AI-Powered B2B Customer Data Enrichment
A global metals retailer needed structured business intelligence on a customer base of over 200,000 B2B accounts. Clearlead developed an AI agent-based company research system, evaluated multiple underlying LLMs for the task, and benchmarked results against commercial data, finding the AI approach substantially more accurate, more customisable, and far less expensive.
The client
- A global retailer
- Manufacturing, Retail
- United States, Canada, and United Kingdom
Overview
The client is a global metals retailer serving a substantial B2B customer base across manufacturing, construction, and industrial sectors. Their customer database held a large number of records, but most contained only basic contact and transaction data, with no industry classification, company size information, or understanding of what each customer actually did.
Clearlead developed an AI agent-based approach to company research, in which an agent searches the web for each business using only a name and location, discovers the company website where one is not known, and returns structured business intelligence. Multiple underlying LLMs were evaluated for this agentic research task. Results were benchmarked against manual verification and against the client's commercial data, finding the AI approach substantially more accurate, more customisable, and far less expensive.
The client now holds enriched profiles across their active customer base of over 200,000 accounts, with structured data on industry classification, company size, and other commercial attributes that their existing records did not contain.
Methods
- Agentic web research
- LLM enrichment
- Multi-model evaluation
- Commercial data benchmarking
- B2B intelligence
Engagement type
- Design and build
The situation
The client operates a large B2B distribution network, selling metals in a wide range of grades and formats to businesses across manufacturing, construction, and industrial sectors. Their customer database contained a substantial number of records, but most held only basic contact and transaction data: a company name, a location, and purchase history. There was no industry classification, no company size data, and no understanding of what each customer actually did.
Without this information, it was not possible to segment the customer base meaningfully, prioritise sales outreach, or build the predictive models needed for territory planning and commercial forecasting. The client had access to business intelligence from a commercial data provider, but the accuracy of that data had never been independently tested. They engaged Clearlead to evaluate whether AI could produce richer, more reliable enrichment at scale, and to design and build a system capable of processing their full active customer base.
The engagement
Clearlead structured the work in two phases: a feasibility and benchmarking phase to validate the approach before any major development commitment, followed by a production build to process the full active customer base.
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AI agent-based company research and multi-model evaluation: the core approach used an AI agent to research each business using only a company name and location, searching the web to discover the company website where one was not recorded, and returning structured fields including industry classification, company size, revenue range, formation date, and confidence scores. Several underlying LLMs were evaluated for this agentic research task. Results were then benchmarked against manual verification of a representative sample, and against the client's existing commercial data. The commercial data turned out to be substantially inaccurate on the records checked. The AI agent approach produced meaningfully more reliable classifications, at substantially lower cost, with the added benefit that the fields returned and the confidence thresholds applied could be fully customised to the client's requirements.
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Pre-enrichment record classification: before web-research queries were made, a lightweight classifier distinguished genuine business records from records containing personal names rather than company names. A significant share of the database fell into this category. Routing these records away from the enrichment pipeline focused the workload on records where meaningful enrichment was possible, and avoided unnecessary processing cost.
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Production enrichment system: a processing system built to run the full active customer population through the agent pipeline reliably and at scale, with durable state management so progress was preserved across runs, monitoring across all processing activity, and delivery of the enriched profiles in a format ready for downstream use.
Outcomes
- AI agent-based enrichment was validated as substantially more accurate than the client's existing commercial data, as well as more customisable and far less expensive: the cost of enriching the full active customer base was a fraction of the equivalent commercial data purchase.
- The client now holds structured business intelligence across their active customer base of over 200,000 accounts, including industry classification, company size, revenue range, and confidence scores, where previously most records contained only a name, location, and transaction history.
- The enriched profiles directly enable the customer segmentation, sales prioritisation, territory planning, and predictive analytics work the client's data previously could not support.
Similar applications
The methods and architecture used in this engagement apply to a wide range of data enrichment and business intelligence challenges.
Company and entity enrichment
Any database of named organisations, properties, or professionals where structured attributes need to be discovered from public sources rather than entered manually.
Validating commercial data accuracy
Testing a commercial data provider's outputs against independent evidence before using them to drive decisions or build models.
Supplier and prospect profiling
Procurement or business development teams that need structured profiles on many organisations, where commercial databases are incomplete or inaccurate for their specific market.
Research and due diligence at volume
Workflows requiring structured information on many entities from public sources, where manual research is too slow but accuracy matters enough to warrant careful validation.
Dataset scoping before expensive processing
Large datasets where a lightweight upfront classifier identifies which records are worth enriching and which should be excluded, reducing cost and keeping the pipeline focused.
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