Proximitty growth playbook

Agree one accurate statement for each claim and use it everywhere. SOC 2: give the report type, the audit period and how a bank can get the report (for example under NDA through the Trust Center). If an audit is still in progress, say so plainly with the expected date, and change the badge to match. Go-live: pick one number with a definition, such as "sandbox in week 1, production by week 3", and present the faster testimonial as an example, not the promise. Then add a short "Vendor due diligence" block to the Compliance & Governance page listing what a bank can request (the SOC 2 report, GLBA safeguards summary, model cards, validation artifacts, examiner support) with one request button that goes to a founder.

Community banks and credit unions run a third-party risk review before any pilot. Consistent, specific claims answer the reviewer's first questions before they're asked. Competitors already set the bar: Aloan and EnFi both state SOC 2 Type II plainly, and Aloan links to a Trust Center.

[ ] One SOC 2 sentence (type, period, how to request) on homepage, /security and Trust Center
[ ] Badge text matches that sentence
[ ] GLBA badge explained in one line
[ ] One go-live definition (sandbox / production) on homepage, /customer, /company
[ ] Testimonial shown as an example, not the promise
[ ] Vendor due diligence block with request button
[ ] Documents listed: SOC 2 report, GLBA safeguards summary, model cards, validation artifacts, examiner support
[ ] Date each page was last reviewed

• A slower, accurate timeline feels less exciting

  Lead with sandbox in week 1 and keep the one-week testimonial as an example.

• Wording that still overstates the audit status

  Have the auditor or a compliance adviser read the final SOC 2 sentence.

• Your websiteHomepage: H1 "The AI operating system for commercial lending"; a "SOC 2 Type II Certified" badge and a GLBA badge next to "Trusted by credit unions and banks across the US"; "Integrate Proximitty with any banking system in 3 weeks" (Week 1 Integration, Week 2 Sandbox Testing, Week 3 Deploy) on the same page as "Most integrations go live in under two weeks."; Request Demo is the only call to action.Source
• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source
• Your websiteCustomer page: H1 "Trusted by FDIC-insured commercial banks and fintechs"; "From top-20 banks to high-growth fintechs"; one testimonial, from an unnamed "SVP Credit Risk, Fintech Lender": "They built agents running on our internal systems within a week without any engineering involvement from our side"; "Most integrations go live in under two weeks"; no named customers or case studies.Source
• Competitor researchAloan (AI commercial loan underwriting for community banks and credit unions) names five bank and credit union customers, shows 5.0 reviews across Gartner Peer Insights, G2, Capterra and SourceForge, publishes comparison pages against nCino, Abrigo, Baker Hill and manual spreading plus buyer's guides, states a 2-4 week go-live, and lists SOC 2 Type II with a Trust Center.See the research Source

Build four pages: /products/document-ingestion, /products/credit-analysis, /products/servicing and /products/custom-loan-ops. Each covers the problem in the lender's words, the agent's workflow, which cores and loan systems it runs in, one sourced result, an FAQ and a demo button. Generate sitemap.xml and robots.txt from the Next.js app and submit them in Search Console.

Buyers search by job to be done, not by platform name. Separate pages rank for those searches and give sales a link to send after each call.

• Thin pages that repeat the homepage

  Give each page one specific workflow and one sourced result.

• Your websiteSite structure (checked November 10, 2026): the four products (Document Ingestion, Credit Analysis, Servicing, Custom Loan Ops) are anchors on the homepage (/#products-0 to /#products-3), not pages; /sitemap.xml and /robots.txt return 404; the Blog page ("Follow our journey in building the most intelligent system in banking") lists no posts.Source
• Your websiteIntegrations on the homepage: Fiserv DNA and Premier, FIS Horizon and IBS, Jack Henry SilverLake and CIF 20/20, Finastra Phoenix, Temenos Transact, CSI NuPoint and Oracle FLEXCUBE, plus nCino, Abrigo and Moody's; browser agents work on existing systems without APIs; Custom Loan Ops builds agents from standard operating procedures or screen recordings.Source
• Competitor researchAloan (AI commercial loan underwriting for community banks and credit unions) names five bank and credit union customers, shows 5.0 reviews across Gartner Peer Insights, G2, Capterra and SourceForge, publishes comparison pages against nCino, Abrigo, Baker Hill and manual spreading plus buyer's guides, states a 2-4 week go-live, and lists SOC 2 Type II with a Trust Center.See the research Source

Add a one-line footnote under each number: the type and size of institution, the period, and how it was measured (for example "servicing staff before and after, $5B fintech, Q2 2026"). Drop or soften any number that can't be sourced. Link each footnote to the case study (idea 4) once it is live.

