To Labs

Name: To Labs
Brand: To Labs
Rating: 2.5 (12 reviews)

Where creative AI gets to work

Est 2018|Valuation $28.3M|Raised $9.2M

LinkedIn 4K|Website|Web traffic 371K|FTEs 5|HQ Stockholm, 01, SE

MarketsContent Creation & Design Digital Asset Management (DAM)

Published Sep 26, 2026

01Common complaints

- Regulated or enterprise procurement-heavy organizations that require public trust documentation (SOC 2 Type II, ISO 27001, DPA, subprocessors, pen test summaries) should consider vendors with publicly documented artifacts. Kive may provide such artifacts privately, but they are not clearly public. - Brands that require pixel-perfect product accuracy (for example, medical devices or products with regulated labeling) where AI-induced alterations could present legal or reputational risk. - Organizations sensitive to credit-based billing risk: teams that have experienced issues with other credit-based AI tools (unexpected consumption, unclear upgrade flows) may encounter similar friction based on reported reviews. - Organizations that already operate an enterprise DAM and require the depth of governance, permissions, and integrations typically offered by enterprise DAM vendors.

Deal disqualifiers (poor fit)

Organizations that require publicly documented SOC 2/ISO 27001 certifications and SSO/SCIM prior to purchase.
Teams that need guaranteed product fidelity with minimal retries (e.g., regulated product labeling or strict packaging compliance).
Teams that require a mature mobile-first workflow.

Limitations acknowledged by vendor documentation

Kive troubleshooting documentation notes that the AI can make slight changes to product details and provides troubleshooting steps. This indicates that perfect fidelity is not guaranteed.

Limitations highlighted by customers and reviewers

- Output fidelity issues: some users report that generated images alter product details in undesirable ways. - Text rendering: reports of unreadable or warped text in generated images. - UX and stability: some reviewers report glitches and navigation difficulties. - Support: several reviews reference difficulty reaching human support and reliance on automated support. - Billing and credit transparency: complaints include unexpected charges and unclear credit usage.

Roadmap visibility

No public roadmap with dated commitments was found in accessible sources.

Why customers might leave / not purchase (from negative reviews)

Common reasons cited or implied by reviews and discussions:

Perceived mismatch between marketing examples and typical outputs.
Concerns about credits (burning credits on retries; unclear billing practices).
Demand for mobile app workflows (some reviewers reported a lack of satisfactory mobile experiences).

02Why they buy

Unique selling points supported by product documentation

Preset-based studio workflows: Kive documents guided, preset-driven workflows intended to simplify product-shot generation and reduce the need for deep prompt engineering.
Unified workflow: generation (image and video) and asset management (library, boards, AI tags) are provided within a single platform.

Claims that need validation

Marketing claims related to brand consistency and high precision in outputs (for example, phrases presented on vendor marketing pages such as claims about "on-brand" output or "incredible precision") were not independently validated in accessible sources and are contradicted in part by some user reports that describe fidelity issues.

03Field reports

Review platform availability (G2, Gartner Peer Insights, TrustRadius)

G2: Kive has a listing, but there are not enough reviews to provide robust buying insight.
Gartner Peer Insights: No accessible, product-specific Gartner Peer Insights page for Kive was located in searches; if present it may be limited or inaccessible.
TrustRadius: No meaningful Kive product review page was found in accessible results.
Trustpilot: Kive has a page with a small number of reviews.

Trustpilot (strong negative sentiment, billing/support issues)

Examples of negative feedback (direct quotes): - Negative review (Feb 10, 2026): "Appalling customer service. Mostly AI, rude, and almost impossible to get real human help. ... glitchy, results are poor, and there is very little transparency around credit usage or billing ... charged a substantial amount ... 'misleading and untrustworthy.'" - Negative review (Jan 14, 2026): "Not at all fit for purpose. Changes your products too much ... 'No app. Don't waste your money!!' ... calls it 'A SCAM' and says the visuals don't match advertised examples and that refund was denied." - Negative review (Oct 23, 2025): Reviewer states "guides are outdated" and "Very hard to navigate," but also notes they received "a full refund" and suggested trying again after upgrades. Pattern observed in these reviews centers on three themes: (1) perceived output quality not meeting advertised examples, (2) support experience, and (3) billing and credit transparency.

Community / social discussion (Reddit)

Kive-specific organic user discussion appears limited; several mentions appeared promotional or included responses from employees. - In small business and e-commerce subreddits, a commenter explicitly identifying as a Kive employee promoted the tool for AI product photography use cases and referenced a preset catalog. - A widely-shared post in a community forum mocked Kive advertising for photorealistic claims, implying that some marketing imagery can appear uncanny or low quality. This post is a perception signal rather than a measured review.

