Carvis.AI
AI-powered diagnostic tool for auto repair shops leveraging proprietary data.
Website: https://carvis.ai
Cover Block
PUBLIC
| Field | Value |
|---|---|
| Name | Carvis.AI |
| Tagline | AI-powered diagnostic tool for auto repair shops leveraging proprietary data |
| Headquarters | San Francisco, California, United States |
| Founded | 2024 |
| Stage | Pre-Seed |
| Business Model | B2B |
| Industry / Vertical | Automotive aftermarket software |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Funding Label | Pre-Seed (amount undisclosed) |
| Lead Investor | Trucks Venture Capital |
Links
PUBLIC
- Website: https://carvis.ai/
- Product app: https://app.carvis.ai/
- LinkedIn: https://www.linkedin.com/company/carvis
- Instagram: https://www.instagram.com/carvis.ai/
- Crunchbase: https://www.crunchbase.com/organization/carvis-ai
- PitchBook: https://pitchbook.com/profiles/company/597745-81
Executive Summary
PUBLIC
Carvis.AI is a 2024-vintage San Francisco startup building an AI estimating and diagnostics layer for independent auto repair shops, a segment where pricing has historically relied on technician memory and shop-by-shop convention rather than structured data [carvis.ai]. The company describes its core product as a system that turns repair orders into actionable intelligence, helping shops produce faster and more consistent estimates for customers [carvis.ai]. A secondary product listing characterises the platform as delivering "rapid, accurate vehicle diagnostics, repair estimates, and maintenance guidance" for both shops and car owners [MOGE]. The company is led by co-founder and CEO Jay Warida [Crunchbase]. Carvis.AI has raised an undisclosed pre-seed round led by Trucks Venture Capital, a transportation-focused fund whose thesis explicitly covers founders "building the future of transportation" [Crunchbase]. The pairing of an aftermarket-services target market with a transportation-specialist lead investor is one of the more interesting signals in an otherwise quiet public profile. Over the next 12 to 18 months, the questions worth tracking are whether Carvis can demonstrate paid shop deployments at scale, whether it can defend a proprietary repair-order dataset against incumbent shop management systems, and whether the team expands beyond its founder-led core. Public disclosure today is thin, and investors should expect to source most diligence material directly from the company.
Data Accuracy: YELLOW -- Founder, HQ, founding year, and lead investor are confirmed across Crunchbase, PitchBook and the company website; round size, customer count, and team depth are not disclosed publicly.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Pre-Seed |
| Business Model | B2B SaaS (auto repair shops) |
| Industry / Vertical | Automotive aftermarket / shop software |
| Technology Type | AI / Machine Learning applied to repair-order data |
| Geography | North America (HQ San Francisco) |
| Growth Profile | Venture Scale |
| Founding Team | Jay Warida (CEO, co-founder) |
| Funding | Pre-Seed, lead Trucks Venture Capital, amount undisclosed |
Company Overview
PUBLIC
Carvis.AI was founded in 2024 and is headquartered in San Francisco, California [PitchBook][Crunchbase]. The company positions itself publicly as an "AI Technician for Auto Repair Shops", with a stated focus on converting the millions of repair orders flowing through the independent shop ecosystem into structured pricing and diagnostic intelligence [Crunchbase][carvis.ai]. Crunchbase lists the company in the 1 to 10 employee band as of its most recent profile snapshot [Crunchbase].
The founding story, as far as it is publicly told, centres on Jay Warida, who is identified as CEO on Crunchbase and as a co-founder on third-party contact databases [Crunchbase][RocketReach]. The company website's team page carries a third-party endorsement framing the problem the company is attacking: "Auto repair shop pricing has historically been more guesswork than science. By turning millions of repair orders into actionable intelligence, Carvis is giving their partners clarity they've never had before" [carvis.ai]. The quote is presented as testimony from a partner rather than as marketing copy from the founders themselves.
The single confirmed financing milestone to date is an undisclosed pre-seed round led by Trucks Venture Capital, a fund that has historically backed early-stage transportation and mobility software companies [Crunchbase]. Crunchbase obfuscates both the announcement date and the dollar figure for that round. There are no confirmed accelerator affiliations, no announced enterprise customer wins, and no public legal entity filings beyond the Crunchbase and PitchBook records. Investors evaluating the company should note that several similarly named entities exist in adjacent automotive categories, including a parking lot management company also called Carvis [Crunchbase] and a personal car expenses assistant of the same name developed by LANARS [LANARS]; these are distinct businesses from the San Francisco Carvis.AI covered here.
