Fourteen early-stage companies and hundreds of people attended the 84th Extra Crunch Live event on May 26.
Fifteen Extra Crunch analysts threw questions.
From automating document fraud detection and investment workflows to shrinking structural design cycles and remaking how buyers find homes, this class tells us where capital has found customer pain right now.
How AI is reshaping operations across fintech firms
One of the selectees focuses on an untold but expensive issue in finance: modified or forged documents, using computer vision and large language models to perform verification of bank statements, IDs, and income proofs at onboarding. The pitch strikes a nerve: The Federal Trade Commission last year reported more than $10 billion in losses from fraud, while lenders are caught between the demands of policymakers to lend faster and an aversion, disproportionately high among consumers, to any risk.
Another startup collapses together the patchwork stack packed into common community bank sites — CRM, BI, and sales enablement — into a single AI-powered platform. That’s important because, according to the FDIC, community banks make up the overwhelming majority of U.S. banks by count yet commonly struggle with expensive, generalist tools that were never designed for relationship banking. A dedicated layer can reduce the length of sales cycles and provide more razor-sharp visibility into a portfolio than Salesforce–Tableau–Seismic grids.
On the back end, a venture-optimized fractional CFO service will outperform jack-of-all-trades accounting firms. And with investors leaning into efficiency metrics and runway discipline, per PitchBook, more founders are seeking finance partners who can talk in the language of cohorts, net dollar retention, and dilution.
Dealmakers are in the mix, to be sure. One team is constructing an AI analyst for VC and private equity to triage inbound, automate diligence, and track portfolios. Most firms are piloting AI for deal sourcing, according to Deloitte’s private equity survey; the race will go to platforms that combine speed with explainability and compliance guardrails.
Consumer finances and trading gain fresh tools
For consumers, a kid-focused payments app that has prepaid debit cards and chore completion tied together in one place can take the guesswork out of allowance, making it a budget that’s programmable and has parental controls. As most U.S. students continue to graduate without what education advocates are calling guaranteed personal finance coursework, tools that offer low fees and a lightweight UX can go the distance in building good habits early.
And taxes are having the AI treatment, too. A platform plugs into banks and payroll providers to surface personalized tax strategy and real-time withholding insights. When you consider the fact that the tax code is thousands of pages long and that more Americans than ever before are living in mixed-income households (W-2, 1099, equity), embedded guidance inside financial apps could help eliminate expensive April surprises — assuming the models stay up to date with shifting rules.
If you are a day trader, an AI research and signal engine offers institutional-level predictions for individuals. The opportunity is clear, but so are the guardrails: the SEC has flagged “predictive data analytics” conflicts and risks in gamification. Any product in this lane will have to feature, above all else, transparent mechanics, Fort Knox disclaimers, and suitability controls if they want to garner lasting trust.
Real estate and building enter the digital age
There are several picks aiming at the cost and latency of the built world. One converts architectural drawings into code-compliant, physics-validated structural designs, and promises to reduce cycles by 10x or more — a goal commensurate with McKinsey’s observation that construction productivity has stagnated relative to other sectors for decades; abbreviating engineering reviews by weeks can mean smaller carrying costs of financing projects and faster project starts.
Another startup has an AI copilot for repeatable development, automating design documentation and cost projections for high-volume projects. In a world where owners are being hit over the head with supply chain volatility and permitting bottlenecks, standardized workflows make it possible to scale without having to increase headcount exponentially.
Data center operators also benefit. An optimization layer adjusts compute loads and cooling to maximize performance and margins. The International Energy Agency has warned that worldwide electricity usage from data centers could nearly double by 2026 due to AI workloads; milking better PUE and heat management out of current sites is no longer a nice-to-have.
How buyers and investors are managing real estate
A Dubai-based analytics platform screens residential and commercial assets in the UAE and U.S. to bring lucrative opportunities for retail investors to light. With the Dubai Land Department reporting record transaction activity in recent years, cross-border tools that standardize data quality and mitigate risk can make new markets available to all, rather than just insiders.
On the homebuying front, a “pre-market” service constructs searchable profiles of millions of off-market U.S. homes and allows buyers to join waitlists — essentially, a standing IOU of intent.
At a time when chronic shortages, tracked by the National Association of Realtors, are making it harder and more expensive for buyers to find homes, early demand signals could help sellers, agents, and builders better match supply up with where buyers actually exist.
Another is an AI-fueled brokerage that rebates as much as 1.5% of the commission — before closing — to help finance a down payment. Given that traditional commission structures are being legally challenged, and affordability is stretched, rebates (legal in most states) can be a potent wedge for first-time buyers. Freddie Mac has cited down payments as a major hurdle again and again; moving cash to the prior end of the transaction is an ingenious — if operationally complicated — solution.
Why this cohort of 14 startups matters right now
Through each of the 14, it’s practical AI, not in a regulatory way. The go-to-market motions are instructive: partner with banks instead of displace them; sell copilots to general contractors rather than demand wholesale stack replacement; embed in brokerages rather than fight the MLS. The actual hard parts remain integration and trust — SOC 2 audits, model risk management, fair lending and privacy reviews, rock-solid data lineage.
If a few of these teams make good on their promises, they would compress time-to-decision in finance, reduce soft costs in construction, and make housing liquidity just a little less unattainable. That’s the promise of this Battlefield class: less clicking, less waiting, and less darkness — for precisely what matters.
