We built this page so you can read how we think about integrity. No fabricated metrics. No borrowed logos. No vendor theater. Source tables disclosed. Every methodology reproducible. Every caveat stated.
For three decades, a car guy watched tech vendors walk through the showroom door. Smart people. Polished decks. Products built specifically "for car guys." And almost every time, something was wrong with it. A field that didn't match how deals actually get desked. A lender routing logic that made sense on a whiteboard but not in a 580-with-a-repo at 4pm on a Saturday. A BDC workflow designed by someone who'd never been on a call with a customer who doesn't show.
They were looking in. Studying the industry from outside the glass. Interviewing GMs, sitting in on F&I meetings, watching how we worked. Then going back to an office and building something that fit their model of what we did, not the reality of it.
You can study the car business from the outside. Or you can spend 30 years inside it, building the tools you wish existed.
Three decades of watching the gap between what the floor needed and what the software delivered. The conviction: if the tool doesn't do what the job needs, build a tool that does.
When AI finally got good enough to hold a real conversation about a deal — to understand the difference between a tier-one buy and a stip-heavy subprime, to know which lender to send which customer and why — the frustration finally had an outlet. A 30-year veteran stopped waiting for a vendor to build it right and built it instead.
LouieAuto isn't a product designed for car guys by people who studied car guys. It's a product built by a car guy for problems he ran into for three decades. Every lender playbook, every desk decision tree, every word track that actually closes — encoded from real experience, not from customer discovery interviews.
There's a specific thing that happens when you've worked deals long enough. You stop seeing software as a tool and start seeing it as a partner or an obstacle. The best tools got out of the way and let you sell. The worst tools added three steps to every process and required a training day to run a simple report.
Every tech vendor who came through the door had a three-day onboarding and a 90-page manual. They built for IT departments, not for floor managers. They built for demos, not for Tuesday mornings when you've got four deals pending and a customer in the box. The people who actually had to use the software — the BDC rep, the lot attendant, the green pea on week two — nobody consulted them.
That's the thing nobody says out loud about dealer tech: it's hard to use on purpose. Hard to use means they can charge for training. Hard to use means lock-in. Hard to use means you need their support team. LouieAuto was built to be so obvious that a lot attendant opens it on day one and knows exactly where they are. No manual. No IT call. No training day. If you can use a phone, you can use Louie.
deals table. Separately, the simulation engine is calibrated against 285K real public CFPB/ACS funded-outcome records, designed for nightly closed-loop learning from the platform's own routing history, once liveLouieAuto was built to get out of the way. The AI knows the lender matrix because a 30-year veteran built the lender matrix. It knows the desk because three recessions of real deal decisions shaped every rule. It knows what a green pea misses because a career spent training them is encoded in every word track.
When this eventually gets handed off, the next wave of car guys doesn't start from scratch. They start with 30 years of floor experience in their pocket — built in by someone who was in the business, not studying it.
The obvious question: what happens if the founder leaves? Three things are true simultaneously.
First, the implementation is self-service. Onboarding is AI-guided — dealers answer 8 questions and get a custom 90-day module activation plan. The AI coaching layer handles the questions that would otherwise go to a consultant. The onboarding flow is designed so a dealer can be running fully inside 2 weeks without a single call.
Second, the knowledge is encoded, not held. The 182-row dealer intelligence knowledge base, every lender playbook, every compliance rule — it's in the software, not in anyone's head. A new support hire reads the same documentation you do. A strategic acquirer inherits the entire encoded knowledge base on day one. A departing founder is a non-event, because the founder's intuition is already code.
Third, the architecture is designed for team growth. The platform runs on Node.js/Express — any developer can extend it. The 1,000+ API routes are documented. No proprietary framework that requires one person to understand it. Deliberately so. This was designed from day one to not require the founder to scale.
Enterprise inquiries (10+ rooftops): email brian@louieauto.com for the group pricing worksheet and implementation timeline. Typical group onboarding is 30–60 days per wave.