Data Accuracy: GREEN -- Founding year, HQ, CEO, and lead investor confirmed across Crunchbase, PitchBook, and the company website.
Product and Technology
MIXED
The public product description is narrow and consistent across surfaces: Carvis.AI applies machine learning to repair-order data to generate estimates, diagnostics, and maintenance guidance for independent auto repair shops [PUBLIC] [carvis.ai][MOGE]. The company's positioning line on Crunchbase is "AI Technician for Auto Repair Shops" [PUBLIC] [Crunchbase]. A live product surface exists at app.carvis.ai, indicating the product is past pure concept stage and is being delivered as a hosted web application [PUBLIC] [carvis.ai]. The company also publishes a testimonials page, suggesting at least some pilot or early-paying shops are in production [PUBLIC] [carvis.ai].
The differentiation claim, as stated by the company and echoed by third-party listings, rests on proprietary repair-order data rather than on a novel model architecture [PUBLIC] [StartupSeeker][carvis.ai]. The implied thesis is that the value of an AI estimator in this category is determined less by the underlying language or vision model and more by the volume, structure, and labelling of historical repair-order outcomes the system can train and ground on. That is a defensible framing in principle, although the company has not publicly disclosed dataset size, source partnerships, or coverage by vehicle make and model.
No verified technology stack details are public. There are no open engineering roles surfaced on the company careers page or major ATS hosts at the time of research, which limits the usual signal investors derive from job descriptions [PUBLIC]. Integrations with incumbent shop management systems (the category occupied by Shopmonkey and Tekmetric), pricing model, and the split between shop-facing and consumer-facing surfaces are not described in public materials. The MOGE listing's mention of "car owners" as a secondary user suggests a possible consumer-facing surface, but this is not corroborated by the company's own website, which reads as shop-first [carvis.ai][MOGE].
Data Accuracy: YELLOW -- Product positioning corroborated by company site, MOGE, and Crunchbase; technical architecture, dataset scale, and integration footprint are not disclosed publicly.
Market Research and Opportunity
PUBLIC
The independent auto repair shop is one of the larger pools of small-business software spend in the United States that has not yet fully consolidated onto a modern AI-native stack. The U.S. Bureau of Labor Statistics estimates roughly 770,800 automotive service technicians and mechanics employed in the United States as of May 2023, working across tens of thousands of independent and chain repair facilities (analogous market, U.S. Bureau of Labor Statistics, OOH 2024 release). The Auto Care Association's most recently published Auto Care Factbook places the total U.S. auto care industry at well over $400 billion in annual end-user spend (analogous market, Auto Care Association Factbook). These are widely cited public figures used by category investors to frame the opportunity, although Carvis.AI itself has not published its own TAM/SAM/SOM disclosure.
| Sizing reference | Figure | Source |
|---|---|---|
| U.S. auto service technicians employed | ~770,800 (May 2023) | U.S. Bureau of Labor Statistics OOH (analogous market) |
| U.S. auto care end-user spend | $400B+ annual | Auto Care Association Factbook (analogous market) |
The analyst takeaway is straightforward: the denominator is genuinely large, but the addressable software wedge is a fraction of total category spend, and pricing power for shop software has historically been constrained by the small-business buyer's willingness to pay only low-hundreds of dollars per shop per month. The opportunity is a function of how many shops Carvis can reach and what attach rate it can hold against that ceiling.
Three demand drivers are worth flagging from the cited research surface. First, the diagnostic complexity of modern vehicles, particularly hybrids, EVs, and ADAS-equipped models, has risen sharply, increasing the value of decision-support software at the service-writer's desk. Second, technician supply remains tight relative to demand, which raises the marginal value of any tool that compresses estimate-writing time. Third, the broader move of vertical SaaS into AI-native estimating and quoting (visible in adjacent categories such as construction, dental, and HVAC) suggests buyer readiness for AI-assisted pricing tools is no longer purely theoretical.
Adjacent and substitute markets matter. Shop management systems such as Shopmonkey and Tekmetric already sit in the workflow and could add AI estimating as a feature; parts catalogues and labour-time guides such as Mitchell 1, ALLDATA, and Motor sit upstream and own much of the structured data Carvis would need to compete on coverage. Regulatory and macro forces are mostly indirect: right-to-repair legislation and growing OEM telematics gatekeeping shape what data third parties can access, which over time will materially affect any company whose moat rests on a proprietary dataset.
Data Accuracy: YELLOW -- Market sizing references are drawn from named third-party sources covering the analogous category; Carvis.AI has not published a company-specific TAM disclosure.