Why customers choose Kive (inferred from vendor documentation and discussions)

Potential advantages observed in vendor materials and discussions include:

Preset-driven product-shot generation intended to reduce the need for deeper prompt engineering.
Combined creation and organization capabilities in a single tool rather than requiring a separate generator and DAM.

Vendor-published / vendor-guided outcomes

Vendor documentation describes the "AI Product Shots" feature as capable of producing commercial-style images in a short amount of time and highlights preset-based studio workflows.

Critical evaluation: these are vendor-authored guides that demonstrate intended workflows but do not provide independent performance metrics (for example, operational retry rates, failure modes, or representative quality distributions).

Independent, quantified case studies

No robust third-party case studies with documented methodology and independently validated metrics were located in accessible sources.

04Best fit

Best-fit company sizes (practical estimate)

Given published pricing tiers and library limits:

SMB to mid-market organizations, with small to medium internal creative teams (solo contributors up to modest-sized teams).

Industries/verticals

Most aligned verticals include:

E-commerce and direct-to-consumer goods (beauty, wellness, food and beverage, accessories) where product visuals influence conversion.
Brands that produce frequent ads and seasonal variants.

Roles and personas

Decision makers: Head of Marketing, Creative Director, E-commerce Manager.
End users: Designers, content creators, performance marketers, brand managers.

Qualification criteria (good fit)

The organization already maintains clean product cutouts or high-quality product photos for use as generation inputs.
The team treats AI-generated images as variations and iterations rather than guaranteed replacements for high-end photography.
The organization values an integrated generation and asset-management workflow.

05The pitch

What the product does (objective value proposition)

Kive is a creative AI platform that combines: - AI product photography ("AI Product Shots") to generate studio- and lifestyle-style images from an existing product photo. - AI image/video generation and editing using a credit-based model. - A lightweight digital asset management (DAM)-style library with AI tagging, search, boards, versioning, and sharing. The product is positioned as a workflow tool for consumer brands and creative teams to generate visuals intended to align with brand guidelines with reduced turnaround time, and to organize and reuse assets in a searchable library rather than leaving assets distributed across drives and chat tools. Vendor messaging emphasizes rapid product-shot generation and outputs intended for brand consistency.

Target market & positioning

Based on Kive’s homepage and the "AI Product Shots" guide, the most explicit target segments appear to be: - Consumer brands producing frequent marketing and e-commerce visuals (ads, landing pages, social posts, marketplaces). - Teams that prefer preset-driven generation workflows to reduce reliance on prompt engineering. Kive also overlaps with: - Smaller DAM / moodboard tools (inspiration boards, collaboration, asset search). - AI product-photo generators (specialized tools such as Flair).

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

Download full report →

Published Sep 26, 2026

01Common complaints

Regulated or enterprise procurement-heavy organizations that require public trust documentation (SOC 2 Type II, ISO 27001, DPA, subprocessors, pen test summaries) should consider vendors with publicly documented artifacts. Kive may provide such artifacts privately, but they are not clearly public.
Brands that require pixel-perfect product accuracy (for example, medical devices or products with regulated labeling) where AI-induced alterations could present legal or reputational risk.
Organizations sensitive to credit-based billing risk: teams that have experienced issues with other credit-based AI tools (unexpected consumption, unclear upgrade flows) may encounter similar friction based on reported reviews.
Organizations that already operate an enterprise DAM and require the depth of governance, permissions, and integrations typically offered by enterprise DAM vendors.

Deal disqualifiers (poor fit)

Organizations that require publicly documented SOC 2/ISO 27001 certifications and SSO/SCIM prior to purchase.
Teams that need guaranteed product fidelity with minimal retries (e.g., regulated product labeling or strict packaging compliance).
Teams that require a mature mobile-first workflow.

Limitations acknowledged by vendor documentation

Kive troubleshooting documentation notes that the AI can make slight changes to product details and provides troubleshooting steps. This indicates that perfect fidelity is not guaranteed.

Limitations highlighted by customers and reviewers

Output fidelity issues: some users report that generated images alter product details in undesirable ways.
Text rendering: reports of unreadable or warped text in generated images.
UX and stability: some reviewers report glitches and navigation difficulties.
Support: several reviews reference difficulty reaching human support and reliance on automated support.
Billing and credit transparency: complaints include unexpected charges and unclear credit usage.

Roadmap visibility

No public roadmap with dated commitments was found in accessible sources.

Why customers might leave / not purchase (from negative reviews)

Common reasons cited or implied by reviews and discussions:

Perceived mismatch between marketing examples and typical outputs.
Concerns about credits (burning credits on retries; unclear billing practices).
Demand for mobile app workflows (some reviewers reported a lack of satisfactory mobile experiences).

