PARES AI

Cover Block

PUBLIC

Attribute	Value
Company Name	PARES AI
Tagline	AI platform for CRE brokers: lead sourcing to deal closing [extruct.ai, 2025]
Headquarters	Pasadena, CA, USA [Y Combinator, 2025]
Founded	2025 [PitchBook, 2026]
Stage	Seed [extruct.ai, 2025]
Business Model	SaaS
Industry	Proptech
Technology	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding Label	Undisclosed (total disclosed ~$1,000,000) [extruct.ai, 2025]

Links

PUBLIC

• Website: https://www.pares.ai
• LinkedIn: https://www.linkedin.com/company/pares-ai
• YouTube: https://www.youtube.com/watch?v=_LA9YHCv-js

Executive Summary

PUBLIC PARES AI is an early-stage Y Combinator-backed startup building an integrated software platform for commercial real estate brokers, using artificial intelligence to automate workflows from lead generation to deal closing [Y Combinator, 2025]. The company, founded in 2025 by solo founder Zihao Wang, is attempting to address a market where manual research and fragmented tools still dominate, positioning its AI agents for seller recommendations and underwriting as a potential wedge [extruct.ai, 2025]. The core product is described as an all-in-one system that combines prospecting, deal tracking, underwriting, and marketing automation, aiming to consolidate a broker's toolkit [Y Combinator, 2025].

The founding team's background is not detailed in public sources, and the company's disclosed traction consists of a single, unverified revenue figure of $440,000 reported for September 2025 [getlatka.com, Sep 2025]. PARES AI has raised an estimated $1 million in a seed round, with Y Combinator as the only publicly confirmed investor [extruct.ai, 2025]. Over the next 12-18 months, the key milestones to watch are the validation of its revenue claims through customer case studies, the articulation of a clear go-to-market motion beyond the YC network, and the demonstration of product differentiation in a proptech landscape crowded with point solutions. Data Accuracy: YELLOW -- Core company description corroborated by multiple sources; key traction and team details rely on limited or single-source reporting.

Taxonomy Snapshot

Axis	Value
Stage	Seed
Business Model	SaaS
Industry / Vertical	Proptech
Technology Type	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding	Undisclosed (total disclosed ~$1,000,000)

Company Overview

PUBLIC

PARES AI was founded in 2025 by solo founder Zihao Wang, launching as a participant in the Y Combinator S25 batch [Y Combinator, 2025]. The company is headquartered in Pasadena, California, at 2820 East California Boulevard [extruct.ai, 2025]. The founding narrative centers on streamlining the historically manual workflows of commercial real estate brokerage through an integrated AI platform, a concept developed during the YC program.

Key milestones are concentrated in the company's first year of operation. The primary public event is its Y Combinator launch, which includes a public launch video and company listing [YouTube, 2025] [Y Combinator, 2025]. The company closed a seed funding round in 2025, raising $1 million, though the lead investor and valuation remain undisclosed [extruct.ai, 2025]. A revenue figure of $440,000 was reported for September 2025 by a single third-party source [getlatka.com, Sep 2025].

Data Accuracy: YELLOW -- Core founding and location data confirmed by YC and company aggregator; funding amount corroborated by one source; revenue figure is single-source and unverified.

Product and Technology

MIXED The product is an integrated workflow platform, not a collection of point tools. PARES AI presents a suite of AI agents and automation tools designed to address specific, time-consuming tasks across the commercial real estate brokerage lifecycle [extruct.ai, 2025]. The platform's stated goal is to consolidate functions that typically require multiple software subscriptions and manual data entry into a single environment.

Its core features, as described in public materials, target three primary workflow stages. For lead generation, the platform includes AI Likely Seller Recommendations and skip-tracing capabilities [extruct.ai, 2025]. For deal analysis, an AI Underwriting Agent is offered to assist with financial modeling and valuation [extruct.ai, 2025]. Finally, for marketing and closing, the company lists Automated OM/BOV Generation for creating offering memorandums and brochures [extruct.ai, 2025]. A company overview also mentions a CRM with real-time transaction data and an AI Marketing Maker, suggesting an integrated system for managing the pipeline from initial contact to final documentation [extruct.ai, 2025].

The underlying technology stack is not detailed in public sources. The company's messaging emphasizes an "all-in-one" platform built specifically for CRE, which implies a focus on workflow integration and domain-specific data structuring over novel model development. No public technical documentation, API details, or architecture diagrams were available for review at the time of writing.

Data Accuracy: YELLOW -- Product claims are consistently described across the company's Y Combinator profile and third-party aggregators, but lack independent user validation or detailed technical corroboration.