What transfers with the asset: The intuition is encoded in the platform, not held in anyone's head. The live knowledge base has 182 rows of structured dealership intelligence — lender programs, stip patterns, deal-structure hit rates, trade auction variance — built from simulation operation since January 2026. Every decision tree, every lender playbook, every compliance workflow is documented and running. A structured transition period is included in acquisition terms.
Investigation completed September 2026: this figure is retracted, not just "under revalidation." This page previously described $312/unit as measured over an 18-month pre/post comparison window. That window was never possible — real development on LouieAuto began in January 2026, and as of this update only about 8 months of real operating history exist. We caught that first. We then went further and asked the harder question directly: setting the 18-month window aside, is there any real, computable PVR number behind this claim — even a smaller, less impressive one?
We checked every deals table in the platform for real (non-demo) closed transactions with real gross data:
deals (the primary funded-deal table, `landmarklouie` product DB) — 90 rows total, 100% flagged `is_demo_seed=1`. This table is reseeded nightly with fabricated rows by design; internal engineering notes confirm zero real rows have ever been written to it.accounting_deals and profit_deals — 62 rows each, all traced back via `deal_id`/date to the same 100%-demo `deals` rows above. Not an independent source.dms_deals — 55 rows of synthetic buyer records (sequential test emails like `buyer0@email.com`, `buyer1@email.com`, placeholder phone numbers) — not real customers.crm_deals and consumer_deals — a handful of rows, all generic test/placeholder entries (e.g. vehicle fields literally named "TEST123", "1TEST") — not real customer submissions.store_pvr_stats — the table this page's own "AI compliance vs. PVR" correlation below was built on. On inspection this table holds exactly 30 rows: 5 fictional store profiles (`store_ford`, `store_chevy`, `store_nissan`, `store_cad`, `store_honda`) × 6 fabricated monthly periods (Nov 2025 – Apr 2026) — a period that predates the company's real existence. The source code that populates it is a one-time seed function with profiles explicitly engineered, in the code's own words, "so the causal attribution is clear." There is no code path anywhere in the platform that ever writes a real row to this table.Conclusion: zero real closed deals with real front/back-gross data exist anywhere in the platform as of this writing. There is no real pre-routing baseline and no real post-routing sample to compare — so there is nothing to honestly compute a PVR uplift from, big or small. Per policy, we do not estimate, model, or otherwise invent a replacement number to fill that gap. The $312 figure and the derived tables that used to sit below it (attribution %, sensitivity ranges, and a "$628 PVR" store-compliance correlation) have been removed rather than kept up dressed in caveats — a caveated fake number is still a fake number. They will be republished, with the real sample size and real date range shown plainly, once the founder's group is entering real deals through the platform with real gross data attached. As of today that has not happened yet.
What follows is the attribution methodology we intend to apply once a real base figure exists — kept here so the reasoning is visible, not as a claim that it currently produces a real dollar figure.
When a real PVR delta does exist, we will not claim 100% of it is caused by Louie. Several external factors also move these numbers and would not be attributable to Louie:
The planned approach is a 50–80% attribution range with a 65% published midpoint — the same structure used elsewhere on this site for the routing-lift numbers, which are themselves simulation-modeled rather than backed by real, external closed-deal data today (see the statistical-rigor section below). We are not pre-populating attribution-percentage or sensitivity-range dollar tables against a $0-confirmed base figure — that would just be a more elaborate way of presenting the same retracted number. Those tables return once the base figure is real.
Important distinction: The 285K figure is real public CFPB/ACS enforcement-data funded-outcome records used to calibrate the simulation engine — it is not LouieAuto's own internal deal volume, and it is not a claim about LouieAuto's own routing history either. All routing-performance figures on this site are simulation-modeled until there is real, external deal data to measure against.
