AskHandle

Data Search

Data search is the process of finding relevant information from structured or semi-structured datasets such as tables, spreadsheets, catalogs, inventories, listings, or database records.

Unlike document search, which focuses primarily on unstructured text, data search often needs to preserve fields, values, relationships, and filters so the system can retrieve records accurately.

What Is Data Search?

Data search helps an application find the rows, records, or values that match a request.

Examples include searching:

  • Product catalogs
  • Property listings
  • Vehicle inventories
  • Customer records
  • Service directories
  • Pricing tables
  • Availability data
  • Spreadsheets
  • CSV files
  • Structured business datasets

A user may search using exact values, natural language, or a combination of both.

For example:

"Show me two-bedroom properties in Brooklyn under $4,000."

The system needs to understand the natural-language request while also respecting structured fields such as:

  • Bedrooms = 2
  • Location = Brooklyn
  • Price < 4000

How Data Search Works

A data search system typically combines several types of processing.

Query Interpretation

The system interprets what the user is looking for.

Field Matching

Relevant parts of the request are mapped to fields in the dataset.

Filtering

Structured conditions are applied.

Examples include:

  • Price range
  • Location
  • Date
  • Category
  • Status
  • Capacity
  • Availability

Search

Text fields may also be searched using lexical, semantic, or hybrid methods.

Ranking

If several records match, the system determines which should appear first.

Result Presentation

The results may be displayed directly or passed to an AI agent for explanation, comparison, or recommendation.

Structured Data and Unstructured Data

The distinction between structured and unstructured information is important.

Structured Data

Structured data is organized into defined fields.

For example:

ProductCategoryPriceStock
Model ALaptop99912
Model BLaptop12994

Each value has a defined meaning.

Unstructured Data

Unstructured data is primarily free-form content such as:

  • Documents
  • Policies
  • Manuals
  • Articles
  • Emails
  • Notes

Document Search is generally better suited to this kind of source.

Many real business systems contain both.

Data Search vs. Database Query

A traditional database query uses explicit fields and conditions.

For example:

text
1category = "laptop"
2price < 1200
3stock > 0

A user, however, may ask:

"Which laptops under $1,200 can I actually order today?"

Data search can add a natural-language layer that interprets the request and maps it to the relevant fields and conditions.

The underlying system may still use database-style operations to return the result.

Data Search vs. Document Search

The two solve different retrieval problems.

Data Search is best suited to records with defined fields.

Document Search is best suited to larger blocks of unstructured content.

For example:

Data Search: Find available hotel rooms with capacity for four people.

Document Search: Find the hotel's policy for children staying in the room.

A workflow may use both during the same conversation.

Data Search and Hybrid Search

Structured datasets can also contain text fields such as:

  • Product descriptions
  • Listing descriptions
  • Service details
  • Notes
  • Categories

Hybrid search can be useful when these text fields need both exact and meaning-based retrieval.

For example, a user searching for:

"quiet room away from elevators"

may not be searching an exact structured field.

Semantic retrieval can help match descriptive content, while lexical retrieval preserves exact terminology.

Structured filters can then narrow the results further.

Data Search and Semantic Search

Semantic search can help interpret meaning inside text-heavy fields.

This is useful when:

  • The user describes preferences naturally
  • Records contain descriptive text
  • Synonyms are common
  • Exact keyword overlap is limited

Semantic search should not replace structured filters when precise numeric or categorical conditions are available.

For example, a semantic model should not estimate whether a price is below $500 when the dataset contains an exact price field.

Data Search and Lexical Search

Lexical search is useful for exact terms such as:

  • Product names
  • Vehicle models
  • Neighborhood names
  • Codes
  • Categories
  • Service names

This is why a combined approach can be valuable for business data.

Data Search for AI Agents

An AI agent can use Data Search to retrieve records before responding or taking another action.

For example, an automotive agent may receive:

"Do you have any electric SUVs under $50,000?"

The workflow can:

  1. Interpret the request.
  2. Search the inventory.
  3. Filter by vehicle type, powertrain, price, and availability.
  4. Return matching vehicles.
  5. Let the agent explain or compare the results.

The agent does not need to memorize the inventory. It searches the current dataset.

Data Search for Recommendations

Data search is particularly useful when a recommendation must respect hard constraints.

For example:

"Find a hotel room for two adults and two children next weekend under $350."

The system should first filter records that meet the required conditions.

AI can then help explain the options or ask follow-up questions.

This separates factual eligibility from conversational presentation.

Search Quality in Structured Data

Good data search depends on more than natural-language understanding.

Important factors include:

  • Correct field mapping
  • Accurate filtering
  • Fresh data
  • Search relevance
  • Ranking
  • Handling of missing values
  • Units and formats
  • Query ambiguity
  • Duplicate records
  • Permissions

If the underlying dataset is outdated, the search result will also be outdated.

Data Search in AskHandle

AskHandle includes Data Search as a dedicated workflow node for retrieving relevant information from structured or semi-structured datasets.

The node uses hybrid retrieval that combines lexical and semantic search, while allowing structured business information to remain searchable as data rather than treating everything as one block of text.

This makes Data Search useful for workflows such as:

  • Product discovery
  • Vehicle inventory search
  • Property search
  • Service lookup
  • Structured knowledge access
  • Catalog search

The results can then support an AI answer, recommendation, routing decision, or another workflow step.

When to Use Data Search

Data Search is useful when:

  • Information is stored in records or tables
  • Users search using natural language
  • Hard filters matter
  • The dataset changes over time
  • Exact values must be preserved
  • The same dataset supports many different queries
  • Results need to be ranked or compared

Related Terms

← Back to glossary