake, and surfaces three verified, commitment-ready options.
Sky confirmed housing in Nairobi before she left Stanford. She never set foot in Kenya before her move-in date.
First US arrival, no local network, data at risk. Her org partner creates her account and sponsors her onto the platform. Circe guides her step by step — she never navigates alone.
Placed without exposure. The org partner tracks every step from one dashboard.
PCS orders, 30 days, family of three, hard reporting date. Circe reads BAH, base proximity, and family size, then identifies commitment-ready operators near the new posting.
Family placed in 57 hours. No leave days burned on showings.
Coordinating 50+ placements at once for the people her organization sponsors. One dashboard, full visibility, bulk onboarding — no more spreadsheets and phone calls.
Every client placement tracked from intake to lease in one place.
Fifteen units and no qualified pipeline. His Gida inbox fills with tenants arriving from five countries, all Circe-verified as commitment-ready, all set to book. He pays only when a placement succeeds.
Vacancy days down. Zero showings run. Commission only on success.
0 in-person showings. Not one.
Every placement Gida has ever made was confirmed on video. The showing is dead. The video walkthrough is the contract.
Video is the tour
Operators record a walkthrough. Tenants watch it. Operators are held contractually to what appears in the video — no surprises on arrival.
Confirmed before landing
Housing locked in before the plane boards, even for first-time arrivals who have never visited the city.
72 hours or less
From Circe intake to confirmed booking. Not two to six weeks. One intake. Three commitment-ready options. One placement.
See how the pricing lines up with each path.
Nobody pays Gida a subscription to search. Operators pay for outcomes, sized to how they operate.
This is not a SaaS fee. Nothing is charged for access, listing, or search. What's charged depends entirely on which side of the market you're on.
Tenants
Tenants never pay to search, apply, get matched, or get placed. The commitment gap has historically cost tenants the most; Gida's model doesn't add to that.
- No account fee
- No application fee
- No placement fee
Small operators
A single charge tied to a confirmed placement — not a lead fee, not a tour fee, not a subscription. No cost to list inventory or receive matches.
- Nothing owed until a tenant commits
- No monthly or annual minimum
- Per-placement, not per-unit
Large / portfolio operators
Circe is licensed as infrastructure across the operator's full portfolio under a fixed-term contract — priced to the portfolio, not to any single placement.
- Fixed term, negotiated per portfolio
- Not billed per placement
- Includes dashboard access for the leasing team
The side with the most to lose shouldn't be the side footing the bill.
Traditional listing platforms charge fees regardless of outcome — tenants pay application fees to get rejected, operators pay for leads that never convert. Gida only gets paid when the thing both sides actually wanted happens: a placement that sticks. Large operators pay differently because they aren't buying individual placements — they're licensing the prediction engine itself, across hundreds of units at once.
Curious how this looks for your portfolio?
Gida is not underwriting. It is what happens before and after it.
Prediction before demand exists, and prediction of retention before a lease is even signed.
Traditional tools check whether someone can pay: credit, income, background. That work already exists, and Gida does not replace it. What Gida does is anticipate demand before it surfaces — using tenant ecosystem data, enrollment deposits, PCS orders, and offer letters, Circe identifies who is coming before they ever visit a traditional listing platform.
Then, after underwriting has already qualified someone on paper, Circe predicts what underwriting cannot see: whether they will actually commit to this unit, at this time — and whether they are likely to stay and renew.
That is the moat. Not a faster screening tool. Prediction before demand exists, and prediction of retention before a lease is even signed.
Demand engine
Reads ecosystem signals — enrollment, relocation, PCS orders, job offers — to anticipate who is coming before they ever search a listing platform.
Commitment engine
Once traditional underwriting has qualified someone, predicts whether they will actually follow through and commit to this specific unit.
Retention engine
Predicts likelihood to renew and stay, so operators know tenant lifetime value before the lease even starts, not after it ends.
These three engines run differently depending on who's using them.
A small landlord experiences this as commitment-ready tenants showing up in their inbox. A 300-door operator experiences it as scored leads and renewal-risk flags inside their own dashboard.
See the small-operator vs. licensing flow →Want the full model behind Circe?
Fund the automation of a process that already works.
We didn't build to find fit — we proved it manually. 50+ placements. $30K+ revenue. Zero code. Now we're raising to automate what already works.
What we proved before writing a line of code.
I have been every person Gida serves.
I have seen what happens when the system has no intelligence.
I have moved across the US for school, for work, and internationally. Every time, the housing process broke down. Qualified but rejected. Great listings that never matched. Endless showings that led nowhere.
As an operator, I have been ghosted. I have watched inventory sit idle. I have run showings that signaled nothing and cost everything.
I once slept in a shelter as an Ivy League student. Not because I lacked qualification, but because I had a small budget and no fast way to connect to rentals that matched it. At that shelter I met people with cars, jobs, and income. They simply had no housing.
I have also lived the other side of this failure. A landlord who would not fix what was broken. A building sold mid-lease, forcing me to find a new place on short notice. The system had no way to see that the operator had broken commitment first. It only saw a tenant who stopped paying.
My military friends describe every PCS move as dating, over and over, when an engine could simply think ahead and tell them exactly what and where to go.
See what this conviction has produced so far.
What we proved before building.
Every number below was generated manually, before writing a single line of code. That is not a prototype. That is product-market fit.
The zero-showing proof. Every placement confirmed on video. Tenants committed based on a walkthrough they watched from another country. Operators held contractually to what was shown. Zero-showing is proven behavior, not a hypothesis.
Next milestone: We proved the model manually. Now we are converting that proof into our first signed operators, 50 to 500 doors each, with active conversations already underway with a private landlord and a property management company, both closing by November 11.
Recognized on the ground, not just on paper.
Gida has been presented and tested in front of real investors, operators, and founders — at startup weekends, pitch rooms, and industry events.
See what's funding the next phase.
From Lagos to Chicago. From Seoul to San Francisco.
Gida is not a local listing platform. It is infrastructure for the corridors that global movement creates — and it works before the arrival, not after. Warm operator relationships in Lagos, Nairobi, and Accra existed before the product did.