Competitive Landscape
MIXED
Carvis.AI is entering a category with two well-funded incumbents in shop management software and a long tail of estimating tools, and its positioning rests on being an AI-native estimating layer rather than a full shop OS [PUBLIC].
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Carvis.AI | AI estimating and diagnostics for repair shops | Pre-Seed, Trucks VC lead | Proprietary repair-order dataset framing | [Crunchbase] |
| Shopmonkey | End-to-end shop management OS | Series C, $75M raised in 2021 | Full workflow incumbency, broad shop install base | [Shopmonkey press, 2021] |
| Tekmetric | Cloud-based shop management system | Growth-stage, Series B led by Bregal Sagemount | Deep integrations with parts and payments, large shop footprint | [Tekmetric press] |
The segment-by-segment map breaks roughly into three groups. Shop management incumbents (Shopmonkey, Tekmetric, Mitchell 1 Manager SE, Tekmetric, Protractor, AutoLeap) own the system of record at the shop and increasingly bundle estimating, parts ordering, and customer communication. Data and content providers (Mitchell 1, ALLDATA, Motor, MOTOR Information Systems) own the labour-time guides and OEM service information that any estimator must reconcile against. AI-native challengers, the cohort Carvis sits in, are betting that an estimating workflow rebuilt around a learned pricing model produces a better answer than the rules-and-lookup approach baked into incumbent systems.
Where Carvis appears to have a defensible edge today is in focus and capital-efficient design: a single, narrow workflow (estimate generation) rebuilt natively on machine learning, paired with a transportation-specialist lead investor whose network spans both fleets and aftermarket [Crunchbase]. That edge is real but perishable. Shopmonkey and Tekmetric can plausibly add AI estimating to their existing platforms inside a 12 to 18 month product cycle, and they would do so with a distribution advantage Carvis does not yet have: a paying shop already inside their UI every day.
Where Carvis is most exposed is the channel question. Shop owners are notoriously hard to reach at scale and tend to buy software through trusted local relationships, distributor reps, and industry trade events. A pure-play AI estimator that does not own the shop management surface has to win either by integrating into the incumbent (which then sets the commercial terms) or by convincing shops to run a second piece of software next to their existing system. Neither is impossible, and both have been done in adjacent verticals, but both are slower and more expensive than the company's current public footprint suggests it has yet financed.
The most plausible 18-month competitive scenario splits cleanly. Carvis becomes a winner if it signs a defensible data partnership (for instance, with a multi-location chain, a warranty provider, or a parts distributor) that gives it a repair-order corpus the incumbents cannot replicate without similar deals; in that scenario, an acquisition by a Shopmonkey or Tekmetric class buyer becomes a credible outcome. Carvis is the loser if Shopmonkey ships a comparable AI estimating module bundled into its existing subscription before Carvis can demonstrate measurable shop-level ROI, in which case the standalone wedge collapses into a feature.
Opportunity
PUBLIC
If Carvis.AI executes against the brief implied by its product and its lead investor, the prize is the default AI estimating layer for the long tail of independent U.S. auto repair shops, a position no incumbent currently holds.
The headline opportunity. The single largest plausible outcome is for Carvis to become the pricing brain that sits behind every estimate written at an independent shop, in the same way that Mitchell 1 and ALLDATA became the default repair-information layer over the previous two decades. The cited evidence makes this reachable, not aspirational, for three reasons. First, the category genuinely lacks an AI-native estimating standard today; the incumbents are workflow systems, not pricing models [Crunchbase]. Second, the company's framing of its moat around a proprietary repair-order dataset is the right shape of moat for this category, because pricing accuracy compounds with data volume in a way that workflow features do not [carvis.ai][StartupSeeker]. Third, Trucks Venture Capital's involvement signals that an investor with deep transportation pattern-matching saw enough founder and market signal to lead the round [Crunchbase].
