# Cogram vs Labelbox

Cogram records 4,376 monthly visits with -22.3% month-over-month growth and Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Cogram](https://openaitools.app/tool/cogram) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.cogram.com | https://labelbox.com |
| Categories | AI Project Management, AI Meeting Assistant, AI Email Assistant, AI Report Generator | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 4,376 | 762,803 |
| MoM Growth Rate | -22.3% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 1,719 | 370,024 |
| Review Score | 4.2 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Cogram

>Cogram was founded in 2021 and is positioned as an AI-native alternative to the decades-old, pre-AI software stack that architecture and engineering firms still rely on. Rather than bolting a chatbot onto existing tools, Cogram rebuilds core project workflows around AI: an agent that reads and answers questions about project documents, email that files and searches itself, meeting minutes and field reports that draft into your templates, and structured RFI and submittal logs.

The platform is designed to fit around a firm's existing systems. It connects to communication tools such as Microsoft Teams, Outlook, Zoom and Google Meet, and to project and ERP platforms including Procore, Autodesk Construction Cloud, Deltek and Unanet, many of which can sync project data in. Cogram emphasizes controls as much as automation: it is SOC 2 Type II certified, supports SAML 2.0 SSO and MFA, uses AES-256 at-rest and TLS 1.3 in-transit encryption, offers optional private-cloud or on-prem storage, and states that no AI models are trained on customer data.

Cogram serves architects, engineers, consultants and general or specialty contractors, with offices referenced in San Francisco, New York, London and Berlin. Accessibility matters for adoption here, so the company offers a free 14-day self-serve trial, free guided trials with customer reference calls, and free external collaborator accounts. Commercial pricing is quoted per firm after a demo.

**Pricing & Plans:** Cogram uses contact/tailored pricing: a free 14-day self-serve trial and free guided trials are available, external collaborator accounts are free, and full pricing is provided after a demo.

- **Project AI Agent:** Ask natural-language questions across project documents, contracts and correspondence, and get answers grounded in your own files rather than generic responses.
- **Email Management:** Automatically file, organize and search email across all project correspondence so nothing gets lost in personal inboxes.
- **Automated Meeting Minutes:** Capture minutes in virtual and in-person meetings and have them drafted directly into your firm's templates.
- **RFIs & Submittals:** Log and review RFIs and submittals in a fast, user-friendly workflow that syncs with Procore and other project platforms.
- **Field Reports:** Record spoken observations and photos on site, then generate field reports inside your own document templates with minimal formatting.

**Strengths:** Purpose-built for architecture, engineering and construction rather than generic project management; Automates high-friction AEC paperwork: email filing, minutes, field reports, RFI and submittal logs; AI agent answers questions grounded in your own project documents; Strong security posture: SOC 2 Type II, SAML 2.0 SSO, MFA, encryption, and no model training on customer data

**Limitations:** Pricing is quote-based and requires a demo or sales conversation, so costs are not transparent upfront; Value depends on adoption across a whole project team, so partial rollouts may underdeliver; Deepest benefits come from integrations like Procore, Deltek and Unanet, which add setup effort; Feature scope is specialized for AEC workflows, offering less fit for unrelated industries

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

## 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)

## Frequently Asked Questions

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

Cogram has 4,376 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Cogram has a month-over-month growth rate of -22.3%; Labelbox has -22.8%.

### How does pricing compare?

Cogram: Contact for Pricing. Cogram uses contact/tailored pricing: a free 14-day self-serve trial and free guided trials are available, external collaborator accounts are free, and full pricing is provided after a demo. 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.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Dooray!](https://openaitools.app/tool/dooray) | 1,532,922 | -3.9% | Freemium, Free Trial, Paid, Contact for Pricing | All-in-one Korean collaboration suite for teams and public institutions | [.md](https://openaitools.app/tool/dooray.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [Jump](https://openaitools.app/tool/jump-ai) | 553,230 | +33.1% | Free Trial, Paid, Contact for Pricing | Compliant AI assistant for financial advisors | [.md](https://openaitools.app/tool/jump-ai.md) |
| 7 | [Motion](https://openaitools.app/tool/usemotion-com) | 511,886 | -15.5% | Free Trial, Paid | The AI super app for work — tasks, projects, calendar and meetings in one place | [.md](https://openaitools.app/tool/usemotion-com.md) |
| 8 | [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) |
| 9 | [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) |
| 10 | [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) |

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

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