AI Data Preprocessor

Nextqore offers three subscription tiers based on monthly data volume. Standard starts at $1,200/month for up to 10,000 rows processed. Professional is $2,800/month for up to 30,000 rows. Enterprise is $10,000/month for up to 100,000 rows. All plans include AnySource Data Combiner, Data Context Builder, Analytics, Visualisation, Notifications, and Enterprise AI Connector. Additional data processing is available from $80–$120 per 1,000 rows depending on tier. Custom configurations are available for specialised requirements.

There is no minimum commitment — Nextqore's consumption-based pricing is designed to give enterprise teams the flexibility to start, scale, and structure engagements in line with their own procurement and deployment cycles. That said, based on customer experience, organisations that engage with the platform consistently over three to six months are where tangible results become clearly visible — data preparation timelines shorten, AI model accuracy improves, and the operational overhead of managing disparate data sources reduces measurably. Most customers find that a three to six month horizon from initial deployment through to production gives them the evidence base they need to scale confidently across additional use cases and business units.

A Nextqore subscription includes access to AnySource Data Combiner and Data Context Builder as core products, along with the Extensions relevant to your plan — Analytics, Machine Learning, Visualisation, and Notification. Onboarding, implementation support, and access to the Nextqore professional services team are scoped separately based on deployment complexity.

The economic case for Nextqore starts with a well-documented industry reality. According to the Anaconda State of Data Science 2020 report and Pragmatic Institute's analysis of the 80/20 rule in data science, data professionals spend up to 80% of their time finding, cleaning, and organising data — leaving only 20% for actual analysis and AI work. This means the majority of enterprise AI project budget is consumed before a single model runs. Nextqore systematically eliminates this bottleneck by handling preprocessing at platform level rather than through ad hoc engineering effort, shifting that ratio significantly in favour of productive AI work. Customers achieve AI deployment timelines more than 20% faster than industry average, meaning faster time to value on AI investments that typically run into millions of dollars. The platform also reduces ongoing AI maintenance costs by ensuring consistent, high-quality, contextualised data inputs that prevent costly model retraining cycles.

Yes — and the process is straightforward. Start by subscribing to the Starter tier, which gives your team sufficient capacity to run an initial proof-of-concept using a representative sample of your actual data sources. Define your target output metrics upfront, process your test dataset through the platform, and validate the results against those benchmarks. If the output meets your criteria, scaling to a full project is seamless — your configurations, mappings, and validated pipelines carry forward without rework. No parallel infrastructure, no throwaway effort. This approach fits naturally into enterprise procurement cycles where demonstrating measurable outcomes is a prerequisite for investment approval. To scope your proof-of-concept, schedule an initial conversation with the Nextqore team.

Nextqore's consumption-based model is designed to scale economically as AI initiatives expand. The per-unit cost of data processing decreases as platform usage increases — Standard tier processes additional rows at $120 per 1,000 rows, Professional at $100 per 1,000 rows, and Enterprise at $80 per 1,000 rows — meaning organisations that grow into higher usage tiers benefit from progressively lower unit economics on every row processed. This ensures that as AI initiatives expand across additional business units, geographies, or data source types, the cost of data preprocessing scales proportionally but not linearly — the more you use the platform, the more cost-efficient each unit of data processing becomes. Organisations are not locked into fixed capacity that either constrains growth or forces payment for headroom they don't use, and pricing remains predictable and aligned with the business value being generated at every stage of the AI deployment journey.