Introducing Deep Lookup — your new AI-powered research engine

If you’ve ever found yourself saying “I need a list of entities with X properties” or “I want to build a dataset of companies/people/products matching these filters for my model”, then this tool might intrigue you.

What is Deep Lookup?

At its core:

• It’s an AI-powered natural‐language query engine that searches the public web (both structured + unstructured sources) and returns table‐ready data about entities (companies, professionals, products, events, locations) rather than just links.

• You craft a query like: “Find all fintech startups in Europe founded after 2022 with Series A funding and >50 employees”, and it will attempt to return rows matching those criteria.

• It is designed to perform at scale: the company claims it can search 1,000+ sources simultaneously and scan hundreds of billions of web pages.

• The output is enriched: you get not only the entity name, but fields like website, industry, HQ location, funding, technology stack, employee count, contact info (if available).

• Billing is usage‐based: you pay only for matched records (i.e., those that satisfy your filters), not for the skipped/unmatched ones.

  And for reference, here’s a video introduction:

Why this matters for ML researchers & practitioners

Here are a few angles relevant to our world:

• Dataset creation, fast: Many ML pipelines depend on curated datasets of entities (companies, people, products) with rich attributes. Deep Lookup offers a high-level way to generate such datasets without building a full scraper + ETL pipeline yourself.

• Feature engineering / enrichment: If you already have a list of entities, you might enrich them with attributes (funding size, tech stack, geography, contact signals) which can then become features in your ML model (e.g., in a classifier or clustering pipeline).

• Agent or LLM augmentation: If you are building an AI agent or LLM workflow that requires up-to-date web context (e.g., “Which companies in region X are exploring AI deployments?”), then a tool like this helps fetch structured facts rather than raw search results. The company itself frames it as “intelligence of today’s LLMs is no longer its limiting factor; access is.”

• Rapid hypothesis testing: In research you often form hypotheses like: “Startups in country Y with >$10 M funding adopt ML stacks more aggressively than those with < $10 M.” You could quickly pull a dataset via Deep Lookup to test such hypotheses.

• Reducing noise / improving precision: Unlike generic search engines, Deep Lookup attempts to filter and structure based on your criteria so you spend less time cleaning raw web results.

How Deep Lookup works (for you as an ML-oriented user)

Here’s a guided workflow tailored for ML/research:

1. Define your entity-space and criteria:

   • e.g., “companies”, or “people (CTOs)”, or “products in category Z”.

   • Add measurable filters: founding year, region, funding round, employee count, technology keyword, etc.

2. Specify enrichment columns / attributes:

   • Example columns: entity name, website, description, industry, HQ location, funding amount, employees, tech stack keywords, contact email.

   • Additional optional columns: competitor names, recent news mentions, product list, LinkedIn profiles, etc.

3. Enter natural-language query:

   • Start with: “Find all … that …”

   • Provide criteria. Eg: “Find all AI/ML startups headquartered in Berlin founded after 2020 with Series A funding > $5M and at least 10 employees.”

4. Preview mode:

   • Run a small preview (e.g., 10 sample records) to validate whether the results align with expectations.

   • Check accuracy of returned records, alignment with criteria, quality of enrichment fields.

5. Run full extraction:

   • Execute full job, export results (CSV/JSON) into your ML environment (Pandas DataFrame, etc.).

6. Post-run enrichment (optional but powerful):

   • After initial collection, you may decide you need extra columns (e.g., “recent news citations”, “open roles”, “tech stack keywords”). Deep Lookup supports adding columns for a small incremental cost.

7. ML pipeline integration:

   • Use the structured dataset as input to your ML pipeline: feature engineering, clustering, classification, exploratory analysis, etc.

   • Because it’s structured and pre-filtered, less time is wasted cleaning or transforming.

8. Iterate:

   • Based on findings you might refine your query (e.g., tighten filters, add/remove attributes) and rerun for more granularity or a different segment.

   • You might also combine with other data sources (your internal data, alternative enrichment APIs) for richer models.

Key metrics / claims & pricing model (so you can budget)

Some of the numbers you’ll want to know:

• Accuracy claim: “95 %+ accuracy” for matched records.

• Pricing: ~ US $1.00 per matched record (first tier) including first 10 enrichment columns.

• Additional enrichment columns beyond ten: ~$0.05 each per matched record.

• You only pay for entities that meet your criteria (matched); skipped/unmatched ones don’t incur cost.

• Volume discounts:

  • 1-1,000 records: $1.00/record

  • 1,001-5,000: $0.80/record

  • 5,001-10,000: $0.70/record

  • 10,000+ : custom pricing.

• Example cost comparison: manual research ~$30-50 per row vs Deep Lookup ~$1 per row—huge cost/time savings.

Strengths ✅

• Dramatically lowers the barrier for generating structured datasets from web-scale sources.

• Natural-language queries—less technical overhead for dataset creation.

• Rich enrichment capabilities aligned with ML workflows (attributes, metadata, contact, etc.).

• Transparent cost model and preview mode—good for experimentation.

• Built for scale and updated breadth of sources (public web, large archive) so potentially broader entity coverage.

Limitations & things to watch ⚠️

• While “95%+ accuracy” is claimed, in practice the quality will depend heavily on your query precision and the public availability of data for your entity type/region/criteria. Rare entities or obscure niches may have less coverage.

• Don’t treat it as a replacement for primary data collection—sometimes you still need domain-specific data, manual verification, or internal records.

• Privacy/compliance: If you are acquiring contact data (emails/phones) or sensitive personal info, ensure your usage is compliant with regulations (GDPR, CCPA etc.).

• Cost can scale quickly: large volumes + many enrichment columns = higher cost. Budget accordingly.

• The tool is in Beta (as of recent announcements) which means features/coverage may evolve.

Practical suggestions for ML/AI research use‐cases

Here are some concrete ideas you (or your readers) might try:

• Startup segmentation: Pull all “Computer Vision startups in Eastern Europe founded after 2019 with funding > $10M”, then cluster them by tech stack keywords and derive insights on geographic/stack patterns.

• Professional network modeling: Query “CTOs at mid-size SaaS companies in San Francisco with Machine Learning roles” → build graph of people, companies, tech stacks → use embeddings/clustering for persona modeling.

• Product mapping: “Find all products in the ‘AI operations’ category launched 2023 by companies with >50 employees”, enrich with tech stack, website, funding → feed into recommendation or market sizing model.

• Agent/LLM context feeding: If you’re building an LLM-based agent (e.g., research assistant), you might use Deep Lookup to fetch up-to-date entity tables that the agent can query (rather than only relying on static training data).

• Enrichment for predictive models: Suppose you have records of companies and want to predict “will they adopt ML in next 12 months?” → enrich each company with attributes via Deep Lookup (funding, employee count, tech stack presence, industry) and use those as features.

💡 Example Deep Lookup Natural-Language Prompt

Final thoughts

For ML researchers and practitioners, Deep Lookup is a compelling toolbox component. It doesn’t replace modeling, feature engineering, or domain expertise—but it accelerates the data-generation and enrichment phase, which is often a major bottleneck in ML workflows.

If you’re spending hours scraping, cleaning, filtering entity data before you even get to modeling, #Deep Lookup might help you shift effort toward higher-value tasks (modeling, interpretation, experiments) rather than plumbing.

References