AI Business Data Platform Adds 150k Records/Week and Boosts Capacity by 500%

Context

A global business information platform managing records for over 13 million companies underwent a strategic AI-driven transformation. The platform combined machine learning algorithms, Natural Language Processing and vector-based pattern matching to enrich sparse seed data and generate augmented datasets. These enriched records powered a sales navigator-style experience for prospecting and sales teams, enabling smarter segmentation, contextual search and higher-quality lead scoring across multiple markets. The objective was to convert a large but underutilised dataset into a scalable information service that could be consumed by enterprise customers and data aggregators.

Challenges

The initiative began with several critical challenges. Hosting and data processing costs were uneconomical relative to the revenue generated, making routine enrichment and batch processing financially unsustainable. The engineering organisation lacked a standardised approach to data operations and product delivery, which led to inconsistent outputs, missed deadlines and a loss of predictability. The original consumer-focused product strategy exacerbated the problems: acquisition costs were too high to create profitability at scale, and reliance on a front-end portal limited enterprise resale opportunities. Compounding these issues was an early-stage ML toolchain developed before modern MLOps practices were established, so operationalising models was brittle and labour-intensive.

Implementation

The team implemented a multi-pronged transformation to address cost, scale and product-market fit. Data operations were migrated to a tier 2 cloud provider (completed before MLOps practices were broadly adopted), which dramatically increased processing capacity and reduced operational overhead by over 500%, enabling higher-throughput enrichment jobs without proportionate cost growth. The product strategy shifted from a consumer-facing portal to an API-centric Platform-as-a-Service (PaaS), allowing enriched business data to be re-sold to global aggregators and large B2B customers rather than relying exclusively on single-user acquisition funnels.

Operational maturity was addressed through standardisation: full documentation and formal product management processes were introduced, and an Agile delivery framework created transparency between engineering teams and the Board. This transparency restored stakeholder confidence, improved prioritisation and reduced scope creep. Recognising people as a critical asset, a targeted retention programme was launched for key AI engineers and data scientists. Leadership made a point of listening to engineering suggestions and rapidly piloting promising ideas, which accelerated the delivery of high-value features and improved morale.

On the technical front, machine learning pipelines, NLP enrichers and vector-based pattern matching were formalised into repeatable processes. Augmentation workflows were optimised to incrementally add high-quality attributes to existing records, and a continuous ingestion cadence was established so that new augmented datasets could be committed weekly.

Results

The transformation delivered measurable commercial and operational benefits. Augmented data sets began to be added at roughly 150,000 records per week across the 13 million-record index, significantly increasing the platform’s usable intelligence for prospecting and analytics. The migration to a tier 2 provider increased processing capacity and slashed operational overhead by over 500%, enabling faster model runtimes and lower per-record enrichment costs. Shifting to an API-first PaaS model opened new distribution channels: the enriched datasets could be monetised via reseller agreements with global aggregators and B2B customers, making the service less dependent on portal subscriptions and reducing customer acquisition costs.

Financially, the transition to a proper B2B service improved the company’s P&L position by increasing recurring, higher-margin revenue streams and lowering variable costs tied to consumer acquisition. The combination of standardised engineering practices, documentation and Agile governance reduced missed deadlines and improved delivery predictability. The retention programme and responsive product leadership preserved institutional knowledge and unlocked rapid, high-impact engineering innovations. In aggregate, these changes converted an expensive, consumer-grade solution into a scalable, profitable enterprise information service that now delivers enriched business intelligence at scale.