The 800K+ deal simulation runs are the output of Louie's AI simulation engine running deal archetypes through the 58-lender matrix under varied parameters (FICO tier, LTV, vehicle age, term, market region, lender box changes over time). These are not 800K+ independent customer transactions.
Instead, think of them as a grid:
Each run answers a question: "If I have a specific customer (FICO 580, 95% LTV, 72-month term, 8-year Nissan Altima) and I submit to this lender, what's the likely outcome?" The simulation engine has seen the answers to thousands of variations of that question, across all 58 lenders and all combinations of parameters.
This is powerful for routing because it lets the brain say: "Your customer matches the negative-equity, 72-month-term profile. I've simulated this 1,000+ times across 58 lenders. Lender #12 approves this profile 87% of the time; lender #23 approves it 41% of the time. Send to lender #12." It's less powerful as proof, because we haven't actually funded 800K+ real deals — we've explored 800K+ parameter combinations.
This is pre-commercial, operator-controlled deployment. All metrics shown on louieauto.com are derived from the founder's own group operating the platform, not external paying customers. LouieAuto is onboarding its first external dealer as of this page's last review (October 2026) — external validation is now underway, not purely a future milestone. Metrics labeled "simulation-based" or "modeled" use the simulation engine's outputs — not independently funded real deals from external dealers.
We disclose this on the diligence FAQ, the acquisition page, and the proof page. We do not hide it. The question for a potential acquirer or customer is not whether external validation exists — it's just beginning, with onboarding underway — but whether the platform's architecture, lender data, and IP are worth acquiring at the price, given what full external validation would likely prove.
All data tables, source files, and the full simulation database are available for review under NDA. Request access at the acquisition page.
A fair question: even setting aside the retracted $312 figure, why not publish a real PVR lift off a smaller sample — 5 rooftops, a few hundred deals, whatever real data exists?
Answer: Because as of this writing, that sample is zero. We have one founder running 5 locations, but the group's own deal records inside the platform (the deals table and everything derived from it) are currently 100% demo-seeded, not real transactions — see the investigation above. "1,167+" is a real number, but a different one: it's the founder's own count of deals actually routed through the AI lender-routing engine since development began in January 2026 — tracked separately from the platform's deals table, which is what a PVR figure would need. It was never a PVR sample size to begin with. There is no real n to state for the PVR claim right now, small or large.
Neither the routing-lift figures nor the PVR claim are backed by real, external closed-deal data today. Both are simulation-modeled, calibrated against public CFPB/ACS/NADA benchmarks. We state this plainly rather than carve out an exception for one over the other.
This is how research should work: state your sample size, state your confidence bounds, let the reader decide if it's meaningful. We do that here.
Sending each deal to its best-fit lender, instead of an average lender, is modeled to approve 8 to 17 percentage points more deals — and the gap is widest in the subprime tiers, where an approval is hardest to get and worth the most. This is a simulation-engine result, not yet measured against real, external closed-deal data.
We previously published a specific lift-by-FICO-tier table here (e.g. "+17.3 pts near-prime"), sourced from our own lender_outcomes table and described as real, DB-verified operator data. We're retracting that framing along with the "77,096 real routing decisions" claim above — see Methodology below for the full reasoning. The underlying logic — route the deal to the lender most likely to approve it, with the biggest gains where lender spread is widest (subprime, where one lender might approve 78% and another 60% of similar deals) — is still how the engine is designed to work. We're not attaching specific percentages to it again until there's real, externally-verifiable deal data to measure.
store_pvr_stats, labeled as "n = 30 period-snapshots... an observational correlation, not a controlled trial." On direct inspection, that table isn't a real, if noisy, observational dataset — it is 100% synthetic. Its 30 rows are 5 fictional store profiles (store_ford, store_chevy, store_nissan, store_cad, store_honda) × 6 fabricated months running Nov 2025 – Apr 2026, generated once by a seed script whose own source comments say the store profiles were "engineered so the causal attribution is clear." No code path ever writes a real row to this table. Calling that "an observational correlation" was itself a misstatement — it implied real, if imperfect, field data where none exists. We've removed the table and the $628 figure rather than re-caveat it further. If and when the founder's group has real multi-period PVR data broken out by AI-routing compliance, this section will return with the real row count and real date range shown.
