# Labelbox vs TechScreen

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. TechScreen records 28,970 monthly visits with +101.7% growth and Freemium pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [TechScreen](https://openaitools.app/tool/techscreen) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://techscreen.app |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Interview Assistant, AI Assistant, Undetectable AI, AI Code Assistant |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 762,803 | 28,970 |
| MoM Growth Rate | -22.8% | +101.7% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 4,760 |
| Review Score | 4.4 | 3.8 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **Data labeling and expert annotation:** Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
- **Autonomous agent sessions:** Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
- **Domain specialists by function:** Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
- **Graded outcomes that compound:** Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** Covers both sides of the problem: training and eval data production plus agents that act on recurring work; Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand; Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/labelbox.md)

## TechScreen

TechScreen is a desktop application built around a single promise: giving candidates real-time answers during interviews without the interviewer knowing. It works for coding rounds, system design, and behavioral interviews, accepting questions either typed in or captured from your system audio, which means it can pick up what an interviewer is saying on a call and surface an answer almost immediately.

The core technical claim is that invisibility comes from the operating system rather than a visual workaround. The app window is excluded from screen capture at the platform level, so when you share your screen on Zoom, Microsoft Teams, Google Meet, Webex, Amazon Chime, CoderPad, Codility, HackerRank, or similar tools, your audience sees the interview question but not the assistant's answer. The site notes that Zoom 6.16+ requires enabling window-filtering in the screen-share settings, and states that platform compatibility is re-checked daily.

The interface is designed around keyboard shortcuts so nothing on screen hints that help is being requested. Screenshots, conversation mode, sending answers, hiding the window, and repositioning it are all bound to hotkeys, and the window floats above your IDE, browser, and call. Users can configure programming language, spoken language, role, and custom prompts, and the vendor advertises that no user has been detected to date.

**Pricing & Plans:** TechScreen offers a free trial that does not charge a card, after which you pick a subscription plan. The company states every plan includes the full app, there are no hidden fees and no $1,000-plus tiers, and you can change or cancel at any time. Check the official pricing page for current amounts.

- **Screen-share invisibility:** The app window is excluded from screen capture by the operating system, so it does not appear in Zoom, Teams, Meet, or session recordings.
- **Typed and audio question capture:** Reads questions from typed text or live system audio, so answers appear as soon as the interviewer finishes asking.
- **Keyboard-only control:** Show, hide, move, and send responses entirely through keyboard shortcuts, with no mouse movement that could give it away.
- **Custom prompts and profiles:** Set your programming language, spoken language, role, and custom prompts so answers match the interview you are walking into.
- **Multi-format interview support:** Covers software engineering, product, and other roles across programming and behavioral formats, with no visible app icon in the dock or tray.

**Strengths:** Invisibility is implemented at the OS level, so it survives screen shares and recordings.; Reads live system audio, letting it react to spoken questions quickly.; Keyboard-only operation keeps the workflow discreet during a call.; Supports coding, system design, and behavioral rounds across languages and roles.

**Limitations:** Only macOS 13+ and Windows 10+ are supported; no Linux build is listed.; On Zoom 6.16+ users must manually enable the window-filtering screen-capture setting for invisibility to hold.; Using an interview assistant raises genuine ethical and policy questions with employers and interview platforms.; Effectiveness still depends on the candidate explaining answers convincingly rather than just reading them.

[Profile (Markdown)](https://openaitools.app/tool/techscreen.md)

## Frequently Asked Questions

### How do Labelbox and TechScreen compare in monthly traffic?

Labelbox has 762,803 monthly visits, while TechScreen has 28,970. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Labelbox has a month-over-month growth rate of -22.8%; TechScreen has +101.7%.

### How does pricing compare?

Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases. TechScreen: Freemium. TechScreen offers a free trial that does not charge a card, after which you pick a subscription plan. The company states every plan includes the full app, there are no hidden fees and no $1,000-plus tiers, and you can change or cancel at any time. Check the official pricing page for current amounts.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 2 | [ParakeetAI](https://openaitools.app/tool/parakeetai) | 1,094,780 | +12.6% | Paid | Real-time AI interview copilot that answers while you are still talking | [.md](https://openaitools.app/tool/parakeetai.md) |
| 3 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 4 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 5 | [Final Round AI](https://openaitools.app/tool/final-round-ai) | 397,782 | +3.6% | Free Trial, Paid | AI interview copilot with real-time answers and automatic debriefs | [.md](https://openaitools.app/tool/final-round-ai.md) |
| 6 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 7 | [LockedIn AI](https://openaitools.app/tool/lockedin-ai) | 337,801 | -18.7% | Freemium | Real-time AI copilot for interviews, meetings and coding rounds | [.md](https://openaitools.app/tool/lockedin-ai.md) |
| 8 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 9 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in advanced computer vision and physical AI data pipelines, though with a narrower focus than general text-and-image labeling. | [.md](https://openaitools.app/tool/encord-com.md) |
| 10 | [InterviewFox AI](https://openaitools.app/tool/interviewfox-ai) | 224,859 | +34.5% | Freemium | Real-time AI interview assistant with a safer dual-device mode | [.md](https://openaitools.app/tool/interviewfox-ai.md) |

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Source: https://openaitools.app/compare/labelbox-vs-techscreen

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