A sourced number survives a credit committee and a forwarded email; an unsourced one invites doubt. EnFi pairs its numbers with specific tasks, such as "45 minutes per annual review, down from 8 hours".

• Numbers look smaller once qualified

  Credible beats big for bank buyers; keep the best sourced one first.

• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Competitor researchEnFi ("The AI credit platform for commercial lenders") leads with a named bank CEO testimonial (over $300 million in new loans in 12 months with existing headcount), five customer logos, task metrics such as "45 minutes per annual review, down from 8 hours", and SOC 2 Type II and ISO/IEC 27001.See the research Source

Write a one-page case study: the lender type and size, the servicing workload before, what the agents took over, the go-live time, staff and originations after, and what the model-risk or compliance team asked. Publish it anonymised if the customer can't be named, with a quote from the SVP's role. Do a second one for the $2B regional bank.

Bank buyers want to see an institution like theirs succeed first. A specific before-and-after with a credit officer's quote does more than a list of features.

• Customer won't be named

  Anonymise by type and size; offer a reference call instead.

• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteCustomer page: H1 "Trusted by FDIC-insured commercial banks and fintechs"; "From top-20 banks to high-growth fintechs"; one testimonial, from an unnamed "SVP Credit Risk, Fintech Lender": "They built agents running on our internal systems within a week without any engineering involvement from our side"; "Most integrations go live in under two weeks"; no named customers or case studies.Source
• Competitor researchEnFi ("The AI credit platform for commercial lenders") leads with a named bank CEO testimonial (over $300 million in new loans in 12 months with existing headcount), five customer logos, task metrics such as "45 minutes per annual review, down from 8 hours", and SOC 2 Type II and ISO/IEC 27001.See the research Source

Write three fair pages: Proximitty and nCino, Proximitty and Abrigo, Proximitty and Moody's. Frame them as "works alongside" where that's true (agents that act inside the system of record). Cover what each does, where the agent fits, integration effort, governance and a switching or add-on path. Add a fourth page on manual spreading in spreadsheets.

Comparison searches come from buyers who already have budget and a shortlist. A fair page also gives champions inside the bank something to forward.

• Annoying a system you integrate with

  Use "works with" framing for nCino and Abrigo, not attack pages.

• #2 Give each of the four products its own page, and publish a sitemap

• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• Competitor researchAloan (AI commercial loan underwriting for community banks and credit unions) names five bank and credit union customers, shows 5.0 reviews across Gartner Peer Insights, G2, Capterra and SourceForge, publishes comparison pages against nCino, Abrigo, Baker Hill and manual spreading plus buyer's guides, states a 2-4 week go-live, and lists SOC 2 Type II with a Trust Center.See the research Source
• Your websiteSite structure (checked November 10, 2026): the four products (Document Ingestion, Credit Analysis, Servicing, Custom Loan Ops) are anchors on the homepage (/#products-0 to /#products-3), not pages; /sitemap.xml and /robots.txt return 404; the Blog page ("Follow our journey in building the most intelligent system in banking") lists no posts.Source

Give the hero video a lightweight poster image so the page paints at once, load the video only after it does, and serve images in modern formats at their display size. Fix the one colour-contrast issue at the same time. Re-run PageSpeed and watch the field LCP trend.

A faster first paint keeps the slowing trend from becoming a failing grade, and makes a better first impression on prospects at their desks.

• Poster looks static

  Use the video's strongest frame.

• PageSpeedGoogle PageSpeed (November 10, 2026): mobile score 38, desktop 57; in the lab the largest element, the hero video, appears at 25.0 s on mobile; the homepage downloads 11.8 MB, 7.0 MB of it images, with about 6.6 MB of image savings and 143 KB of unused JavaScript; real Chrome visitors pass Core Web Vitals (75th percentile LCP 1.6 s, INP 111 ms, CLS 0), with LCP 11% slower than the previous period; one colour-contrast accessibility issue.Source

Publish three practical posts: how Proximitty documents each agent for model risk review (what a model card contains), what an examiner sees in an agent's audit trail, and how a browser agent works in a core with no API, step by step. Write them for the CRO, the CISO and the credit officer, not for investors. Then post one a month.