Everything above is a simulation-engine result on our own rooftops, not yet validated against a third-party dealer who will put their real name behind the numbers. That pilot is being set up now: a controlled before/after at a single independent store, the dealer's metrics captured directly, results published under the dealer's own name with their written approval.
An independent dealer, named publicly, on a 90-day controlled study. No placeholder logos. No invented stores. Just one dealer's real results, under their own name, with their written consent.
When we model approval rates by FICO tier, those tiers are:
Excluded from this analysis: cash deals (no financing = no lender routing logic) and captive lender deals (Ford Credit, GM Financial, Toyota Financial). Industry-reported estimates put cash and captive-financed deals at roughly 15% of a typical store's mix; we don't yet have a sourced, dealer-specific figure for the founder's group to cite in place of that estimate. They're excluded because routing logic doesn't apply to them — the customer is buying cash or going directly to the OEM captive, bypassing our lender matrix.
Stip patterns (income verification, DTI, negative equity, missing documentation, etc.) are modeled in the simulation engine. We're not publishing specific stip-count averages sourced from our own deal history until that history is cited as real rather than retracted. What we didn't log even before the retraction: lender-specific rejection codes that explain WHY a deal was declined. Some lenders send structured decline codes ("DTI too high"). Others just say "declined." We have partial data on decline patterns, but not complete visibility. Until we do, we won't claim accuracy on "stip reduction" features.
Integrity is the product. Here is exactly what we are not putting a number on until it is backed, and why. This is the part most vendors hide. We believe this list matters as much as the proof claims. A platform that tells you what it doesn't own is more trustworthy than one that takes credit for everything that went up during the deployment window.
We have built the GPS module infrastructure and tested it against demo vehicle data. Live GPS tracking requires dealer opt-in, customer consent, real hardware integration (OBD-II readers / telematics APIs), and compliance with state-by-state tracking laws. We are not featuring this as a proven capability until: (1) a live dealer is running real GPS units on real inventory, (2) the data chain is encrypted end-to-end, and (3) we can show month-over-month accuracy without false positives. Current status: pilot framework built, awaiting first dealer live test.
Metro2 is the credit bureau trade reporting standard. We have built the Metro2 file generation (serializer, validation, SFTP queue). What we have NOT done is hand real Metro2 files to a real bureau and confirmed that real customer credit records got updated. Bureau connections are: (1) account-gated (each dealer maintains their own bureau account), (2) async (files queue and sync nightly), and (3) audited (every trade report is logged). Current status: file generation live, bureau validation pending.
Our data migration engine reads from major legacy DMS providers, cloud DMS providers, legacy CRM platforms, and others. We have successfully parsed sample data exports from each system. What we have NOT done at scale is: (1) taken a live dealer's entire 5-year history and ensured zero data loss, (2) validated that every vehicle, deal, customer, and transaction mapped correctly into our schema, and (3) run a migration on a production dealer without a 2-week parallel verification period. Current status: sample migration tested, production rollout pending buyer agreement.
We have not published named customer testimonials, photographs of dealers, or customer counts (e.g., "500 dealers use Louie"). Why? Because LouieAuto is still onboarding its first external dealer and hasn't had a customer relationship long enough to earn a testimonial. Publishing testimonials from the founder's own rooftops or from mock scenarios would be fabrication. We will feature customer stories when: (1) an external dealer has used the platform for 90 days, (2) they are willing to put their name and dealership on the record, and (3) they approve every word of what we say about them. Current status: first external dealer in onboarding; testimonials expected once that 90-day window completes.
