NeuralTrust

Name: NeuralTrust
Brand: NeuralTrust

AI Native

NeuralTrust secures AI Agents and LLMs for the largest companies.

Est 2022|Raised $17M|10 Ref Customers

LinkedIn 10K|Web traffic 10K|FTEs 22|HQ Barcelona, Spain

Published Oct 1, 2026

01Common complaints

- Company sizes or stages where this product is overkill or insufficient - Small teams experimenting with a single internal chatbot may find the platform and deployment overhead disproportionate to the risk addressed. - Industries or use cases where competitors are clearly superior - When the primary need is lightweight in-app guardrails without adding gateway infrastructure, embedding open-source guardrails libraries or application-level frameworks may be a better fit. - Technical requirements that this product cannot meet - If an organization requires publicly available, independently verifiable SOC 2 Type II reports or additional specific certifications (for example, ISO 27017/27018, HIPAA, PCI DSS) and the vendor cannot provide appropriate evidence under NDA or otherwise, alternative vendors with stronger public compliance artifacts may be required. - Budget constraints that make alternatives more appropriate - If procurement requires a published price list or strict low-cost limits, the sales-led enterprise pricing model may be a blocker. - Specific workflows or integrations where the product falls short - If strict ultra-low-latency requirements exist, buyers should benchmark the product with the full policy stack enabled. If acceptable latency cannot be demonstrated in a PoV, simpler alternatives may be preferable. - Red flags in the buying process - If the product value cannot be demonstrated in a time-boxed PoV (with measurable latency, false-positive rates, and incident reduction metrics), the platform approach may not justify the operational overhead. - Current limitations acknowledged by the vendor - No explicit public “known issues” document page was identified in the reviewed materials. - Features on the roadmap (not yet available) - note estimated dates if available - No public, dated roadmap details were located in the reviewed sources. - Scale limitations (at what volume/size does it break down?) - The vendor publishes throughput and latency claims, but independent benchmark reports were not identified in the reviewed materials. Buyers should conduct PoV benchmarking with representative policy sets and traffic patterns. - Security or compliance gaps - SOC 2 Type II is asserted in vendor documentation, but the public report was not available in the reviewed sources. Additional certifications (ISO 27017/27018, HIPAA, PCI DSS) were not documented publicly in the materials reviewed. - Technical debt or architectural limitations mentioned in reviews - No public end-user review data was available to evaluate reported technical debt or architectural issues. - API limitations or integration constraints - The gateway operates as an independent component with admin and proxy APIs; this provides flexibility but may require refactoring traffic patterns and additional operational ownership.

"Why customers left" / churn reasons

Public information on customer churn and reasons for leaving were not available in the reviewed sources.

02Why they buy

- Competitive advantages validated by customer feedback (not just vendor claims) - Public, independent customer feedback is limited in the reviewed dataset; the most direct customer feedback found in public materials is a single vendor-published testimonial from a banking customer. - Unique features and capabilities competitors lack (based on available evidence) - Split-plane data sovereignty architecture enabling a customer-controlled data plane across SaaS/hybrid/on-prem modes. - An open-source gateway component with plugins and operational instrumentation. - Documented security research and a catalog of attack probes for evaluation and red-teaming. - Differentiation from competitors based on reviews - Insufficient third-party review data was available in the reviewed materials to draw firm conclusions about comparative customer sentiment. - Areas where competitors clearly have advantages - Open-source guardrails libraries may enable faster in-app integration for single-application use cases. - Vendors with a larger and mature public review footprint can provide more peer validation and reference material for buyers.

03Field reports

Review platform availability reality check

- Gartner Peer Insights: The vendor has a listing with no end-user reviews in the reviewed crawl, which limits the ability to cite validated end-user sentiment from that platform. - G2: Public materials show an alternatives listing but limited substantive end-user review content for the vendor in the portion accessed. - TrustRadius: No substantive product page with reviews was located in the accessible sources. - Community forums (e.g., Reddit, Hacker News): No substantive, directly relevant discussions about the vendor were identified in the accessible searches performed.

Direct quotes available (mostly vendor-published)

A vendor-published testimonial from a banking customer references stress-testing a chatbot and validating a secure go-live in a regulated environment. This is a vendor-provided quote and was not corroborated by independent review platforms in the reviewed dataset.

