Data, Software & AI – Frequently Asked Questions (FAQs)

Scriptoria’s in‑house Data, Software & AI Team helps organisations to bring together (integrate), manage, analyse, and use complex data – delivering secure, cloud‑based and hybrid software systems that support data analysis, programme delivery, reporting, and decision‑making.

The type of systems built range from relational databases (used to manage and curate data), to data frameworks, multi-dimensional cubes, and AI systems (to explore and analyse data).

Scriptoria’s Data, Software & AI Team builds a range of systems requested by clients. One end of the spectrum is the design and build of relational databases at the start of programmes – ensuring that data is collected, structured, and governed efficiently from day one. An example is the Germplasm Laboratory Analysis Request Tracking System we built for the CGIAR.

At the other end of the spectrum, clients might ask us to review, clean, and integrate large historical datasets spanning decades, and to consolidate them into a single, trusted source of truth. A good example of this is the Desert Locust Pest Tracking System and multi-dimensional analytics cube developed for the United Nations Food and Agriculture Organization (FAO).

We work with organisations worldwide focused on doing good and improving the world. This includes: 

• Donors 

• Donor‑funded international development and research programmes 

• Research organisations and universities 

• Non‑profits, charities, and other mission‑driven organisations 

Many of our data systems support multi‑country, multi‑partner programmes operating in complex or low‑resource environments. 

Our work sits at the intersection of advanced software engineering, data and AI architecture, and deep scientific and domain expertise in international development. We are not just technologists building systems around data – we understand the science that generates the data, and the analytical questions that data is meant to answer. 

The key strengths of the Data, Software & AI Team include: 

• Senior software, data architecture, and AI expertise, covering secure cloud systems, complex integrations, analytics platforms, and AI-ready data pipelines 

• In-house analysts with deep subject-matter expertise across international development, including agriculture and food systems, water and climate, health, and livelihoods – including team members with PhDs and extensive research experience 

• Ability to work directly with scientific and research data, understanding experimental design, indicators, uncertainty, and data quality, not just database structures 

• Registered Microsoft Partner, delivering secure, enterprise-grade cloud and hybrid systems 

• Extensive experience in low-resource and low-bandwidth environments, including multi-country and multi-partner programmes 

• Integrated delivery teams, where programmers, data architects, and analysts work closely with programme managers, researchers, and MEL specialists 

• Over 20 years’ experience delivering data-driven systems for research, policy, and programme delivery 

Because we combine technical excellence with scientific understanding and development-sector experience, we can rapidly understand client needs and translate them into practical software, data architecture, and AI solutions. The result is systems that accurately reflect the underlying science, support meaningful analysis, and work in real-world delivery contexts. 

No. While we work extensively with structured research and survey data, many of our systems are designed specifically to handle the messy, complex, real-world data environments common in international development programmes. 

This includes: 

• Operational and programme management data 

• Financial, budget, and spend-tracking data 

• Large, heterogeneous, and partially unstructured datasets 

• Data drawn from multiple historical and live sources, often owned by different partners 

• Resources such as documents, videos and images 

In practice, this means designing systems that can integrate and reconcile data collected for different purposes, at different times, and to different standards – and turning it into something that can actually be analysed, visualised, and used for decision-making. 

A good example is our work for the Tropical Legumes Program, funded by the Bill & Melinda Gates Foundation. For this programme, we not only integrated a wide range of large, complex datasets using data warehousing, multi-dimensional cubes, and machine-learning approaches, but also built systems to manage and surface unstructured programme outputs. This included creating a resource centre that links research papers, policy briefs, and other outputs directly to the underlying research data that supports them. 

This combination (integrating structured and unstructured data, linking evidence to outputs, and making the resulting data AI- and analytics-ready) is typical of the work we do across research and development programmes. 

A data cube is a multi‑dimensional data structure that allows users to analyse large datasets quickly across multiple dimensions; for example geography, time, programme component, funding stream, or outcome area. 

Data cubes are particularly valuable when data comes from multiple sources (e.g. many separate databases) or spans many years, and when users need to ask complex questions that would be difficult or slow to answer using conventional databases. 

Yes. We have built and maintained multi‑dimensional, cloud‑based data cubes for many major international programmes and organisations ranging from the United Nations to the UK FCDO and Gates Foundation. A good example is the work we did the Gates Foundation‑funded West African Virus Epidemiology (WAVE) programme. In that case we worked with researchers from around the world to combine five years of field and laboratory data into a single analytical system. This enabled rapid exploration of virus spread across countries and time periods; analyses that was not possible when data was held in separate repositories. 

