Delivering business value
We help our clients identify opportunities to exploit data to deliver business value. By ranking these opportunities based on technical feasibility, value, and strategic alignment, you can prioritise those with the biggest impact. We support you throughout your data exploitation lifecycle, from initial identification to experimentation and full-scale implementation and management.
What we do
Ideation and opportunity identification
We can bring our senior data and product experts on-site with your business stakeholders to identify the opportunities where data can support your strategic objectives. This is done in a two-fold method, both business strategy led and grassroots ideations with your teams.
Experimentation and proof of value
Rapid prototyping of ML and AI workloads to demonstrate viability of a potential solution, and the business ROI that can be achieved. Whether Generative-AI based applications such as document processing, intelligent workflows, or Agentic AI, through to ML-based solutions.
Productionisation and delivering value
Delivering our ML, AI, and BI products into live systems is the most critical step for creating a return on investment. And it takes more than a BI developer, or a data scientist or an AI engineer to do this. We look at how downstream systems need to be integrated and live data feeds are consumed. Plus, we identify business processes that may need changing to fully exploit the new technologies.
MLOps, AIOps, and path to scaling
We can help our clients build out the Data/ML/AIOps that they need to underpin and maintain the quality of their solutions, and the ML and AI platforms they need to rapidly prototype and productionise new solutions. Well monitored solutions with a DevOps mentality around continual quality and iteration is critical to consistently deliver business value.
Business process transformation
Building and delivering a solution into production isn’t a single isolated change - especially in the case of new, rather than upgraded, applications. It can often require making a real change to business processes - whether through reduction in manual effort with a new AI-based solution, or consuming automated, system-based output from an ML model.
Enterprise BI strategy
In addition to building and implementing BI dashboards and data analytics that give you the insight needed to make better decisions, we help to design an enterprise reporting strategy. This involves separating out the board-facing reports that need to have dependable, accurate data against the ad-hoc reports that don’t drive key strategic decisions. We design a service model appropriate to catering to both needs.
How we deliver
Crosstide has a range of locations and price points to support you at every stage of the data product lifecycle. From on-shore consulting to help identify opportunities and prototype potential solutions to cost-effective near-shore delivery with on-shore oversight.
Our technology ecosystem
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FAQs
Aren’t ML and AI quite different?
Absolutely. Integrating an AI-based solution requiresdifferent technical skills compared to building a custom ML solution from scratch. However, the mindset and mentality are related, and ultimately, these are all ways of building systems that can predict something.
It’s increasingly common for data scientists to be part of a team working to integrate and deploy an AI application. This scientist is well placed to spot opportunities that would be better implemented using machine learning (e.g. because of incremental performance improvement) rather than using commodity, off-the-shelf models.
What languages do you support for ML?
We use both Python and R for machine learning workloads. In a greenfield scenario, we would typically use Python, but some workloads (for example pharma and insurance) are better aligned to R.
What Generative AI solutions do you support?
We can work across the GenAI ecosystem. Our internal GenAI solutions are underpinned by the modular use of a self-hosted DeepSeek instance, as well as Claude, Gemini and OpenAI’s ChatGPT.
Why do my foundations matter?
Organisations are prototyping and trying to take AI or ML solutions into production. Many are finding that if they are not fully prepared, it becomes very laborious to deliver the solutions. This causes disappointment for business stakeholders. They may have seen working prototypes and don’t understand why technology functions are quoting such a long time to turn a working prototype into a usable, production-grade product.
Sorting out the foundations helps to ensure that, as much as possible, these barriers to entry are removed and we ease the path into production.
Furthermore, building on top of shifting sand, where there is a lack of data reliability or clarity, means your products might end up being exposed with incorrect predictions, and failing to deliver business value.
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