Bodyguard

Name: Bodyguard
Brand: Bodyguard

All-in-one moderation and audience insights solution

Est 2018|Valuation $31.6M|Raised $11.4M|27 Ref Customers

LinkedIn 9K|Web traffic 8K|FTEs 36|HQ Paris, Île-de-France, FR

MarketsSocial Listening & Consumer Intelligence

Published Oct 15, 2026

01Common complaints

1) An organization needs publicly available enterprise compliance certificates (SOC 2 Type II / ISO 27001) without executing an NDA - If a procurement process requires public certificates and reports upfront, Bodyguards publicly accessible materials may not be sufficient. 2) The organization wants an API-only, build-your-own pipeline rather than a dashboarded suite - Some organizations prefer model-provider APIs and custom workflows, particularly if they have an in-house ML/Trust & Safety engineering team. 3) The core issue is voice chat toxicity - Specialized vendors focused on voice-native detection may provide better coverage for voice-specific harms. 4) Budget is constrained and only basic comment filtering is required - Native platform moderation and lightweight keyword automation may be more cost-effective for simple needs. 5) The buyer requires extensive independent user reviews as part of evaluation - Limited presence on major B2B review platforms reduces availability of peer-review validation.

Deal disqualifiers

Buyers requiring publicly available SOC 2/ISO certifications before engagement
Teams that require extensive public developer-community validation and published benchmarks
Very small businesses with low moderation volume where cost may be disproportionate to need

Known limitations from public information

Lower tiers do not include all languages or custom classifications by default.
Limited publicly available information on SOC 2 / ISO certifications and detailed security controls.

Roadmap signals

Partner materials note exploration of large language model innovations; no public timeline for roadmap items was found.

Scale limitations

No explicit public documentation of maximum throughput thresholds; buyers should validate:
- Maximum requests per second for the API
- Rate limits
- Expected latency under peak load
- Multi-region and failover posture

API/integration constraints

Social platforms impose their own API limits and permission scopes; available moderation actions vary by platform.

Why customers might leave (inferred)

Because public churn and negative reviews are scarce, churn reasons are inferred from category norms:

Cost escalation with volume-based pricing
False positives or false negatives that affect brand voice or user experience
Procurement requirements for data residency or security certifications that are not publicly documented
Integration complexity for proprietary platforms

02Why they buy

Differentiators supported by available evidence

Published entry pricing in EUR (some Trust & Safety competitors are primarily sales-led with less public pricing)
Multi-platform integrations including major social platforms and Discord, plus API/webhooks for owned platforms
Emphasis on near-real-time moderation and a centralized dashboard for multi-channel oversight

Claims that require more verification

Sub-100 ms moderation latency and large-scale throughput: plausible but buyers should request latency SLOs by region, benchmark methodology, and false positive/false negative measurements by language.

Where competitors may have an advantage

Spectrum Labs: deeper Trust & Safety and compliance positioning
Modulate: voice-native moderation
API-first vendors: flexibility and potential cost control for engineering-led organizations

Why customers chose Bodyguard (inferred)

Based on positioning, testimonials, and case materials:

Proactive or pre-publish moderation capabilities
Multi-platform coverage with a single dashboard
Ability to tune rules and classifications
Hybrid automated + human review approach to manage false positives

03Field reports

Important limitation: lack of mainstream B2B review coverage

There is limited presence of Bodyguard reviews on major B2B review platforms discoverable via straightforward searches. This reduces the availability of broad, comparable end-user feedback.

Vendor-published testimonials (positive)

- A media organization states Bodyguard helped strengthen online protection strategy and maintain a moderated discussion space. - A major sports organization describes measuring hateful messages and processing them within seconds. - A global social app highlights scaling moderation for a worldwide audience. - A public broadcaster mentions identifying crisis typologies and supporting legal processes. These are vendor-published endorsements and may reflect curated or selected outcomes.

Independent/third-party references (contextual, not product reviews)

Media coverage notes event- and federation-level usage of protection tools based on Bodyguard.ai, with references to low-latency analysis and language updates.
A partner-published case story references high-volume comment processing and performance figures; these partner materials were not independently audited during this review.

Community/social discussions (negative/critical)

No substantive Reddit or Hacker News threads focused on Bodyguard.ai as a product were found during this research run. This suggests the product may be discussed primarily in enterprise or private channels rather than public developer communities.

What customers say they achieved (metrics)

Commonly cited, vendor/partner-published metrics are described in broad terms:

Real-time analysis of very large comment volumes
Processing latency framed as low, near-real-time response

Critical note: these performance metrics are not accompanied by a detailed methodology, test harness, or independent benchmarking in the reviewed materials.

1) Partner-published case story

What is claimed: - Industrialized, containerized deployment using managed Kubernetes and common messaging/caching patterns - Comment processing reported with low, near-real-time latency - Scale described as analysis of very large comment volumes - Growth and early revenue figures are referenced in partner materials Critical evaluation: - This is a partner-published case study and presents positive outcomes. - Metrics in the case study are not independently audited; latency and throughput measurement methodologies are not disclosed. - The case study provides signals of infrastructure maturity (managed Kubernetes, message queuing, caching) and an example deployment architecture.

