New Generation

Name: New Generation
Brand: New Generation

AI Native

An AI agent for your brand.

Est 2024|Raised $4.5M

LinkedIn 2K|Website|Web traffic 9K|FTEs 28|HQ San Francisco, us

Published Jul 14, 2026

01Common complaints

1. You require verified enterprise compliance now (SOC 2 Type II, ISO 27001) and cannot accept documentation provided upon request or a vendor roadmap. 2. You require transparent, self-serve pricing and low-commitment monthly models. 3. You already operate a mature enterprise search/discovery stack (Bloomreach, Coveo, Constructor) and your main need is incremental tuning rather than a new agent layer. 4. You require deterministic, fully auditable outputs for all customer interactions (generative interfaces introduce probabilistic behavior). 5. You lack internal ownership for data quality (attribute enrichment, taxonomy). Agent-readiness typically requires catalog data quality work.

Disqualifiers (poor fit)

Very small catalogs or early-stage DTC businesses where procurement overhead outweighs benefit
Regulated environments requiring publicly documented compliance artifacts (unless vendor provides them privately)
Teams that require fully self-serve onboarding and transparent public pricing

Publicly known / acknowledged limitations

Some Kepler pages list features as coming soon; a comprehensive public roadmap was not identified.

Key limitations inferred from available evidence

Review scarcity: a lack of independent, public reviews increases diligence risk
Security documentation scarcity: procurement friction for enterprise buyers
Naming collisions: "New Generation" and "Kepler" names overlap with other entities online, complicating diligence and search

02Why they buy

Unique selling points that appear meaningfully differentiated

The vendor’s messaging prioritizes AI agent and assistant discovery scenarios rather than positioning the product as a retrofitted search layer.
The vendor exposes an agent-readiness metric (Agent Score) intended to provide a single measure for assessing preparedness for agent interactions; the value of this depends on the availability and transparency of the methodology.

Where the unique selling point needs validation

Claims of minimal engineering effort or one-click activation appear plausible for demos but should be validated for enterprise-grade integrations where data quality, brand control, and realtime constraints matter.

03Field reports

Availability reality check (important)

New Generation appears early-stage and does not have a visible footprint on major review platforms (G2, TrustRadius, Gartner Peer Insights, Capterra) in the accessible sources used for this report. - Public searches for New Generation (vendor website) on common review platforms did not surface an accessible vendor review page. Consequently, independent customer reviews are scarce in publicly accessible sources. Available signals are limited to company announcements, investor communications, and sparse community discussion complicated by name collisions.

Quotes and sentiment (what is publicly available)

Investor- and partner-originated statements in press materials describe the product in positive terms; these are vendor- or investor-originated claims and do not substitute for independent end-user reviews.
Social posts and vendor marketing highlight demos with a number of brands and claim outcome improvements; such posts are marketing context and are not independently validated.

Why customers might choose New Generation (inferred)

Path to implement conversational commerce interfaces without replacing the primary storefront
Desire to prepare catalog and endpoints for discovery by AI agents
Interest in measuring a new referral channel (AI-referred traffic and agent interactions)

Why customers might avoid or churn (likely concerns)

Based on common procurement criteria for early-stage commerce infrastructure, potential deal-breakers include:

Security and compliance maturity insufficient for enterprise procurement
Unclear ROI or measurement methodology for agent-readiness metrics
Brand risk from inaccurate or ungrounded generative outputs
Integration effort being larger than vendor marketing suggests
Pricing opacity and negotiation due to demo-driven sales

Public case studies

No formal customer case studies with named customers and independently verifiable metrics were found on New Gen’s public site in the sources reviewed.

Vendor-published benchmarks (not independent)

New Gen’s Kepler product references an "Agent Score" benchmark with bands and variables such as speed, effort score, failure rate, and task durations. Critical evaluation: - The methodology and reproducibility of the Agent Score are not clearly documented in public sources. - Without transparent sampling and technical details, the score is difficult to use as a procurement KPI or to compare across retailers reliably. Buyer action: request a technical paper or documented methodology that covers: - Tasks performed (e.g., checkout, search, filtering) - Bot/agent configuration used for testing - Handling of dynamic content, geolocation, personalization, and sampling details

04Best fit

Company size

Likely best fit:

Mid-market to enterprise retailers with sizable catalogs and meaningful web revenue
Organizations with dedicated e-commerce, merchandising, and analytics teams

Industries / verticals

Marketing materials reference verticals such as fashion, CPG, electronics, home goods, and travel; the vendor has also highlighted fashion use cases in public announcements.

