150,000 records enriched every week.
Capacity up 500%.
A machine learning and NLP pipeline enriches sparse business records at scale, turning a 13 million-company index into a sellable, API-first data product.
per week
and lower overhead
in the index
portal to PaaS
Client
A global business information platform holding records on over 13 million companies, used to power a sales-navigator-style prospecting and sales experience.
Goal
Transform an AI-based business data platform so machine learning, NLP and vector-based pattern matching could enrich sparse seed data into augmented datasets at scale, for prospecting and sales.
A 13 million-record platform that cost more to run than it earned.
Hosting and data processing costs were uneconomical against the revenue the platform generated, so routine enrichment and batch processing didn’t pay for themselves. The product was built consumer-first, and consumer acquisition costs were too high to ever reach profitability at that scale.
Underneath it, engineering had no standard way of working. There was no documentation, no formal product management process, and delivery kept missing deadlines because there was no structure to hold it together.
Three constraints were non-negotiable:
- Uneconomical hosting and data processing costs
- Consumer acquisition costs too high for profitability
- No standardisation, missed deadlines, no engineering structure
From consumer portal to enterprise data engine.
Data operations moved to a tier 2 cloud provider, done before MLOps was an established practice, which increased processing capacity and cut operational overhead by over 500%. Enrichment jobs could run at far higher throughput without the cost climbing at the same rate.
The fix wasn’t more customers. It was turning the same data into something enterprises would pay to resell themselves.
The product itself changed shape. Instead of relying on a consumer-facing portal, the platform became an API-centric Platform-as-a-Service, so the enriched business data could be resold directly to global aggregators rather than acquired one user at a time.
Alongside the migration, the business put engineering on a proper footing: full documentation, formal product management processes, and an Agile approach that gave both engineering and the Board visibility into progress. A retention programme kept the key AI talent in place, and leadership backed engineering’s own ideas to get value out faster.
150,000 records richer, every week.
The platform went from an expensive, consumer-grade product to a scalable, profitable enterprise information service. Cheaper processing, a resale-ready data product, and an engineering team that could finally hit its deadlines, together turned a 13 million-record dataset into recurring, higher-margin revenue.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.