2) Vendor customer summaries (gaming context)

What is claimed:

Chat moderation support in a gaming context

Critical evaluation:

These items are vendor-hosted customer summaries and likely curated.
Missing details often include baseline comparisons, experimental design, false positive rates, long-term retention impact, and cost-to-moderate metrics.

3) Sports event context (major sports event / national federation)

What is claimed:

Players and officials could opt into social-account protection during event windows
Real-time moderation with low-latency analysis and linguistic updates

Critical evaluation:

Media coverage supports event-level usage but relies on statements from the federation or vendor.
The coverage supports that Bodyguard has been used in high-profile sports contexts.

04Best fit

Company sizes (practical ranges)

Mid-market to enterprise is the clearest fit based on entry price and a sales-led motion.
Likely sweet spot: brands and platforms with meaningful UGC volumes but without a fully built internal Trust & Safety engineering organization.

Industries / verticals

Strong signals from customer materials:

Media and entertainment (broadcasters, publishers)
Sports teams, leagues, and events
Gaming (chat moderation, Discord/community)
Consumer brands with reputational risk in comments

Roles and personas

Decision makers:

Head of Trust & Safety
Head of Community / Social Media
Brand reputation / Communications leadership
Product leadership for UGC features

Day-to-day users:

Community moderators
Social media managers
Trust & Safety analysts

Best-fit pain points

High toxicity or spam in comments that damages brand perception
Need to moderate multiple platforms without separate tools
Sudden surges (live events, launches) that overwhelm human teams
Need for reporting and analytics to assess community health

05The pitch

What the product does (objective value proposition)

Bodyguard is a B2B content moderation and audience-insights platform designed to enable brands and platforms to monitor and act on toxic or otherwise undesirable user-generated content (UGC) in near real time. The product is a hybrid system combining automated detection (large language models, NLP rules, and machine learning) with human review workflows to reduce false positives and enable policy control. Bodyguard supports moderation across: - Social networks via native integrations (e.g., Instagram, TikTok, YouTube, Twitch, Discord, LinkedIn, X/Twitter, Meta/Facebook) - Owned/proprietary platforms via API/webhooks (use case: in-app chat, forums, marketplaces) The vendor states low-latency detection and broad multi-language coverage, with a subset of languages included by default in lower tiers. The help center describes proactive moderation workflows intended to analyze content and act before some content is visible.

Target market & positioning

Bodyguard primarily targets: - Brands with high reputational risk in public comment sections (consumer brands, luxury/fashion, sports, media) - Platforms that host UGC (social apps, gaming, communities) - Trust & Safety, Community, and Marketing teams that require centralized moderation workflows and reporting The product is positioned as an integrated moderation and insights solution, with emphases on: - fast near-real-time handling during spikes and crises - reducing human review workload compared with fully manual moderation - multi-platform coverage from a single dashboard

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

Download full report →

Published Oct 15, 2026

01Common complaints

An organization needs publicly available enterprise compliance certificates (SOC 2 Type II / ISO 27001) without executing an NDA

If a procurement process requires public certificates and reports upfront, Bodyguards publicly accessible materials may not be sufficient.

The organization wants an API-only, build-your-own pipeline rather than a dashboarded suite

Some organizations prefer model-provider APIs and custom workflows, particularly if they have an in-house ML/Trust & Safety engineering team.

The core issue is voice chat toxicity

Specialized vendors focused on voice-native detection may provide better coverage for voice-specific harms.

Budget is constrained and only basic comment filtering is required

Native platform moderation and lightweight keyword automation may be more cost-effective for simple needs.

The buyer requires extensive independent user reviews as part of evaluation

Limited presence on major B2B review platforms reduces availability of peer-review validation.

Deal disqualifiers

Buyers requiring publicly available SOC 2/ISO certifications before engagement
Teams that require extensive public developer-community validation and published benchmarks
Very small businesses with low moderation volume where cost may be disproportionate to need

Known limitations from public information

Lower tiers do not include all languages or custom classifications by default.
Limited publicly available information on SOC 2 / ISO certifications and detailed security controls.

Roadmap signals

Partner materials note exploration of large language model innovations; no public timeline for roadmap items was found.

Scale limitations

No explicit public documentation of maximum throughput thresholds; buyers should validate:
- Maximum requests per second for the API
- Rate limits
- Expected latency under peak load
- Multi-region and failover posture

API/integration constraints

Social platforms impose their own API limits and permission scopes; available moderation actions vary by platform.

Why customers might leave (inferred)

Because public churn and negative reviews are scarce, churn reasons are inferred from category norms:

Cost escalation with volume-based pricing
False positives or false negatives that affect brand voice or user experience
Procurement requirements for data residency or security certifications that are not publicly documented
Integration complexity for proprietary platforms

02Why they buy

Differentiators supported by available evidence

Published entry pricing in EUR (some Trust & Safety competitors are primarily sales-led with less public pricing)
Multi-platform integrations including major social platforms and Discord, plus API/webhooks for owned platforms
Emphasis on near-real-time moderation and a centralized dashboard for multi-channel oversight

Claims that require more verification

Sub-100 ms moderation latency and large-scale throughput: plausible but buyers should request latency SLOs by region, benchmark methodology, and false positive/false negative measurements by language.

