# Aquin vs Deep Search

Aquin records 0 monthly visits with 0% month-over-month growth and Contact for Pricing pricing. Deep Search records 359,001 monthly visits with -52.7% growth and Paid pricing.

| Metric | [Aquin](https://openaitools.app/tool/aquin) | [Deep Search](https://openaitools.app/tool/deep-search) |
| :--- | :--- | :--- |
| Website URL | https://www.aquin.app | https://www.deepsearch.bio |
| Categories | AI Browsers, AI Search Engine, AI Assistant, AI Speech-to-Text | AI Search Engine, AI Assistant, AI Lead Generation, AI Recruiting |
| Pricing Model | Contact for Pricing | Paid |
| Monthly Visits | 0 | 359,001 |
| MoM Growth Rate | 0% | -52.7% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 43,324 |
| Review Score | 4.1 | 3.8 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Aquin

Aquin is best understood as a research lab rather than a single consumer app. The homepage describes the team as reverse engineering intelligence through interpretability, and frames its goal as applied prediction that compounds over time - systems that understand what a model has learned and that teams can point at their own data. The flagship applied product is Aleph, a universal predictor spanning tables, media, annotation and decisions.\n\nWhat separates Aleph from a general-purpose assistant is its output contract. It produces schema-bound results for programs: probabilities, labels, bounding boxes and scores, each carrying confidence so that uncertain items can be escalated rather than silently guessed. The lab positions this as 'one base, any modality, any task', fine-tuned into a specific workload and improving as more labelled work arrives.\n\nAround that core, Aquin publishes applied research on interpretability. Published work includes an interpreter for attention-as-routing-graph circuits from a single forward pass, tiled SVD methods for locating usable weight mechanisms, agentic weight-editing experiments on small models, and simulations of LLM behaviour in varied environments. Separate projects like the axi self-learning agent and the aq developer environment target people building and refining foundation models.

**Pricing & Plans:** Aquin does not publish a public price list. The site mixes freely readable research with applied engagements, so commercial terms are discussed directly with the team.

- **Universal predictor (Aleph):** A single base that handles tables, media, annotation and decision tasks, with the option to fine-tune toward your specific data.
- **Schema-bound outputs:** Outputs are typed and scored rather than free text, so downstream programs can act on results and escalate low-confidence cases.
- **Interpretability research:** Treats attention as a routing map from a single forward pass, isolating a small subgraph aimed at the answer and testing it under ablation.
- **Weight editing experiments:** Agentic weight-editing experiments that localise causal layers, apply rank-one MLP updates and validate then roll back steps when checks fail.
- **Self-learning developer agent:** An agent for controlling developer environments and running coding or research loops with recorded lessons and rejected paths.

**Strengths:** Research-grade interpretability work published openly alongside the applied product; Outputs designed for programs rather than chat, which suits automation pipelines; Explicit confidence scores make it practical to escalate uncertain predictions; One base is claimed to cover tables, media, annotation and decisions

**Limitations:** Aquin Labs is a research organisation, so most output is papers and interfaces rather than a polished consumer app; Aleph is described as a predictor for programs and teams, not a general chat assistant; No transparent public price list was found for commercial engagements; Material is heavily technical and assumes familiarity with ML evaluation work

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

## Deep Search

Deep Search replaces the tedious ritual of opening a dozen browser tabs whenever you need to understand a person. Instead of jumping between LinkedIn, a Google results page, a GitHub account and a company team page, you start with a single name and optionally add context such as employer, city or job title. The tool then proposes candidate matches so you can confirm you are looking at the right individual, especially with common names.

Once a match is confirmed, Deep Search compiles a structured profile: current and previous roles, companies, most likely city and region, education and alumni mentions, plus news articles, podcast appearances, talks and other web mentions. Everything is condensed into a short AI summary with inline links, so each assertion can be traced back to the public page it came from. A follow-up chat lets you ask targeted questions about a person's projects, background or public statements without starting the research over.

Deep Search presents itself as a tool for recruiters sourcing passive candidates, founders preparing for investor and partner conversations, analysts mapping a market, and journalists doing pre-interview research. Lookups are private — the people you search are not notified — and the site repeatedly notes that it is for research only, not for regulated screening decisions such as employment, credit, housing or insurance.