These are the questions that slow AI pilots in banks. Answering them in public saves sales calls and gives champions a link to send to their risk team.

• No time to keep posting

  Turn each diligence answer into a post; hide the Blog link until there are three.

• Your websiteSite structure (checked November 10, 2026): the four products (Document Ingestion, Credit Analysis, Servicing, Custom Loan Ops) are anchors on the homepage (/#products-0 to /#products-3), not pages; /sitemap.xml and /robots.txt return 404; the Blog page ("Follow our journey in building the most intelligent system in banking") lists no posts.Source
• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source
• Your websiteIntegrations on the homepage: Fiserv DNA and Premier, FIS Horizon and IBS, Jack Henry SilverLake and CIF 20/20, Finastra Phoenix, Temenos Transact, CSI NuPoint and Oracle FLEXCUBE, plus nCino, Abrigo and Moody's; browser agents work on existing systems without APIs; Custom Loan Ops builds agents from standard operating procedures or screen recordings.Source

Define one pilot: for example, servicing and covenant monitoring for up to 200 commercial loans, or spreading for one quarter's annual reviews, in the bank's own core, for a fixed fee credited against year one. Publish the scope, the success measure (hours saved, loans per FTE, exceptions caught) and the exit terms on a /pilot page, and send it after every scoping call.

Banks approve small, bounded, reversible spends faster than open-ended platform deals, and a paid pilot filters out tyre-kickers for a team with no spare hours.

• Pilots that never convert

  Agree the conversion terms before kickoff.

• #1 Make the SOC 2 and go-live claims match on every page, and put the proof one click away

• Your websiteHomepage: H1 "The AI operating system for commercial lending"; a "SOC 2 Type II Certified" badge and a GLBA badge next to "Trusted by credit unions and banks across the US"; "Integrate Proximitty with any banking system in 3 weeks" (Week 1 Integration, Week 2 Sandbox Testing, Week 3 Deploy) on the same page as "Most integrations go live in under two weeks."; Request Demo is the only call to action.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source
• Your websiteCompany page: mission "to make commercial lending as seamless as possible to power America's small-medium businesses"; serves community and regional banks, credit unions and non-bank lenders; "System of action, not system of record"; hiring "engineers, lending experts, and forward-deployed specialists"; demos start with a 30-minute scoping call.Source

Create a page for each core: which screens the agent works in, what it reads and writes, how access is granted and logged, typical time to sandbox, and one deployment example on that core. Link each from the product pages and use them in outreach to institutions known to run that core.

It turns the hardest objection into the first proof point, and it captures searches that pair a core name with a task, where few AI vendors have a page.

• Implying an official partnership

  Say "works in", not "partner of", unless a partnership exists.

• #2 Give each of the four products its own page, and publish a sitemap

• Your websiteIntegrations on the homepage: Fiserv DNA and Premier, FIS Horizon and IBS, Jack Henry SilverLake and CIF 20/20, Finastra Phoenix, Temenos Transact, CSI NuPoint and Oracle FLEXCUBE, plus nCino, Abrigo and Moody's; browser agents work on existing systems without APIs; Custom Loan Ops builds agents from standard operating procedures or screen recordings.Source
• Your websiteSite structure (checked November 10, 2026): the four products (Document Ingestion, Credit Analysis, Servicing, Custom Loan Ops) are anchors on the homepage (/#products-0 to /#products-3), not pages; /sitemap.xml and /robots.txt return 404; the Blog page ("Follow our journey in building the most intelligent system in banking") lists no posts.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source

Pull the public call report data, filter for institutions with a commercial and CRE book in the right range (for example $200M-$5B of commercial loans), add their core where known, and rank by fit. Write a three-touch founder sequence that opens with the institution's own loan count and the loans-per-FTE result, and links the matching core page (idea 9).

Specific, numbers-first outreach to the right size of institution gets replies from credit leaders who ignore generic AI pitches, and it focuses scarce travel on deals that can close.

• Low reply rates from cold email

  Use warm intros from investors for the top 20.

• #9 One page per core system: "Proximitty on Jack Henry SilverLake", "on Fiserv DNA" and the rest

• Your websiteCompany page: mission "to make commercial lending as seamless as possible to power America's small-medium businesses"; serves community and regional banks, credit unions and non-bank lenders; "System of action, not system of record"; hiring "engineers, lending experts, and forward-deployed specialists"; demos start with a 30-minute scoping call.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source
• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source

Publish a redacted sample pack for one agent: the model card (scope, inputs, outputs, limits), a sample validation summary, a page of the audit trail, the escalation rules and the human-review defaults. Gate the full pack behind a simple request; keep the sample public so a champion can forward it to their risk team.

