In MarTech, CRM and SaaS, your users are constantly fighting a battle against incomplete information.
For Product Managers, an empty field is not just missing data; it is friction. Every time a user needs to open a new tab to Google a prospect’s revenue, check a competitor’s pricing or verify a lead’s tech stack, they are leaving your product.
In the AI era, in‑app enrichment is no longer a “delighter”; it’s the baseline expectation. The barrier to entry has collapsed. If the data exists on the public web, your product should be able to fetch it.
So, why isn’t everyone doing this already?
The three barriers to living data
Most product teams fall into one of three categories. Each has a gap that modern AI and web access can close.
1) The feature gap (doing nothing)
Many tools do not offer enrichment at all. They act as empty containers waiting for user input.
PM risk: This is the riskiest position. Since AI has made search and extraction a commodity capability, the empty container era is ending.
If you do not provide the data, a competitor will. Users will move to the tool that does the homework for them.
2) The vendor trap (buying static data)
Teams that do offer enrichment often solve it by integrating third‑party data vendors or fixed datasets.
PM reality: Curated datasets, including Bright Data Datasets, are powerful when the needed sources are covered and freshness meets your SLA. They can deliver fast value for well‑defined domains.
PM risk: Unit economics and data coverage often become constraints — especially when targeting long‑tail entities, niche markets or attributes that change rapidly. Agentic workflows (agentic = an AI‑driven loop that plans → searches → extracts → verifies → writes back) exist to address these challenges: The best source may not always be known in advance, and what is true today may shift tomorrow. The winning approach is to use curated datasets where appropriate, while deploying agents that can discover, retrieve and cite new or updated sources when required by your users.
3) The build trap (internal scraping)
Ambitious teams try to build enrichment internally and ask engineering to spin up scrapers.
PM reality: Bright Data’s infrastructure for web access, discovery and archival helps you maintain reliable data access and minimize disruptions.
PM risk: Access alone does not solve the enrichment challenge. You still need logic to extract and structure the information. Scrapers without an agentic layer tend to become fragile point solutions. They often behave like black boxes that do not store citations or confidence scores, which undermines trust. Combining agentic logic, extraction prompts or parsers, and observability is what transforms access into a reliable product feature for your users.
The shift: Web‑connected agents as a feature
The answer is not to buy more static lists or to maintain a sprawl of custom scrapers. Instead, treat web search and extraction as an API‑driven infrastructure layer that your product can call on demand.
By integrating AI agents into this layer, you enable features like auto‑population that feel seamless for users. The agent behaves like a researcher: It reads a row, understands intent, searches the live web, identifies and fetches the relevant page, extracts the necessary data, and returns the value — complete with a citation and timestamp.
This is already changing user expectations:
- Marketing tools: Products now auto‑populate segmentation data, such as tech stack details and recent news, for any uploaded domain.
- CRMs: Fields are no longer static; CRMs update automatically when prospects change jobs or companies announce funding.
- Retail analytics: Dashboards can now monitor competitor pricing and stock levels with minimal manual effort, delivering near real-time insights.
How it works at a high level
Start with a table in your own database or hosting environment, for example Snowflake, Amazon S3, Databricks, Postgres or your preferred stack.
The agent determines how to identify each row in the wild, translates your product intent into search queries, discovers authoritative sources and can re‑rank results for accuracy. It then fetches the selected webpage, extracts the required field, attaches the source URL and timestamp and writes the value back to your table.
If the result is ambiguous, the agent asks a follow‑up question and repeats. You define the freshness SLA and schedule refreshes accordingly.
For products on Snowflake DB: You can initiate from an external function or Snowpark procedure, push results via a stage and Snowpipe, and schedule refreshes with Tasks.
The same read‑write pattern applies for S3, Databricks, or Postgres through your orchestrator.
Implementation: It’s just another table operation
As an infrastructure layer, this approach connects directly to your existing data platforms.
- Source: Your data lives in Snowflake, Amazon S3, Databricks, Postgres or your preferred environment
- Action: Trigger the agent using an external function or a simple API call.
- Result: The agent writes the enriched data, along with the source URL and timestamp, back into your table.
For products on Snowflake DB: You can initiate directly using external functions or Snowpark procedures, push results via Snowpipe, and schedule refreshes with Tasks. The architectural components are already there. You simply provide the enrichment logic.
Product requirements: How to spec trust
When drafting the PRD, move beyond simple data filling. Prioritize trust and freshness.
- Transparency: Always show the extracted value alongside its source URL. No data point should appear without a verifiable source.
- Configurable freshness: Let users control update frequency (Daily, Weekly, or On Demand) for each individual column.
- Observability: Track and monitor match rates, fill rates, data freshness latency, and cost per enriched row with the same rigor applied to uptime and latency.
Why now for your market?
This pattern is relevant to any table, in any industry.
Marketing: Go‑to‑market teams are making AI data enrichment the default. New leads and accounts arrive with fields like domain, headcount, tech stack and social presence pre-filled. This immediate enrichment improves routing, enables personalization from day one and helps increase conversion rates because the key columns are complete from the first touch.
Retail: Merchants now treat price, availability, and reviews as living dynamic data. SKUs are updated to reflect current market prices, stock signals and even image quality scores. With better visibility into competitors and channels, decisions on margins, assortment and replenishment are faster and less risky.
Finance: Risk teams enrich entities with ongoing updates on executive changes, adverse media and other risk indicators on a steady cadence. KYC and portfolio monitoring are performed earlier and more rapidly, reducing manual review time, and auditors gain clear lineage with citations and timestamps attached to every value.
Case study: See how Raylu enriches venture datasets with AI search and extract.
Best practices for high success rates and enterprise readiness
Clarity first
Define each signal precisely. Specify how to identify each row in the wild. Prefer unique and stable identifiers, such as domains, SKUs, or addresses.
Concurrency and throughput
Run requests in parallel, applying sensible caps. Batch intelligently to keep latency low and costs predictable.
Reliability
Use robust web access that handles JavaScript-heavy sites and anti‑bot controls. Implement retries with backoff and maintain idempotency.
Source transparency and explainability
Store source URLs, timestamps, extractor or prompt versions, and confidence scores. Every cell should be auditable.
Quality and evaluation
Track metrics like match rate, fill rate, accuracy (against a gold set), and freshness latency. Promote changes only when these metrics improve. Learn more about data quality metrics.
Cost control
Cache and archive frequently used sources. Reuse snapshots when real time is not required. Set stop conditions to prevent runaway loops. Consider strategies to reduce data collection costs.
Operations
Assign owners and SLAs for each enrichable column. Log every run. Set up alerts for failures and quality regressions. Schedule refreshes to align with business cadence. Review data collection best practices and data pipeline architecture.
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