Building Research Intelligence Pipelines with Gemini Grounding

I built a research intelligence tool that extracts structured datasets from web sources using Google Gemini’s search grounding feature. The key insight: grounding transforms LLMs from “confident guessers” into “citation machines” that show their work.

The Problem: LLMs Hallucinate Data

Ask an LLM to list recent hospital mergers and you’ll get a confident response. But is it accurate? Are those dates real? Did that acquisition actually happen?

Traditional LLM responses have no provenance. You can’t verify claims without manually searching each one. For research applications, “the model said so” isn’t a citation.

What Grounding Changes

When Gemini uses Google Search grounding, every claim comes with source URLs. The model searches the web in real-time and grounds its response in actual documents.

Instead of generating from training data, the model:

1. Searches Google based on your query
2. Retrieves relevant documents (news articles, press releases, filings)
3. Synthesizes a response from those documents
4. Returns the source URLs it used

This creates an audit trail. Each data point traces back to a verifiable source.

Example: Tracking Hospital M&A Activity

I wanted to build a dataset of hospital mergers and acquisitions for a given time period. Manually, this means searching news sites, reading SEC filings, cross-referencing dates—hours of work that needs repeating monthly.

With grounded search, I define what I’m looking for once:

Query configuration:

Find Hospital Mergers and Acquisitions. Prioritize official press releases and SEC filings first, then industry publications like Becker’s Hospital Review and Modern Healthcare. Extract: acquirer name, target name, announcement date, deal status, and value.

Running a scan for Q1 2024:

Starting scan for: mergers (2024-01-01 to 2024-03-31)
Searching Google with Gemini...
Found 12 sources.
Extracting structured data...
Extracted 8 potential records.
Linking records to sources...
Validating records...
6 valid records.
2 records rejected.

What comes out:

Each record links back to the actual source URLs. The rejected records include specific reasons—maybe a date couldn’t be verified, or the source didn’t actually support the claim.

The Four-Stage Pipeline

1. Grounded Search

Gemini searches the web with source priorities I specify. It returns both synthesized text and the URLs it drew from. This is where grounding does the heavy lifting—I get real-time web results, not stale training data.

2. Structured Extraction

The raw search results are unstructured text. A second pass extracts structured records with consistent fields. Gemini’s structured output mode ensures valid data every time.

3. Source Linking

Each extracted record gets matched to its source URLs. This creates the audit trail—anyone reviewing the data can click through to verify the original source.

4. Validation

An LLM-as-judge validates each record against the source content. Does the extracted date match what the article says? Is the acquirer name spelled correctly? Records below the confidence threshold get flagged with specific issues.

The output includes full HTML snapshots of each source page. URLs change and pages get removed—capturing the source at extraction time creates a permanent record.

Why Grounding Matters for Research

Source Provenance

Every data point traces to a URL. This transforms research from “I found this somewhere” to “here’s exactly where this came from.” For anything that might end up in a paper or presentation, this is essential.

Temporal Awareness

Grounding searches the live web. Unlike models with knowledge cutoffs, grounded searches find information published yesterday. For tracking ongoing events—M&A activity, policy changes, market developments—this matters.

Quality Signals

The grounding metadata tells you where information came from. You can see if a claim comes from an SEC filing or a random blog post. Source credibility becomes visible.

Verification Pipeline

Because you have source URLs, you can build automated verification. The validation stage cross-references extracted claims against actual source content and flags discrepancies.

Cost Considerations

Each pipeline run makes multiple Gemini API calls:

• 1 grounded search call
• 1 extraction call
• 1 linking call (batched)
• N validation calls (one per record)

Gemini 2.0 Flash pricing (as of early 2025):

• Input: ~$0.10 per million tokens
• Output: ~$0.40 per million tokens
• Search grounding adds ~$35 per 1,000 grounded queries

For a typical scan returning 10 records with 12 sources, expect roughly:

• Search + extraction: ~5K tokens input, ~2K output
• Validation: ~2K tokens per record
• Estimated cost per scan: $0.50–$2.00

I’m still instrumenting the pipeline to get precise per-run costs. The main cost driver is the grounding fee—at $0.035 per grounded query, search is the most expensive stage. For high-volume use cases, caching and batching strategies would help.

Note: Pricing changes frequently. Check Google’s pricing page for current rates.

Limitations

Grounding isn’t perfect. The sources returned are what Gemini used, not necessarily the best sources available. Specifying source priorities in your prompt helps, but the model might still miss authoritative sources.

Each stage adds error. Search might miss relevant results. Extraction might misparse text. Linking might match records to wrong sources. The validation stage catches many of these, but logging everything is essential for debugging.

Cost and latency. Grounded search adds latency compared to direct model queries—typically 2-5 seconds for the search to complete.

When to Use This Approach

Good fits:

• Research requiring citations
• Tracking recent events (news, filings, announcements)
• Building datasets that need audit trails
• Domains where accuracy matters more than speed

Poor fits:

• General knowledge questions (use base model)
• Historical data before ~2020 (grounding searches recent web)
• High-volume, low-stakes applications
• Real-time applications where latency matters

Takeaway

Grounding transforms LLMs from black boxes into transparent research tools. The model still does the synthesis work, but now you can see its sources and verify its claims.

For research intelligence—building datasets, tracking events, monitoring markets—this changes what’s possible. Instead of manually searching and compiling, you define what you’re looking for once and let the pipeline handle extraction and verification.

The audit trail is the key. Every claim traces to a source. That’s what makes the output usable for real research.

Resources:

• Search Intelligence Framework - The project described in this post
• Gemini Search Grounding