From 16,000 local businesses to a targeted, automated prospecting workflow.
The business question was not “how do we collect more data?” It was which organisations should we spend time approaching? Public company data, AI-assisted qualification and automation were used to turn a broad local market into a practical shortlist.
“Find local businesses” is easy. Finding the right ones is not.
The client wanted to grow locally in Bexley, but a broad company list would simply create more manual work.
The real requirement was to identify organisations that were more likely to fit the client's training, business-improvement and digital-services offer, then turn that intelligence into a repeatable outreach process.
The addressable market was too large for indiscriminate research.
Companies House API constraints and the number of records meant the extraction had to be batched systematically. Records then needed cleaning, age/location filtering, scoring and enrichment before they were genuinely useful for outreach.
- ~16,000 local companies, no way to tell fit
- Searching for companies, not prioritising opportunities
- Manual, indiscriminate research
- Companies House API limits meant slow, unfiltered extraction
- ~1,000 prioritised opportunities across two segments
- AI-assisted scoring surfaces who's worth investigating
- Enriched with relevant decision-maker information
- A repeatable, end-to-end outreach workflow
The five-step shape behind every Radomentis engagement.
Move from market noise to a shortlist the business can act on.
Each stage answered a practical commercial question. The technology sat underneath the story rather than becoming the story itself.
Direct prospects likely to buy training or business-improvement services.
Growth/change signals suggesting a need for digital or implementation support.
A decision-ready prospecting view, not just a data extract.
The client received a structured way to understand the local market, see which organisations best matched the offer and focus human outreach where it was most likely to be worthwhile. The technical work happened in the background so the business could concentrate on the decision.
The success criterion was delivery of the targeted prospecting system itself. Early outreach also produced encouraging lead responses, but the case study does not depend on unverified conversion claims.
Outbound execution, supported by inbound measurement advice.
Outbound growth workflow
Python extraction and batching, data preparation, AI-assisted scoring, prospect segmentation, contact enrichment and outreach workflow.
Inbound measurement layer
Guidance on SEO/GEO, Google Tag Manager, Google Analytics, what to track and how to use measurement to improve lead generation. This advisory layer was not implemented as part of the build.
Client and commercial details are anonymised for confidentiality, and figures are approximate. I'm happy to talk through the approach in more detail, and what it would look like in your business — just ask on the call.
Could your next customer already be visible in the data you are not using?
Radomentis can turn public and internal data into a clear prospecting story: where the market is, which organisations fit, who matters inside them and where your team should focus next.
Client identity and certain operational details have been anonymised. The case study focuses on the business challenge, approach, technologies and outcomes that can be shared publicly. Illustrative visuals use synthetic data.