Market Research

PUBLIC

The commercial real estate brokerage market represents a large, fragmented, and historically inefficient service layer, a profile that invites software automation but has proven resistant to it.

No third-party TAM, SAM, or SOM figures specific to PARES AI's target market are cited in available sources. For context, the broader U.S. commercial real estate brokerage and services market was valued at approximately $109 billion in 2023, according to IBISWorld [IBISWorld, 2023]. This analogous figure provides a sense of the total addressable service revenue, though the software opportunity for workflow automation represents a smaller, adjacent slice. Demand for such tools is driven by several tailwinds. Transaction volumes and property values in commercial real estate are sensitive to interest rate cycles, but the underlying operational inefficiency of brokerage workflows, which rely heavily on manual research, data aggregation, and relationship management, creates a persistent pain point. The proliferation of property and ownership data, from county assessor records to specialized CRE databases, has increased the volume of information brokers must process, making manual methods increasingly untenable.

Key adjacent markets include commercial real estate data and analytics platforms, such as CoStar and Reonomy, which focus on providing the raw data rather than automating the broker's end-to-end workflow. Another adjacent sector is residential proptech, where companies like Compass have invested heavily in agent-facing software tools, demonstrating a model for tech-enabled brokerage services. The primary substitute market remains the status quo: brokers using a patchwork of spreadsheets, generic CRM software, public records searches, and manual underwriting models.

Regulatory forces are a constant consideration, particularly around data privacy for skip-tracing activities and the accuracy requirements for underwriting and offering memorandum generation. Macro forces, namely the cost of capital and commercial transaction liquidity, directly impact broker activity levels and their willingness to invest in new software. A sustained high-interest-rate environment could pressure broker commissions, making cost-saving and efficiency tools more attractive, but could also reduce the total pool of active brokers.

U.S. CRE Brokerage & Services Market (2023) | 109 | $B

The cited market size, while not specific to PARES AI's software wedge, underscores the scale of the underlying service economy the platform aims to make more efficient. The absence of a direct, cited TAM for AI-powered brokerage workflow software suggests the category is still emerging and not yet broadly tracked by market research firms.

Data Accuracy: YELLOW -- Market sizing figure is from an analogous, broader industry report [IBISWorld, 2023]; no specific TAM for the company's product category is publicly available.

Competitive Landscape

MIXED PARES AI enters a commercial real estate technology market defined by established, category-specific incumbents and a growing wave of AI-native challengers, but its direct, named competition is not yet visible in public sources.

The competitive map must be drawn from the broader category. The landscape for commercial real estate workflow software is segmented. Incumbent platforms like CoStar Group, which dominates market data and listings, and CRE-specific CRM and deal management tools like RealNex and Apto, represent the entrenched, high-touch alternatives. A newer wave of challengers includes AI-driven analytics and prospecting startups such as Cherre and Skyline AI, which focus on data aggregation and predictive insights. PARES AI's positioning as an "all-in-one" platform from prospecting to closing suggests it competes across these segments by bundling capabilities that are typically purchased separately. Adjacent substitutes include general-purpose sales intelligence tools like ZoomInfo or Apollo.io, which brokers may adapt for lead generation, and the persistent, low-tech alternative of manual research and spreadsheets.

The subject's claimed edge rests on integration and automation breadth within a single platform, a wedge supported by its Y Combinator affiliation for early-stage talent and go-to-market guidance [Y Combinator, 2025]. The durability of this edge is perishable, however, as it depends on execution velocity. An integrated suite is only defensible if the individual modules,like AI underwriting or automated document generation,achieve parity with or superiority over best-in-class point solutions. The company's early stage and solo-founder structure suggest capital and talent are current constraints, not edges, against better-funded incumbents.

Exposure is highest in two areas. First, in data depth and accuracy, where incumbents like CoStar have decades of proprietary data collection and verification processes that a new entrant cannot quickly replicate. Second, in sales channel ownership; large brokerages have entrenched relationships with existing vendors, creating high switching costs. PARES AI's go-to-market, targeting individual brokers or small teams, may circumvent this initially but hits a scaling ceiling at the enterprise level where procurement and integration become major hurdles.

The most plausible 18-month scenario involves market validation through niche adoption. The winner in this scenario is a company like Apto or RealNex if they successfully acquire or build comparable AI automation features into their established CRM platforms, leveraging existing customer relationships to outpace pure-play startups. The loser is any undifferentiated, AI-enabled workflow tool that fails to demonstrate superior workflow capture or data accuracy, becoming merely a feature within a larger ecosystem. For PARES AI, the path to avoiding the latter outcome hinges on proving that its AI agents deliver materially better seller recommendations or underwriting speed than the adapted use of general tools or the gradual digitization efforts of the incumbents.