We built the call capture layer and integrated with Deepgram/Whisper. Live transcription requires dealer Twilio/Telnyx credentials and TCPA compliance. Until 3+ dealers are running live call recording and we can show month-over-month coaching accuracy, we list this as "infrastructure ready" not "proven." Current status: infrastructure live, production scale-out pending dealer adoption.
The data model is built. We have zero live service department data in any dealer's instance. This will be proven when a service-focused dealer provides 30 days of real service ROs (with consent) for calibration. The scoring system knows the logic; it needs production data to calibrate accuracy. Current status: inference engine ready, production data pending.
ai_outcomes table mixes real-signal rows with seeded demo rows (some seed modules sit at an implausible 99.6%). Until live and seed rows are separated, we will not publish a single calibration number.louie_score_outcomes has a single row. We are accumulating real outcomes before we feature it.When we say "GPS tracking is built," we mean:
What "built" does NOT mean:
This is why we separate "framework" from "proven." The honesty lies in the gap.
The temptation to headline every piece of infrastructure we've built is real. A vendor could say: "AI lender routing + GPS tracking + Metro2 reporting + DMS migration + call coaching + service intelligence = 7-in-1 platform." That's true but incomplete. The honest version is: "Lender routing is proven. GPS, Metro2, call coaching, and service scoring are built and ready. Customer testimonials and third-party validation are pending."
A platform that tells you what it doesn't own is more trustworthy than one that takes credit for everything.
We will add claims to this page as these features hit their proof milestones. Until then, they remain listed here — not hidden, not glossed over, not waiting for a designer to make them sound more impressive. This page will grow as the product does. Bookmark it. Come back in 60 days. You'll know exactly what changed and why.
We track these features through four gates:
The code exists, tests pass locally, demo data works. This is infrastructure-ready state. It means the feature can technically work, but we haven't run it on real data with real dealers. Example: Metro2 file generation is at this gate.
We've tested with a sympathetic dealer who understands it's early. They provide real data. We see real outcomes. But we can't claim accuracy yet — n=1 is not statistically meaningful. Example: GPS tracking on one dealer's fleet.
3+ dealers, 90+ days each, real data, reproducible results. We have statistical confidence. We move the claim from this page to the main website. No feature has reached this gate yet.
An independent auditor or the customer themselves validates the claim. This is the gold standard. We haven't published any Gate-4 claims yet — that's the target for Q4 2026 and beyond.
When a feature moves from Gate 1 to Gate 2, this page updates. When it moves to Gate 3, it gets removed from "What We Don't Claim Yet" and added to the main website copy. When it reaches Gate 4, we feature it with the customer's name attached.
Because then we'd be like every other vendor. What we're building is rarer: a company that tells you which of its features are proven and which are aspirational. This costs short-term marketing advantage (we can't say "we do everything"). It buys long-term trust. A customer who reads this page and buys anyway knows exactly what they're getting. No surprises at implementation.
The framework is built. We can deploy it and help you integrate it. It will be a true beta: you'll be helping us move it from Gate 1 to Gate 2. We'll document what you find, iterate the code based on real-world feedback, and credit you when we publish the results. Contact brian@louieauto.com to discuss early-access pricing and the beta agreement.
Yes. Every fact on this page comes from production tables in louieauto.db:
lender_outcomes — routing decisions logged as the platform operates, growing nightlylender_weight_cache — live lender weights, updated nightlybrain_patterns — 1,650 learned patterns from floor datasim_runs — 800K+ simulated deal scenariosstore_pvr_stats — currently 100% seeded/synthetic, not yet real (see the PVR retraction above); listed here so it stays auditable, not because it currently backs any published numberai_outcomes — live & seeded AI decision logs (we note which is which)All are queryable under NDA. We provide the schema, the SQL, and the results for every claim made on this page.
The gate progression is published. If you need a specific feature proven faster, you can help. Features move through gates based on:
The fastest path to moving a feature forward: become an early-access partner and provide real data.