It moves the risk review earlier, when it can still be shaped, and it shows confidence that most AI vendors don't. Competitors talk about governance; few show the documents.

• Revealing too much about how the agents work

  Share structure and controls, not prompts or system detail.

• #1 Make the SOC 2 and go-live claims match on every page, and put the proof one click away

• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source
• Competitor researchAloan (AI commercial loan underwriting for community banks and credit unions) names five bank and credit union customers, shows 5.0 reviews across Gartner Peer Insights, G2, Capterra and SourceForge, publishes comparison pages against nCino, Abrigo, Baker Hill and manual spreading plus buyer's guides, states a 2-4 week go-live, and lists SOC 2 Type II with a Trust Center.See the research Source

Invite each prospect to bring one redacted, messy file (a scanned tax return, a handwritten rent roll, a multi-entity statement) to the scoping call. Run it through the agent live, show the spread with each value linked to its source, and compare it with the bank's current spread. Follow up with the output and a pilot offer.

It proves the accuracy claim on the buyer's terms and turns the first call into the first result. Credit officers remember the file their current tool got wrong.

• A live run goes wrong

  Show the human review step; that is the product working as designed.

• Prospects can't share files

  Use the backup sample file.

• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteCompany page: mission "to make commercial lending as seamless as possible to power America's small-medium businesses"; serves community and regional banks, credit unions and non-bank lenders; "System of action, not system of record"; hiring "engineers, lending experts, and forward-deployed specialists"; demos start with a 30-minute scoping call.Source

For each customer, put a readiness review on the calendar a month before their expected exam: update the model inventory entry, refresh the validation summary, check the audit trail samples and prepare answers to likely examiner questions. Send a one-page readiness report afterwards.

Banks keep the vendors that make exams easier. A routine review also surfaces expansion needs, such as a new loan type or a new agent.

• Reviews take too much expert time

  Standardise the checklist and report.

• #11 Publish a redacted sample of the examiner pack, so risk teams can review before the first call

• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source

Ask each live customer to take up to one reference call a month with a peer institution. In return, offer them early access to new agents, a seat on a small customer advisory group and a fee credit. Keep a list of references by institution type and core, and offer a call once a prospect reaches the pilot stage.

One credit officer telling another "it worked in our core" outweighs any page. Rationing calls to late-stage deals protects customers' time.

• Reference fatigue

  Limit to one call a month per customer.

• Your websiteCustomer page: H1 "Trusted by FDIC-insured commercial banks and fintechs"; "From top-20 banks to high-growth fintechs"; one testimonial, from an unnamed "SVP Credit Risk, Fintech Lender": "They built agents running on our internal systems within a week without any engineering involvement from our side"; "Most integrations go live in under two weeks"; no named customers or case studies.Source
• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Competitor researchEnFi ("The AI credit platform for commercial lenders") leads with a named bank CEO testimonial (over $300 million in new loans in 12 months with existing headcount), five customer logos, task metrics such as "45 minutes per annual review, down from 8 hours", and SOC 2 Type II and ISO/IEC 27001.See the research Source

Choose the category and one-line description for the next 12 months. A good choice keeps servicing visible, since that's where the strongest proof is. Update the homepage title and description, YC profile, LinkedIn, Finovate and investor materials, and write a 50-word boilerplate for press and partners.

Consistent naming makes every mention add to the same memory, for search engines and AI assistants as well as for buyers.

• A broad name hides the servicing strength

  Put servicing in the one-line description.

• Site historyIn February 2026 the homepage headline was "The world's first autonomous collections and servicing team"; it is now "The AI operating system for commercial lending"; Y Combinator lists the company as "AI operating system for commercial loans" (W26, San Francisco, team of 2).Source
• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteHomepage: H1 "The AI operating system for commercial lending"; a "SOC 2 Type II Certified" badge and a GLBA badge next to "Trusted by credit unions and banks across the US"; "Integrate Proximitty with any banking system in 3 weeks" (Week 1 Integration, Week 2 Sandbox Testing, Week 3 Deploy) on the same page as "Most integrations go live in under two weeks."; Request Demo is the only call to action.Source

Offer a defined first step: the bank records one repetitive loan-ops task (for example, booking a covenant test result in the core), shares it securely, and gets back a sandbox agent doing that task within a week. Publish the offer with what to record, how the recording is handled and deleted, and what the bank gets back.