Where competitors may have an advantage

Spectrum Labs: deeper Trust & Safety and compliance positioning
Modulate: voice-native moderation
API-first vendors: flexibility and potential cost control for engineering-led organizations

Why customers chose Bodyguard (inferred)

Based on positioning, testimonials, and case materials:

Proactive or pre-publish moderation capabilities
Multi-platform coverage with a single dashboard
Ability to tune rules and classifications
Hybrid automated + human review approach to manage false positives

03Field reports

Important limitation: lack of mainstream B2B review coverage

There is limited presence of Bodyguard reviews on major B2B review platforms discoverable via straightforward searches. This reduces the availability of broad, comparable end-user feedback.

Vendor-published testimonials (positive)

A media organization states Bodyguard helped strengthen online protection strategy and maintain a moderated discussion space.
A major sports organization describes measuring hateful messages and processing them within seconds.
A global social app highlights scaling moderation for a worldwide audience.
A public broadcaster mentions identifying crisis typologies and supporting legal processes.

These are vendor-published endorsements and may reflect curated or selected outcomes.

Independent/third-party references (contextual, not product reviews)

Media coverage notes event- and federation-level usage of protection tools based on Bodyguard.ai, with references to low-latency analysis and language updates.
A partner-published case story references high-volume comment processing and performance figures; these partner materials were not independently audited during this review.

Community/social discussions (negative/critical)

What customers say they achieved (metrics)

Commonly cited, vendor/partner-published metrics are described in broad terms:

Real-time analysis of very large comment volumes
Processing latency framed as low, near-real-time response

Critical note: these performance metrics are not accompanied by a detailed methodology, test harness, or independent benchmarking in the reviewed materials.

1) Partner-published case story

What is claimed:

Industrialized, containerized deployment using managed Kubernetes and common messaging/caching patterns
Comment processing reported with low, near-real-time latency
Scale described as analysis of very large comment volumes
Growth and early revenue figures are referenced in partner materials

Critical evaluation:

This is a partner-published case study and presents positive outcomes.
Metrics in the case study are not independently audited; latency and throughput measurement methodologies are not disclosed.
The case study provides signals of infrastructure maturity (managed Kubernetes, message queuing, caching) and an example deployment architecture.

2) Vendor customer summaries (gaming context)

What is claimed:

Chat moderation support in a gaming context

Critical evaluation:

These items are vendor-hosted customer summaries and likely curated.
Missing details often include baseline comparisons, experimental design, false positive rates, long-term retention impact, and cost-to-moderate metrics.

3) Sports event context (major sports event / national federation)

What is claimed:

Players and officials could opt into social-account protection during event windows
Real-time moderation with low-latency analysis and linguistic updates

Critical evaluation:

Media coverage supports event-level usage but relies on statements from the federation or vendor.
The coverage supports that Bodyguard has been used in high-profile sports contexts.

04Best fit

Company sizes (practical ranges)

Mid-market to enterprise is the clearest fit based on entry price and a sales-led motion.
Likely sweet spot: brands and platforms with meaningful UGC volumes but without a fully built internal Trust & Safety engineering organization.

Industries / verticals

Strong signals from customer materials:

Media and entertainment (broadcasters, publishers)
Sports teams, leagues, and events
Gaming (chat moderation, Discord/community)
Consumer brands with reputational risk in comments

Roles and personas

Decision makers:

Head of Trust & Safety
Head of Community / Social Media
Brand reputation / Communications leadership
Product leadership for UGC features

Day-to-day users:

Community moderators
Social media managers
Trust & Safety analysts

Best-fit pain points

High toxicity or spam in comments that damages brand perception
Need to moderate multiple platforms without separate tools
Sudden surges (live events, launches) that overwhelm human teams
Need for reporting and analytics to assess community health

05The pitch

What the product does (objective value proposition)

Bodyguard supports moderation across:

Social networks via native integrations (e.g., Instagram, TikTok, YouTube, Twitch, Discord, LinkedIn, X/Twitter, Meta/Facebook)
Owned/proprietary platforms via API/webhooks (use case: in-app chat, forums, marketplaces)

The vendor states low-latency detection and broad multi-language coverage, with a subset of languages included by default in lower tiers. The help center describes proactive moderation workflows intended to analyze content and act before some content is visible.

Target market & positioning

Bodyguard primarily targets:

Brands with high reputational risk in public comment sections (consumer brands, luxury/fashion, sports, media)
Platforms that host UGC (social apps, gaming, communities)
Trust & Safety, Community, and Marketing teams that require centralized moderation workflows and reporting

The product is positioned as an integrated moderation and insights solution, with emphases on:

fast near-real-time handling during spikes and crises
reducing human review workload compared with fully manual moderation
multi-platform coverage from a single dashboard

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

Download full report →