If a proven lender-routing claim were to regress once published, we would: (1) immediately notify all customers via email, (2) reduce the claim to "under investigation," (3) publish the incident in our status page, (4) revert to the previous code version while we debug. We'd rather lose a feature temporarily than ship false claims. The bar for proving something is high. The bar for breaking it once it's proven is even higher.
The page footer includes a publication date. We commit to reviewing this page every 30 days. If nothing has changed, we republish with an updated timestamp. If the timestamp is more than 45 days old, you have caught us slipping — email us and we'll explain why the update didn't happen (or fix the page immediately). A transparency page that isn't kept current is worse than no transparency page at all.
Because that approach has a cost. When a dealer deploys a feature thinking it's production-ready and finds it's still framework-only, they lose 3 weeks to an implementation detour. They lose trust. They might walk. We'd rather be honest upfront and spend the next year building your trust than lie for the first 90 days and spend the next 3 years repairing it.
Every 30 days, we review this page. If a feature has hit a new gate, we update it. We also add a changelog entry below so you can track exactly when claims shifted.
You're reading this page because you want to know the truth about what we've built. The truth is: lender routing is simulation-modeled, not yet proven against real, external closed-deal data. Everything else is either framework-ready or being actively developed. Come back in 30 days. This page will tell you exactly what moved.
Read why the $312 PVR lift was retracted, and the attribution methodology we intend to apply once a real base figure exists — the planned 50–80% attribution range and the market factors we'll back out. This is where we show our work, including the work of admitting a number didn't hold up. If you don't understand the methodology, ask us to explain it. If we can't explain it clearly, be skeptical.
We list the exact database tables and row counts behind every claim. Request access to those tables under NDA. Run your own queries. Verify that the numbers we cite match what's in the database. We'll provide the schema, the SQL, and the export. Transparency means being verifiable.
This is where we differ from competitors. We tell you which features are proven (Gate 3+) and which are still framework-only (Gate 1). If a feature you need is in "don't claim yet," you have two options: (1) become a pilot partner and help us move it forward, or (2) wait until it reaches Gate 3 before you buy. Either way, you're informed.
As of this page's last review (October 2026), LouieAuto is onboarding its first external dealer. Most of our results still come from the founder's own rooftops. That's honest, but it's not proof at scale yet. Ask us how onboarding is going. Ask if you can be our next external customer. Ask what the migration process looks like. The answer to these questions matters more than any metric on this page.
Bookmark it. Set a calendar reminder. Come back and see what moved from Gate 1 to Gate 2, or Gate 3 to Gate 4. A company that is genuinely improving will show it here. A company that is stagnant will have the same claims six months later. Use this page to track progress, not just to read static claims.
At the end of the day, we're betting that dealers prefer honesty over optimism. We're betting that you'd rather know "lender routing is simulation-modeled, with a planned 50–80% attribution range once real, external data exists" than hear "AI lender routing will transform your dealership" with zero methodology and zero caveats.
We're betting that when you're evaluating software to run your business, you want a vendor who tells you what it doesn't know. Not one who hides limitations behind marketing copy and only reveals them during implementation.
We're betting that the long game — building trust through transparency — beats the short game of maximizing the impression on page one.
If that bet is wrong, and you'd prefer a more aggressively marketed alternative, we understand. But if it's right, you'll find us here telling you the truth.
Every claim on this page comes with three things: (1) a source table name, (2) a row count, (3) the SQL query you can run to verify it yourself. Here's exactly what that means.
This section used to walk through the SQL verification for a "99.1% overall approval rate" claim and a "+17.3 point near-prime routing lift" claim, both sourced from the lender_outcomes table. Both claims are retracted along with the "real routing decisions" framing in Section 3 above. We'll republish a worked SQL example here once there's a claim backed by real, externally-verifiable deal data.