Two or three growth scenarios, each named.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Embedded inside the shop OS | Carvis becomes the AI estimating engine licensed by one or more shop management incumbents | A commercial integration with Shopmonkey, Tekmetric, AutoLeap, or a regional chain | Incumbents need an AI story and would rather buy than build a learned pricing model [Crunchbase] |
| Direct-to-shop network | Carvis signs thousands of independent shops directly on a low-ACV subscription, building the data flywheel shop-by-shop | Demonstrated ROI in time-to-estimate and close rate at lighthouse shops | The company already publishes partner testimonials, indicating live deployments [carvis.ai] |
| Fleet and warranty data partner | Carvis becomes the pricing reference layer for fleet operators, extended warranty providers, and insurers pricing repair claims | A data licensing deal with a national fleet, warranty administrator, or claims platform | Trucks VC's network spans fleet and mobility, which is a natural channel into this buyer [Crunchbase] |
What compounding looks like. The flywheel in this category is data-driven and well understood. Each repair order processed through Carvis improves the pricing model, which tightens estimate accuracy, which improves shop close rates and customer trust, which earns the company more shops and more orders. The compounding is amplified if Carvis can layer parts pricing, labour-time, and warranty outcomes onto the same record, because each additional dimension of structured data raises switching costs for a shop that has come to rely on the system. The early presence of a testimonials page suggests at least the first turn of that flywheel is in motion [carvis.ai], although the company has not disclosed shop counts or estimate volumes that would let an outside analyst quantify it.
The size of the win. A useful comparable is Shopmonkey, which raised a $75M Series C in 2021 at a valuation reportedly in the high hundreds of millions, against a still-fragmented shop install base [Shopmonkey press, 2021]. Mitchell 1 and ALLDATA, the historical pricing-and-information incumbents, sit inside multi-billion-dollar parent companies (Snap-on and Solera respectively). If Carvis executes the embedded scenario above, an outcome in the several-hundred-million-dollar acquisition range looks credible (scenario, not a forecast). If it executes the direct-to-shop network scenario at scale and becomes the system of reference for AI estimating across tens of thousands of shops, the comparable shifts toward the vertical-SaaS public peers that trade at meaningful revenue multiples on durable subscription bases (scenario, not a forecast). Neither outcome is committed by the current public evidence; both are reachable from the position Carvis occupies today.
Data Accuracy: YELLOW -- Opportunity framing draws on confirmed company positioning and named comparables; specific scenario outcomes are explicitly labelled as scenarios rather than forecasts.
Sources
PUBLIC
[Crunchbase] Carvis.AI - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/carvis-ai
[PitchBook] Carvis 2026 Company Profile: Valuation, Funding & Investors | https://pitchbook.com/profiles/company/597745-81
[StartupSeeker] Carvis.AI - Funding: $1M+ | https://startup-seeker.com/company/carvis~ai
[carvis.ai] Carvis - AI Estimates for Auto Repair Shops (team page) | https://carvis.ai/team
[carvis.ai] Carvis app | https://app.carvis.ai/
[carvis.ai] Carvis testimonials | https://carvis.ai/testimonials/
[Crunchbase] Pre Seed Round - Carvis.AI - Crunchbase Funding Round Profile | https://www.crunchbase.com/funding_round/carvis-ai-pre-seed--d682150c
[Crunchbase] Carvis.AI - Profiles & Contacts | https://www.crunchbase.com/organization/carvis-ai/profiles_and_contacts
[Crunchbase] Jay Warida - CEO @ Carvis.AI - Crunchbase Person Profile | https://www.crunchbase.com/person/jay-warida
[Crunchbase] Carvis.AI - Contacts, Employees, Board Members, Advisors & Alumni | https://lb.crunchbase.com/organization/carvis-ai/people
[RocketReach] Jay Warida Email & Phone Number | Carvis.ai Co-Founder | CEO Contact Information | https://rocketreach.co/jay-warida-email_755712841
[MOGE] Carvis.AI product listing | https://moge.ai/product/carvisai
[LinkedIn] Carvis company page | https://www.linkedin.com/company/carvis
[Instagram] CARVIS (@carvis.ai) | https://www.instagram.com/carvis.ai/
[LANARS] Personal car expenses assistant - CARVIS (distinct entity) | https://lanars.com/cases/carvis
[Crunchbase] Carvis (parking lot management, distinct entity) | https://www.crunchbase.com/organization/carvis
[completeaitraining.com] Carvis tool listing | https://completeaitraining.com/ai-tools/carvis/
[aitechsuite.com] Carvis | AI Tech Suite | https://www.aitechsuite.com/tools/carvis.ai
[U.S. Bureau of Labor Statistics] Occupational Outlook Handbook: Automotive Service Technicians and Mechanics | https://www.bls.gov/ooh/installation-maintenance-and-repair/automotive-service-technicians-and-mechanics.htm
[Auto Care Association] Auto Care Factbook | https://www.autocare.org/research/factbook
Articles about Carvis.AI
- Carvis.AI Wants Every Independent Auto Shop to Quote Repairs Like a Dealer — The San Francisco pre-seed startup, backed by Trucks VC, is turning repair orders into pricing intelligence for a fragmented service market.