It lets a bank feel the product on its own system before a full pilot, at almost no cost to itself, and it puts operations staff on Proximitty's side early.

• Too many requests for a small team

  Limit to qualified institutions from the target list.

• Recordings include customer data

  Test data only, and delete on schedule.

• #2 Give each of the four products its own page, and publish a sitemap
• #8 Sell a fixed-scope, fixed-price three-week pilot on one loan book

• Your websiteIntegrations on the homepage: Fiserv DNA and Premier, FIS Horizon and IBS, Jack Henry SilverLake and CIF 20/20, Finastra Phoenix, Temenos Transact, CSI NuPoint and Oracle FLEXCUBE, plus nCino, Abrigo and Moody's; browser agents work on existing systems without APIs; Custom Loan Ops builds agents from standard operating procedures or screen recordings.Source
• Your websiteCustomer page: H1 "Trusted by FDIC-insured commercial banks and fintechs"; "From top-20 banks to high-growth fintechs"; one testimonial, from an unnamed "SVP Credit Risk, Fintech Lender": "They built agents running on our internal systems within a week without any engineering involvement from our side"; "Most integrations go live in under two weeks"; no named customers or case studies.Source

Change the Customer page headline to include credit unions. Then build a /credit-unions page in their language: member business lending, NCUA exams, the cores common among credit unions, and how the examiner support works for them. Add credit unions to the call report list (idea 10) as a separate segment with its own sequence.

Credit unions growing business lending often have small teams and the same manual spreading and servicing work, and few AI vendors speak to them directly. A one-line fix stops turning them away.

• Spreading a tiny team too thin

  Treat it as one segment test with a fixed number of calls.

• #10 Build the outbound list from public call reports, ranked by commercial loan book

• Your websiteCustomer page: H1 "Trusted by FDIC-insured commercial banks and fintechs"; "From top-20 banks to high-growth fintechs"; one testimonial, from an unnamed "SVP Credit Risk, Fintech Lender": "They built agents running on our internal systems within a week without any engineering involvement from our side"; "Most integrations go live in under two weeks"; no named customers or case studies.Source
• Your websiteHomepage: H1 "The AI operating system for commercial lending"; a "SOC 2 Type II Certified" badge and a GLBA badge next to "Trusted by credit unions and banks across the US"; "Integrate Proximitty with any banking system in 3 weeks" (Week 1 Integration, Week 2 Sandbox Testing, Week 3 Deploy) on the same page as "Most integrations go live in under two weeks."; Request Demo is the only call to action.Source
• Your websiteCompany page: mission "to make commercial lending as seamless as possible to power America's small-medium businesses"; serves community and regional banks, credit unions and non-bank lenders; "System of action, not system of record"; hiring "engineers, lending experts, and forward-deployed specialists"; demos start with a 30-minute scoping call.Source

Pick one partner type to start: a lending-as-a-service fintech whose bank partners need servicing capacity, or a loan system whose customers want agents on top of it. Offer a co-sold servicing add-on with shared revenue, a joint integration guide and one shared reference customer. Measure partner-sourced pipeline separately.

Partners bring trust and reach that a new vendor lacks, and "system of action" on top of a partner's "system of record" is a complement, not a threat.

• The partner builds the same agents

  Lead with servicing inside legacy cores, which is hard to copy.

• #9 One page per core system: "Proximitty on Jack Henry SilverLake", "on Fiserv DNA" and the rest

• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteIntegrations on the homepage: Fiserv DNA and Premier, FIS Horizon and IBS, Jack Henry SilverLake and CIF 20/20, Finastra Phoenix, Temenos Transact, CSI NuPoint and Oracle FLEXCUBE, plus nCino, Abrigo and Moody's; browser agents work on existing systems without APIs; Custom Loan Ops builds agents from standard operating procedures or screen recordings.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source

Build a benchmark set of synthetic or fully de-identified commercial financial documents (tax returns, rent rolls, interim statements, debt schedules) with expert answer keys from the in-house lending experts. Publish the method, the dataset and Proximitty's score, and invite other vendors and banks' own teams to run it. Update it every quarter.

The company that defines how accuracy is measured owns the conversation. Banks get a neutral test for vendor reviews, and model-risk teams get validation evidence.

