Data & AI That Pay Their Way for ISVs
AI only matters when it changes the economics or value of the product. We help software businesses test that quickly: build low-risk proofs of value, modernise the data foundation underneath them, and take the ideas that work into a secure, supportable production model.
Start with the outcome, not the model. Reduce your cost to serve, create something customers will pay for, or stop the experiment.
Make AI earn its place in the product roadmap
For software businesses, the most useful AI conversations are usually commercial rather than theoretical: can it reduce the cost of running the service, or can it make the product more valuable?
Claranet structures early work around those two outcomes. We identify a narrow use case, define what success would look like, build a proof of value with the minimum viable data and platform, and measure whether it deserves further investment.
Where the blocker is the data rather than the AI, we can modernise the underlying data platform using technologies such as Microsoft Fabric, Databricks, Snowflake and managed database services. This creates a more reliable foundation for analytics, customer reporting and AI features.
If the use case succeeds, Claranet can help take it into production with the cloud, application, security and managed service controls around it. Where partner investment is available for qualifying projects, we can also help identify funding that reduces the cost of proving the idea.
AI proof of value for software vendors: test commercial outcomes first
The quickest way to waste money on AI is to start with “we need AI” rather than a measurable problem. We help you shortlist use cases by value, data readiness, technical feasibility, risk and time-to-learn.
The first objective is not a production platform. It is evidence. Can the use case reduce handling time, support cost, manual engineering effort or operational overhead? Or can it create a new feature, insight or workflow customers value enough to pay for?
- Use-case discovery and scoring against business value and delivery risk.
- Rapid proof of concept / proof of value with explicit success criteria.
- Cost-to-serve and product-extension hypotheses tested separately.
- Security, data and governance constraints identified before scale-up.
Modern data platforms for ISVs and SaaS products
Many AI projects fail upstream because the relevant data is fragmented, inaccessible or inconsistent. Claranet can build the data layer needed for both product analytics and AI-enabled features.
Our portfolio spans data engineering, managed databases, analytics and modern platforms including Microsoft Fabric, Databricks and Snowflake. We select the tool around the workload rather than forcing every ISV into the same data architecture.
- Data architecture and engineering for analytics and product features.
- Microsoft Fabric, Databricks and Snowflake options alongside managed databases.
- Pipelines, governance and access patterns that make data reusable rather than trapped in silos.
- Managed data infrastructure so specialist operations do not fall back on the product team.
Taking AI from proof of concept to production in your product
A proof of concept is only useful if the successful ones can survive production. We help turn promising experiments into supportable product capabilities with the architecture, security, monitoring, data controls and cost model needed to run them properly.
That might mean an LLM-backed feature, classification or extraction workflow, an agentic process, predictive analytics, or a new customer insight surfaced from your platform data. The implementation follows the use case, not the other way around.
- Production architecture and integration with the existing application platform.
- Security, identity, logging and data-governance controls around AI workloads.
- Usage and cloud cost monitoring to protect product margin.
- Managed cloud and data services to keep the new capability supportable after launch.
Ready to find the AI use cases worth funding?
We can run a focused discovery, build the first proof of value and identify whether the next investment should be in the model, the data platform or neither.
Speak to a Data & AI specialist
Or call us on 0330 390 0507
Data and AI for ISVs: FAQs
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Start with a small number of measurable use cases. For software vendors, two useful categories are internal cost reduction and customer-facing product extension. Rank them by commercial value, data readiness, technical feasibility and risk.
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Yes. Claranet provides AI and machine-learning services and can support proof-of-concept work alongside the cloud, application and data engineering needed to make the result realistic.
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Yes. Claranet provides managed data infrastructure, database management and managed cloud services that can sit underneath production analytics and AI workloads.
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Not necessarily. Claranet works across Microsoft Fabric, Databricks, Snowflake and traditional managed databases. The data platform should fit the workload, skills, ecosystem and commercial model.
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Yes. Data engineering and analytics services can make operational or product data securely reusable for dashboards, embedded analytics, reporting and AI-enabled features.
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Potentially. Microsoft Azure Accelerate and AWS partner programmes can provide investments or credits for qualifying cloud, data and AI projects. Eligibility varies by workload, customer and programme rules, so it should be checked before it is included in a business case.