02Why they buy

Unique selling points supported by product documentation

Preset-based studio workflows: Kive documents guided, preset-driven workflows intended to simplify product-shot generation and reduce the need for deep prompt engineering.
Unified workflow: generation (image and video) and asset management (library, boards, AI tags) are provided within a single platform.

Claims that need validation

Marketing claims related to brand consistency and high precision in outputs (for example, phrases presented on vendor marketing pages such as claims about "on-brand" output or "incredible precision") were not independently validated in accessible sources and are contradicted in part by some user reports that describe fidelity issues.

03Field reports

Review platform availability (G2, Gartner Peer Insights, TrustRadius)

G2: Kive has a listing, but there are not enough reviews to provide robust buying insight.
Gartner Peer Insights: No accessible, product-specific Gartner Peer Insights page for Kive was located in searches; if present it may be limited or inaccessible.
TrustRadius: No meaningful Kive product review page was found in accessible results.
Trustpilot: Kive has a page with a small number of reviews.

Trustpilot (strong negative sentiment, billing/support issues)

Examples of negative feedback (direct quotes):

Negative review (Feb 10, 2026): "Appalling customer service. Mostly AI, rude, and almost impossible to get real human help. ... glitchy, results are poor, and there is very little transparency around credit usage or billing ... charged a substantial amount ... 'misleading and untrustworthy.'"
Negative review (Jan 14, 2026): "Not at all fit for purpose. Changes your products too much ... 'No app. Don't waste your money!!' ... calls it 'A SCAM' and says the visuals don't match advertised examples and that refund was denied."
Negative review (Oct 23, 2025): Reviewer states "guides are outdated" and "Very hard to navigate," but also notes they received "a full refund" and suggested trying again after upgrades.

Pattern observed in these reviews centers on three themes: (1) perceived output quality not meeting advertised examples, (2) support experience, and (3) billing and credit transparency.

Community / social discussion (Reddit)

Kive-specific organic user discussion appears limited; several mentions appeared promotional or included responses from employees.

In small business and e-commerce subreddits, a commenter explicitly identifying as a Kive employee promoted the tool for AI product photography use cases and referenced a preset catalog.
A widely-shared post in a community forum mocked Kive advertising for photorealistic claims, implying that some marketing imagery can appear uncanny or low quality. This post is a perception signal rather than a measured review.

Why customers choose Kive (inferred from vendor documentation and discussions)

Potential advantages observed in vendor materials and discussions include:

Preset-driven product-shot generation intended to reduce the need for deeper prompt engineering.
Combined creation and organization capabilities in a single tool rather than requiring a separate generator and DAM.

Vendor-published / vendor-guided outcomes

Vendor documentation describes the "AI Product Shots" feature as capable of producing commercial-style images in a short amount of time and highlights preset-based studio workflows.

Independent, quantified case studies

No robust third-party case studies with documented methodology and independently validated metrics were located in accessible sources.

04Best fit

Best-fit company sizes (practical estimate)

Given published pricing tiers and library limits:

SMB to mid-market organizations, with small to medium internal creative teams (solo contributors up to modest-sized teams).

Industries/verticals

Most aligned verticals include:

E-commerce and direct-to-consumer goods (beauty, wellness, food and beverage, accessories) where product visuals influence conversion.
Brands that produce frequent ads and seasonal variants.

Roles and personas

Decision makers: Head of Marketing, Creative Director, E-commerce Manager.
End users: Designers, content creators, performance marketers, brand managers.

Qualification criteria (good fit)

The organization already maintains clean product cutouts or high-quality product photos for use as generation inputs.
The team treats AI-generated images as variations and iterations rather than guaranteed replacements for high-end photography.
The organization values an integrated generation and asset-management workflow.

05The pitch

What the product does (objective value proposition)

Kive is a creative AI platform that combines:

AI product photography ("AI Product Shots") to generate studio- and lifestyle-style images from an existing product photo.
AI image/video generation and editing using a credit-based model.
A lightweight digital asset management (DAM)-style library with AI tagging, search, boards, versioning, and sharing.

The product is positioned as a workflow tool for consumer brands and creative teams to generate visuals intended to align with brand guidelines with reduced turnaround time, and to organize and reuse assets in a searchable library rather than leaving assets distributed across drives and chat tools. Vendor messaging emphasizes rapid product-shot generation and outputs intended for brand consistency.

Target market & positioning

Based on Kive’s homepage and the "AI Product Shots" guide, the most explicit target segments appear to be:

Consumer brands producing frequent marketing and e-commerce visuals (ads, landing pages, social posts, marketplaces).
Teams that prefer preset-driven generation workflows to reduce reliance on prompt engineering.

Kive also overlaps with:

Smaller DAM / moodboard tools (inspiration boards, collaboration, asset search).
AI product-photo generators (specialized tools such as Flair).

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

Download full report →