• Proximitty scores lower than its claim

  Publish it anyway and show the human-review step that closes the gap.

• Real borrower data leaks into the set

  Use synthetic documents only.

• #3 Put a source under every homepage number

• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source
• Competitor researchAloan (AI commercial loan underwriting for community banks and credit unions) names five bank and credit union customers, shows 5.0 reviews across Gartner Peer Insights, G2, Capterra and SourceForge, publishes comparison pages against nCino, Abrigo, Baker Hill and manual spreading plus buyer's guides, states a 2-4 week go-live, and lists SOC 2 Type II with a Trust Center.See the research Source

Set servicing pricing per active loan per month, with volume tiers and a minimum, and spreading per file. Publish the model (not necessarily the numbers) with a simple calculator: loans serviced today, staff hours per loan, cost per loan today against Proximitty's price. Use it in the pilot offer (idea 8) so the conversion price is known from day one.

Usage pricing grows with the customer's loan book, matches how banks already budget servicing, and moves Proximitty toward its seven-figure ARR goal through expansion, not just new logos.

• Banks prefer fixed budgets

  Offer an annual commitment with per-loan overage.

• #8 Sell a fixed-scope, fixed-price three-week pilot on one loan book

• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Competitor researchEnFi ("The AI credit platform for commercial lenders") leads with a named bank CEO testimonial (over $300 million in new loans in 12 months with existing headcount), five customer logos, task metrics such as "45 minutes per annual review, down from 8 hours", and SOC 2 Type II and ISO/IEC 27001.See the research Source
• HiringMember of Technical Staff (San Francisco, in person, $250K-$300K, 1.5%-3% equity): the first founding engineer, who will deploy at banks alongside credit officers and fly about twice a month to clients; the problems include reliable computer-use agents in legacy banking systems and evaluation infrastructure for high-stakes decisions; targets the largest US banks and fintechs in a commercial credit market put at over $3 trillion; "on track to hit 7-fig ARR by end of year"; backed by Y Combinator, CRV, Cohen Circle and Uphonest; team of 2.Source

With customer consent and strict aggregation, publish a short quarterly report: share of tested covenants in breach or close to it, by loan type (C&I, CRE, SBA) and sector, and the most common early-warning signs. Publish it as a page and a PDF and share it with credit officer communities.

Original data earns press and links that product pages never get, and it shows the servicing product working at scale without naming anyone.

• Too few loans for meaningful numbers

  Wait until the sample is large enough; start with qualitative trends.

• Customer confidentiality

  Consent and minimum aggregation thresholds.

• #7 Start the empty blog with three posts that answer what bank risk teams ask

• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source
• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source

Add a homepage section and a product-page block on the review layer: who the in-house experts are (by background, not name), what they check, how fast, and what happens when they disagree with the agent. Test a hero variant that pairs speed with the human check (for example "Spreads in minutes, checked by credit experts").

It answers the unspoken fear about AI in credit (errors no one catches) and sets Proximitty apart from vendors that sell autonomy alone.

• Buyers read human review as a weakness of the AI

  Frame it as a control the bank would require anyway.

• Low traffic makes the test slow

  Combine with feedback from calls.

• PressFinovateSpring 2026 profile: an "AI-native commercial loan servicing platform for banks, credit unions, and fintechs" with Servicing Studio and Agent Studio; "Every spread is verified by our in-house commercial lending experts for 100% accuracy"; one fintech cut servicing staff from 15 to 2 and redeployed the team to close $20M of new originations in a quarter; key customers are a $5B publicly traded fintech and a $2B regional bank (unnamed); distribution through financial institutions and other fintechs or platforms.Source
• Your websiteCompliance & Governance page (H1 "Regulator-grade governance by design"): "Proximitty is currently in accordance of all SOC 2 principles", followed by tenant isolation, encryption and no training of shared models; agents are "SR 11-7 aligned by design"; model cards, validation artifacts and challenger testing materials are available on request; "Our subject matter experts are available during examinations at no additional cost." The page does not mention Type II, an audit report or GLBA.Source
• Your websiteHomepage results and FAQ: "Underwrite your loans 5x faster", "<10 days to close a loan", "Increase loan analyst efficiency by 8x", "6500 loans serviced per FTE" and "<2% of unserviced loans end in delinquency", none with a source; the FAQ "How is this different from nCino, Abrigo, Moody's and other spreading software?" says legacy spreading tools are wrong "roughly half the time" and claims over 99.8% accuracy on messy documents.Source