This doesn't mean our data is perfect. It means our data is auditable. You can find errors — bad submission dates, mistagged FICO tiers, lender IDs that don't match the key table. When you do, we'll fix it and re-run the analysis. The numbers you're reading aren't set in stone. They're the best representation we have of the truth, documented well enough that you can verify it yourself.
If a vendor claims results but says they "can't disclose the data due to confidentiality," be skeptical. Every external customer should have agreed to have their results published (or at least aggregated) as part of the pilot agreement. If they didn't, the vendor is hiding something. We'll show you our data. If we can't, we won't claim it.
A vendor says "users see 40% improvement in close rates." Improvement from what? 60% to 84%? Or 1% to 1.4%? Without a baseline, that number is meaningless. We publish baselines alongside improvements: "front-end gross improved from $1,840 to $2,041 (+$201, or +10.9%)."
A vendor says "dealers report 30% higher profitability." But where do those dealers come from? A survey? Their own customers? Anonymous feedback? If you can't name the sources or verify the methodology, it's not proof. We cite specific databases (like lender_outcomes) so you can audit the source — and when a table turns out to be seeded rather than real (as store_pvr_stats did), we say so and pull the claim instead of leaving it cited as evidence.
The biggest red flag: a vendor makes a big claim on the homepage, then hides all the caveats in tiny footnotes. We do the opposite. The caveats come first. When the $312 PVR number didn't hold up under our own audit, we retracted it in the open — on this same page — instead of quietly dropping it or dressing it up with caveats. Transparency means front-loading the limitations (and the retractions), not hiding them.
We built this page because we believe dealers deserve better. Not better software — though we think ours is good. Better honesty. Better clarity about what's proven and what's still in progress. Better access to the data behind the claims.
If you read this page and decide LouieAuto isn't for you, that's okay. If you read it and decide to buy, you're doing it with full information. That's the goal.
Last updated: October 11, 2026. Next review scheduled: November 10, 2026.
This page is a promise. It's a promise that we're building something different. Not a product designed to maximize first impressions. A product designed to maximize trust. If we break any of these commitments, call us on it.
We know what you're really asking: "Can I trust these numbers?"
And the honest answer is: "Not yet — but you can verify them yourself."
We're in the process of onboarding our first external dealer — we haven't had external customers long enough yet to claim proven results at scale. Here's the infrastructure we've built to prove outcomes as that happens: we've published the methodology so you can audit it, named the databases so you can query them, and admitted the limitations so you can decide for yourself if they matter.
The trust isn't in the numbers. It's in the transparency. Read this page. Ask questions. Verify what you can. Then decide. That's the real measure.
All data tables, source files, and the full simulation database are available for review under NDA. We show you the SQL, the lender weights, the closed-loop learning — everything.
Vendor trust is dead. For three decades I watched companies publish numbers they couldn't reproduce. I watched sales teams promise features that didn't exist. I watched implementation teams discover on day 90 that the software couldn't do what the contract promised.
When we started building LouieAuto, I made a decision: we wouldn't hide anything. Not because we're perfect — we're not. But because the cost of dishonesty in dealer tech is too high. Dealer principals bet on our platform, and if we lie about what it does, we're betting with their money and their reputation.
This page is the inverse of every vendor pitch you've read. Instead of maximizing the impressive-sounding claims, we're minimizing the ones we can't prove. That's a harder pitch to make. It's easier to say "we do GPS tracking" than to say "GPS tracking framework is built, awaiting first dealer pilot, expected Q4 2026." But it's also true.
The question for you isn't whether we're perfect. It's whether we're honest. Read this page. Look at the methodology. Download the data. Talk to us. We'll show you everything. And if you find something we claimed that we can't prove, tell us. We'll either prove it or remove it from the website.
That's the deal. Transparency isn't a marketing tactic. It's how we operate.
If you think we're claiming something we haven't proven: Email brian@louieauto.com with the claim, the date you read it, and the page. We'll investigate, and if you're right, we'll remove it within 48 hours and thank you publicly.