Roles and personas

Decision makers: VP/Head of E-commerce, CMO, Digital Experience leaders
Influencers: Head of Search/Merchandising, Data/Analytics, Engineering leadership, Security/IT
Users: Merchandisers, content teams, conversion-rate-optimization (CRO) teams

Pain points New Gen targets

Difficulty finding products using traditional keyword search and filters
Desire to be discoverable by agent or assistant-based channels
Catalog data not structured for agent interfaces
Need for new insights from conversational queries beyond clickstream analytics

05The pitch

What the product does (objective value proposition)

New Generation (New Gen) describes itself as infrastructure intended to integrate AI capabilities into e-commerce. The product materials state the following objectives: - Convert a brand’s product catalog data into structured, machine-readable data - Provide natural-language and generative shopping interfaces for customers - Make a brand more discoverable and accessible to AI systems and agents - Offer a parallel AI interface (often implemented as an AI-specific subdomain such as ai.<brand>) that operates alongside an existing storefront and is presented as not requiring a full rebuild The vendor’s public materials list capabilities such as natural-language search, on-demand generation of product and content experiences, generative outputs aligned to brand guidelines (marketing claim), catalog-to-AI connectivity, and rapid activation (marketing claim) of an AI subdomain.

Target market and positioning

New Gen targets mid-market to enterprise e-commerce brands. Public messaging references larger or well-known brands. The vendor emphasizes use cases for organizations that: - Have substantial traffic and catalog complexity - Are focused on conversion and merchandising outcomes - Anticipate referral traffic from AI assistants or consider AI agents a potential channel for discovery Marketing materials emphasize a shift from traditional SEO/keyword navigation toward conversational and agent-driven discovery.

Get a comprehensive analysis of New Generation including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Jul 14, 2026

01Common complaints

You require verified enterprise compliance now (SOC 2 Type II, ISO 27001) and cannot accept documentation provided upon request or a vendor roadmap.
You require transparent, self-serve pricing and low-commitment monthly models.
You already operate a mature enterprise search/discovery stack (Bloomreach, Coveo, Constructor) and your main need is incremental tuning rather than a new agent layer.
You require deterministic, fully auditable outputs for all customer interactions (generative interfaces introduce probabilistic behavior).
You lack internal ownership for data quality (attribute enrichment, taxonomy). Agent-readiness typically requires catalog data quality work.

Disqualifiers (poor fit)

Very small catalogs or early-stage DTC businesses where procurement overhead outweighs benefit
Regulated environments requiring publicly documented compliance artifacts (unless vendor provides them privately)
Teams that require fully self-serve onboarding and transparent public pricing

Publicly known / acknowledged limitations

Some Kepler pages list features as coming soon; a comprehensive public roadmap was not identified.

Key limitations inferred from available evidence

Review scarcity: a lack of independent, public reviews increases diligence risk
Security documentation scarcity: procurement friction for enterprise buyers
Naming collisions: "New Generation" and "Kepler" names overlap with other entities online, complicating diligence and search

02Why they buy

Unique selling points that appear meaningfully differentiated

The vendor’s messaging prioritizes AI agent and assistant discovery scenarios rather than positioning the product as a retrofitted search layer.
The vendor exposes an agent-readiness metric (Agent Score) intended to provide a single measure for assessing preparedness for agent interactions; the value of this depends on the availability and transparency of the methodology.

Where the unique selling point needs validation

Claims of minimal engineering effort or one-click activation appear plausible for demos but should be validated for enterprise-grade integrations where data quality, brand control, and realtime constraints matter.

03Field reports

Availability reality check (important)

New Generation appears early-stage and does not have a visible footprint on major review platforms (G2, TrustRadius, Gartner Peer Insights, Capterra) in the accessible sources used for this report.