**Pricing & Plans:** Paid subscription. The site advertises plans starting at $7.99 per week, with higher tiers available; exact limits and feature splits should be confirmed on the vendor's own pricing page before purchase.

- **Name-based search with candidate matching:** Enter a name, add optional context like company or location, and pick the right person from a list of matching profiles before generating anything.
- **AI-enriched structured profiles:** Roles, companies, career arcs, likely location, education and web mentions are assembled into one readable dossier rather than scattered across sites.
- **Fully sourced claims:** Each statement in the summary is linked to the public page it came from, so you can verify facts instead of trusting an opaque model output.
- **Follow-up research chat:** Ask clarifying questions about a person's projects, roles or public statements and get answers grounded in the collected sources.
- **Private lookups:** Searches do not notify the subject, and the product is positioned as open-web research rather than contact or surveillance data mining.

**Strengths:** Consolidates a multi-tab manual research process into a single search and summary; Sources are linked, which makes verification possible instead of taking an AI summary on faith; Candidate disambiguation helps with common names and false matches; Lookups stay private and the subject is never notified

**Limitations:** Not a background check service and not FCRA-compliant, so it cannot be used for regulated screening decisions; Quality depends entirely on how much public web presence a person has — thin profiles yield thin results; Subscription-only with weekly pricing, and no permanent free tier advertised on the homepage

[Profile (Markdown)](https://openaitools.app/tool/deep-search.md)

## Frequently Asked Questions

### How do Aquin and Deep Search compare in monthly traffic?

Aquin has 0 monthly visits, while Deep Search has 359,001. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Aquin has a month-over-month growth rate of 0%; Deep Search has -52.7%.

### How does pricing compare?

Aquin: Contact for Pricing. Aquin does not publish a public price list. The site mixes freely readable research with applied engagements, so commercial terms are discussed directly with the team. Deep Search: Paid. Paid subscription. The site advertises plans starting at $7.99 per week, with higher tiers available; exact limits and feature splits should be confirmed on the vendor's own pricing page before purchase.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [Grok](https://openaitools.app/tool/grok) | 203,846,050 | -0.8% | Freemium | xAI's real-time AI assistant and coding workspace powered by Grok 4.7 with live X integration. | [.md](https://openaitools.app/tool/grok.md) |
| 3 | [Perplexity](https://openaitools.app/tool/perplexity-ai) | 88,310,223 | -3.7% | Freemium | Real-time AI answer engine and research workspace combining live web citations with frontier reasoning models. | [.md](https://openaitools.app/tool/perplexity-ai.md) |
| 4 | [Kagi Search](https://openaitools.app/tool/kagi-search) | 4,647,605 | +3.2% | Free Trial, Paid | A paid, ad-free search engine that answers to its users | [.md](https://openaitools.app/tool/kagi-search.md) |
| 5 | [Lenso.ai](https://openaitools.app/tool/lenso-ai) | 1,588,648 | -1.3% | Freemium | Performs AI-powered reverse image and face search across the web, though less optimized for structured professional profile aggregation. | [.md](https://openaitools.app/tool/lenso-ai.md) |
| 6 | [Dia Browser](https://openaitools.app/tool/dia-browser) | 1,254,172 | +108.4% | Free | The AI browser that reads between your tabs | [.md](https://openaitools.app/tool/dia-browser.md) |
| 7 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | The environments frontier AI trains on, and the agents that do the recurring work | [.md](https://openaitools.app/tool/labelbox.md) |
| 8 | [Eigent AI](https://openaitools.app/tool/eigent-ai) | 575,703 | +1.4% | Freemium, Free Trial, Contact for Pricing | Open-source desktop AI workforce that runs browser, file, and app tasks for you | [.md](https://openaitools.app/tool/eigent-ai.md) |
| 9 | [Vocareum](https://openaitools.app/tool/vocareum-com) | 531,556 | +5.4% | Paid | Hands-on lab infrastructure for AI, cloud, and data education | [.md](https://openaitools.app/tool/vocareum-com.md) |
| 10 | [Juicebox (PeopleGPT)](https://openaitools.app/tool/juicebox-work) | 293,157 | -3.4% | Paid | Converts natural language hiring prompts into ranked candidate lists, focusing heavily on recruiting rather than general OSINT research. | [.md](https://openaitools.app/tool/juicebox-work.md) |

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Source: https://openaitools.app/compare/aquin-vs-deep-search

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