Data Accuracy: YELLOW -- Competitive analysis is inferred from product claims and market category; no direct competitor citations are available.

Opportunity

PUBLIC

If PARES AI can successfully embed its workflow automation into the daily operations of commercial real estate brokers, it stands to capture a meaningful share of the software spend in a multi-trillion-dollar asset class that remains largely paper-driven and relationship-based.

The headline opportunity is to become the default operating system for the modern commercial real estate brokerage. The company's positioning as an "all-in-one AI platform" covering the full deal lifecycle, from lead sourcing to closing, suggests a platform play rather than a point solution [extruct.ai, 2025]. This outcome is reachable because the initial wedge,automating time-intensive research and underwriting,targets a well-documented pain point. Brokers reportedly spend up to 95% of their time on research and administrative tasks, a massive inefficiency that creates a clear opening for a productivity tool [extruct.ai, 2025]. By starting there, PARES AI can build a beachhead of users whose daily workflow is then captured within its CRM and marketing modules, creating a natural path to platform dominance.

Growth is not a single path. The company's trajectory will likely be defined by which of several plausible scenarios materializes first.

Scenario	What happens	Catalyst	Why it's plausible
Brokerage Franchise Adoption	A major national or regional CRE franchise (e.g., CBRE, JLL, Colliers) signs a master agreement to roll out PARES AI to its thousands of independent agents.	A successful pilot with a top-producing team within a franchise, demonstrating measurable time savings and deal velocity.	The Y Combinator network provides access to decision-makers at scale-ups and enterprises, a proven channel for B2B SaaS adoption [Y Combinator].
Data Network Effect	The platform's value shifts from workflow tools to proprietary market intelligence, as aggregated, anonymized deal data from users creates a superior comps database.	The launch of a "Market Pulse" or analytics module that leverages pooled transaction data, creating a product users cannot get elsewhere.	The product roadmap already includes AI-driven recommendations based on transaction data, a foundational step towards a data asset [extruct.ai, 2025].
Vertical Expansion into Investing	The toolset expands beyond brokerage to serve the buy-side directly, attracting small-to-mid-sized investment firms and family offices.	The introduction of fund management or portfolio monitoring features built on the same underwriting and tracking engine.	The platform is already marketed to both "brokers and investors," indicating a built-in expansion vector from its initial design [Y Combinator, 2025].

Compounding for PARES AI would look like a classic data and workflow flywheel. Each new broker using the platform contributes more deal flow and outcome data. This data improves the accuracy of the AI's seller recommendations and underwriting models. Better models attract more users and increase engagement, which in turn generates more data. Early signs of this loop are suggested in the product's described capabilities, such as "AI Likely Seller Recommendations" that presumably improve with more signals [extruct.ai, 2025]. Furthermore, as brokers manage more of their pipeline within the CRM, switching costs rise, creating a distribution lock-in that protects the customer base.

The size of the win can be framed by looking at a public comparable. CoStar Group, a provider of commercial real estate information and analytics, had a market capitalization of approximately $31 billion as of early 2026. While CoStar's model is information-centric, its success demonstrates the immense value of being an essential, scaled platform in commercial real estate. If PARES AI executes on the brokerage franchise adoption scenario and captures a material portion of the agent productivity software segment, a valuation in the low single-digit billions is a plausible outcome (scenario, not a forecast). This represents a significant multiple on the capital invested to date.

Data Accuracy: YELLOW -- Opportunity analysis is based on cited product capabilities and market logic; specific growth catalysts and comparable valuations are extrapolated from public positioning.

Sources

PUBLIC

1. [extruct.ai, 2025] PARES AI hub | https://www.extruct.ai/hub/pares-ai/

2. [Y Combinator, 2025] PARES AI: Helping commercial real estate brokers find and close more deals. | https://www.ycombinator.com/companies/pares-ai

3. [YouTube, 2025] PARES AI launch video | https://www.youtube.com/watch?v=_LA9YHCv-js

4. [PitchBook, 2026] PARES AI 2026 Company Profile | https://pitchbook.com/profiles/company/898234-03

5. [getlatka.com, Sep 2025] How PARES AI hit $440K revenue | https://getlatka.com/companies/pares.ai

6. [IBISWorld, 2023] U.S. Commercial Real Estate Brokerage & Services Industry Report | https://www.ibisworld.com/united-states/market-research-reports/commercial-real-estate-brokerage-services-industry/

7. [LinkedIn, 2026] PARES (YC S25) | https://www.linkedin.com/company/pares-ai