Public searches for New Generation (vendor website) on common review platforms did not surface an accessible vendor review page.

Consequently, independent customer reviews are scarce in publicly accessible sources. Available signals are limited to company announcements, investor communications, and sparse community discussion complicated by name collisions.

Quotes and sentiment (what is publicly available)

Investor- and partner-originated statements in press materials describe the product in positive terms; these are vendor- or investor-originated claims and do not substitute for independent end-user reviews.
Social posts and vendor marketing highlight demos with a number of brands and claim outcome improvements; such posts are marketing context and are not independently validated.

Why customers might choose New Generation (inferred)

Path to implement conversational commerce interfaces without replacing the primary storefront
Desire to prepare catalog and endpoints for discovery by AI agents
Interest in measuring a new referral channel (AI-referred traffic and agent interactions)

Why customers might avoid or churn (likely concerns)

Based on common procurement criteria for early-stage commerce infrastructure, potential deal-breakers include:

Security and compliance maturity insufficient for enterprise procurement
Unclear ROI or measurement methodology for agent-readiness metrics
Brand risk from inaccurate or ungrounded generative outputs
Integration effort being larger than vendor marketing suggests
Pricing opacity and negotiation due to demo-driven sales

Public case studies

No formal customer case studies with named customers and independently verifiable metrics were found on New Gen’s public site in the sources reviewed.

Vendor-published benchmarks (not independent)

New Gen’s Kepler product references an "Agent Score" benchmark with bands and variables such as speed, effort score, failure rate, and task durations.

Critical evaluation:

The methodology and reproducibility of the Agent Score are not clearly documented in public sources.
Without transparent sampling and technical details, the score is difficult to use as a procurement KPI or to compare across retailers reliably.

Buyer action: request a technical paper or documented methodology that covers:

Tasks performed (e.g., checkout, search, filtering)
Bot/agent configuration used for testing
Handling of dynamic content, geolocation, personalization, and sampling details

04Best fit

Company size

Likely best fit:

Mid-market to enterprise retailers with sizable catalogs and meaningful web revenue
Organizations with dedicated e-commerce, merchandising, and analytics teams

Industries / verticals

Marketing materials reference verticals such as fashion, CPG, electronics, home goods, and travel; the vendor has also highlighted fashion use cases in public announcements.

Roles and personas

Decision makers: VP/Head of E-commerce, CMO, Digital Experience leaders
Influencers: Head of Search/Merchandising, Data/Analytics, Engineering leadership, Security/IT
Users: Merchandisers, content teams, conversion-rate-optimization (CRO) teams

Pain points New Gen targets

Difficulty finding products using traditional keyword search and filters
Desire to be discoverable by agent or assistant-based channels
Catalog data not structured for agent interfaces
Need for new insights from conversational queries beyond clickstream analytics

05The pitch

What the product does (objective value proposition)

New Generation (New Gen) describes itself as infrastructure intended to integrate AI capabilities into e-commerce. The product materials state the following objectives:

Convert a brand’s product catalog data into structured, machine-readable data
Provide natural-language and generative shopping interfaces for customers
Make a brand more discoverable and accessible to AI systems and agents
Offer a parallel AI interface (often implemented as an AI-specific subdomain such as ai.<brand>) that operates alongside an existing storefront and is presented as not requiring a full rebuild

The vendor’s public materials list capabilities such as natural-language search, on-demand generation of product and content experiences, generative outputs aligned to brand guidelines (marketing claim), catalog-to-AI connectivity, and rapid activation (marketing claim) of an AI subdomain.

Target market and positioning

New Gen targets mid-market to enterprise e-commerce brands. Public messaging references larger or well-known brands. The vendor emphasizes use cases for organizations that:

Have substantial traffic and catalog complexity
Are focused on conversion and merchandising outcomes
Anticipate referral traffic from AI assistants or consider AI agents a potential channel for discovery

Marketing materials emphasize a shift from traditional SEO/keyword navigation toward conversational and agent-driven discovery.

Get a comprehensive analysis of New Generation including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →