Halo by Scam AI vs Zero: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Halo by Scam AI and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Halo by Scam AI

Reality Inc

Freemium

AI trust platform that detects deepfakes, voice clones, and GenAI content across images, video, audio, IDs, and live video calls.

Deepfake Detection: Catches face swaps, lip-sync, reenactment, and cloned voices that impersonate real people in image, video, and audio.
GenAI Content Detection: Identifies fully synthetic images, video, and audio from Stable Diffusion, DALL·E, Midjourney, Sora, and ElevenLabs.
Halo On-Device Call Protection: Runs 100% on-device to flag synthetic faces and face swaps live on Zoom, Teams, and Meet without recording or uploading anything.
Eva-v1 Model Family: Eva-v1-Fast returns verdicts on images in under 2 seconds; Eva-v1-Pro delivers forensic-grade accuracy in under 4 seconds.
Unified REST API: One integration handles both deepfake and GenAI detection across image, video, and audio via a single authenticated endpoint.
Identity & Document Verification: Detects forged IDs, manipulated selfies, and AI-generated documents before onboarding completes.
Add-on Defenses: Adaptive Defense, Active Liveness, and Express Lane low-latency mode extend detection with injection-attack protection and 3s response SLAs.
Enterprise Compliance: GDPR compliant, SOC 2 Type II attested, and no media retention by default, with configurable retention for audit needs.

KYC & Onboarding: Financial services and marketplaces block AI-generated selfies and forged IDs before an account is opened.
Contact Center Fraud Prevention: Detect voice clones in real time to stop synthetic-caller attacks against call-center authentication.
Executive Call Protection: Halo alerts staff to face-swap impersonations on video calls before authorizing wire transfers.
Hiring & Remote Interviews: Recruiters catch deepfake candidates impersonating real engineers during video interviews.
Content Moderation: Media platforms scan uploads at scale to flag GenAI images, video, and audio before they reach users.
Insurance Claim Review: Detect manipulated photos and forged supporting documents submitted with claims.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.