Practical implication for buyers

- The public review footprint is limited across mainstream review sites based on the reviewed sources. This increases the buyer diligence burden and suggests requesting additional validation during procurement, including: - Reference calls with customers in similar regulated industries and architectural contexts - A proof-of-value (PoV) focused on measurable latency, detection quality, false-positive tuning effort, and operational overhead - Direct access to compliance documentation (e.g., SOC 2 reporting, relevant ISO certificates) under appropriate confidentiality arrangements

Common “why choose” signals (inferred from positioning and product materials)

Buyers are likely to evaluate this vendor when they require:
- Centralized gateway control across multiple model providers
- Agent/tool-call governance and monitoring
- Hybrid or on-premises deployment options for data residency
- Runtime threat detection capabilities beyond single-prompt filtering

Publicly named customer examples (vendor-published)

The vendor publishes customer examples and testimonials referencing implementations with airlines and banks and specific case narratives about stress-testing chatbots for regulated environments. These are vendor-published case references and should be validated with direct references where possible.

Quantified outcomes and methodology gaps

The vendor publishes platform-scale and security outcome metrics (for example, aggregated counts of blocked incidents, monitored applications, and analyzed interactions). These metrics were presented without disclosed methodology in the public materials reviewed (definitions of incidents, attribution methods, sampling windows, and customer mix were not provided). Treat such figures as vendor-published metrics to be validated in a PoV.

Security research as a case-study proxy

The vendor documents research work on multi-turn jailbreak techniques and provides example probes and tooling for evaluation. This research demonstrates capability in threat analysis and tooling development but does not guarantee operational effectiveness across diverse enterprise stacks without further validation.

04Best fit

- Company sizes: enterprise-scale organizations - Best fit appears to be large enterprises with dedicated security and platform engineering teams, significant compliance and data residency requirements, and substantial AI traffic or agent usage across multiple business units. - The vendor publishes claims indicating a strong enterprise customer base; these are vendor-published assertions and should be validated during procurement. - Industries and verticals where adoption is strongest (with customer examples) - Vendor-published vertical examples include regulated and large-scale sectors such as banking/financial services and airlines. - Roles and personas: decision makers AND end users - Decision makers: - Chief Information Security Officer (CISO) / Head of Security Engineering - Head of AI Platform / VP Engineering - Risk & Compliance leaders - End users: - Security engineers (policy authoring, SIEM integration) - AI platform engineers (gateway integration, routing/fallback, performance) - ML engineers and red-teamers (evaluation and adversarial testing) - Pain points and challenges the product addresses - Lack of inventory and visibility into agents and applications (agent sprawl) - Prompt injection, multi-turn jailbreaks, and context poisoning attacks - Tool-call abuse by autonomous agents - The need for centralized governance across multiple LLM providers and applications - Qualification criteria: what makes a strong fit - Organizations with multiple production LLM applications or agent deployments across teams and the need for centralized controls. - Organizations that must support hybrid or on-premises processing for sensitive prompts and responses. - Organizations able to commit platform engineering resources to deploy a gateway and data plane as part of a proof-of-value. - Deal disqualifiers: what makes a poor fit - Small teams or early-stage projects with a single LLM application and limited security engineering resources. - Architectures that cannot introduce a gateway into the request path. - Procurement requirements that mandate published, self-serve pricing with minimal negotiation. - Real customer examples when available - The vendor publishes customer references and a banking-sector testimonial describing a stress test for a chatbot go-live; these are vendor-provided references and should be validated with direct references where possible.

05The pitch

What the product does (core value proposition in objective terms)

- NeuralTrust is an enterprise platform that provides discovery and security controls for AI agents and applications. It implements a centralized security layer between users/agents and model/tool execution to support runtime enforcement (blocking abuse or data leakage), posture visibility (inventory and monitoring), and evaluation/red-teaming to detect and reduce incidents in production. - The product suite publicly described includes: - TrustGate (AI gateway / agent gateway) - TrustGuard (runtime security engine) - TrustLens (posture and observability) - TrustTest (automated red teaming) - Agent security components such as Guardian Agents, MCP Gateway, MCP Scanner, and Agent Security Posture

Target market and market positioning

- Messaging and product features target enterprise customers with regulated, performance-critical environments that require centralized governance across multiple teams and AI endpoints. The vendor describes support for SaaS, VPC, and on-prem deployments, SIEM integration, audit logs, and data residency controls. - The vendor has published claims about its customer base and vertical reach (for example, that a high proportion of customers are large enterprises and that customers include banks, airlines, energy companies, and government agencies). These are vendor-published statements and were not independently verified in the reviewed public sources.

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