Yes. We design and build dashboards and visualisations (using tools like Power BI) to help teams review, explore, and understand their data. 

However, dashboards are only one part of what we deliver. Scriptoria specialises in data integration and in building the analytical back-end that dashboards rely on. Rather than visualising disconnected datasets, we bring data together from multiple systems to create a single source of truth before any dashboards are built. 

In many research and international development programmes, data exists across multiple databases, spreadsheets, platforms, and partner systems. Even when all the data technically exists, fragmentation, inconsistent structures, and weak governance prevent teams from answering important analytical and decision-making questions. 

We address this by integrating data from multiple historical and live sources into a coherent data architecture. This typically includes relational databases, data warehouses, and multi-dimensional analytical models designed to support complex queries, analytics, dashboards, and AI-driven exploration. The result is a single, trusted, decision-ready source of truth that can be used consistently across reporting, learning, and decision-making. 

On top of this integrated analytical foundation, we build dashboards and visualisations that allow users to explore large, integrated datasets and generate insights that were not previously accessible – even when the underlying data already existed. 

Throughout, we prioritise data quality, governance, documentation, and sustainability, ensuring that dashboards accurately reflect the underlying data and remain reliable and useful over time. 

We design and build secure, cloud-based and hybrid software systems that replace or significantly enhance spreadsheet-based, email-driven, and manual processes commonly used in research and international development programmes. These systems are not standalone tools: they are designed to generate clean, structured, and well-governed data that can feed dashboards, analytics platforms, and AI-enabled analysis. 

Typical systems we deliver include: 

• Programme delivery, monitoring, and results-tracking platforms, designed to support MEL, learning, and adaptive management 

• Research data management and curation systems that structure data for analysis, reuse, and long-term value 

• Intranets, collaboration hubs, and Office 365-integrated environments that manage documents, workflows, and institutional knowledge 

• Timesheet, reporting, and workflow automation systems that create reliable operational and financial datasets 

• Stakeholder, partner, and interaction-tracking systems that capture engagement data across programmes and organisations 

These systems are intentionally designed to act as data foundations, not just operational tools. They feed into relational databases, data warehouses, and analytical back-ends that support dashboards, multi-dimensional analysis, and AI-driven exploration. This allows organisations to move from day-to-day data capture to integrated reporting, advanced analytics, and evidence-based decision-making. 

All systems are built for distributed, multi-country teams and are mobile-optimised, secure, and role-based. We prioritise data quality, governance, and interoperability so that the data generated by these systems remains reliable, reusable, and ready for dashboards, analytics, and future AI applications – rather than being locked into isolated tools. 

Yes. We design and build programme management systems that support delivery, documentation, financial and spend tracking, and impact and results monitoring across complex programmes. 

Our Data, Software & AI Team works closely with Scriptoria’s programme management consultants to ensure that systems are grounded in real delivery needs and aligned with donor requirements, results frameworks, and reporting cycles. 

In addition to designing bespoke systems, Scriptoria also offers its own prebuilt programme and grant management platform, developed specifically for international development and research projects. This provides a configurable foundation that can be adapted to different programme structures, donors, and reporting requirements. 

Our programme management systems are designed to do more than store information. They bring together operational, financial, and results data into a single, structured environment that can feed dashboards, analytics, and real-time reporting. 

For example, we have delivered donor-facing results-tracking systems that: 

• provide real-time overviews of programme progress, 

• link staff narrative reports directly to live data visualisations, and 

• generate automated alerts and notifications when updates are submitted or thresholds are reached. 

This allows programme teams and donors to move from periodic, manual reporting to continuous visibility, learning, and evidence-based decision-making. 

Yes. We design and build intranets and Office 365 systems that help teams collaborate, manage documents, and work more efficiently. We can support new initiatives or improve existing Office 365 and intranet setups. 

Yes. In addition to handling discrete structured datasets, we design big data solutions that gather, clean, and analyse large volumes of data. Where appropriate, we apply machine learning and artificial intelligence techniques to help clients explore patterns, trends, and relationships within their data. 

Yes. We often work with organisations to review, consolidate, and upgrade existing systems. This can include unifying historical data, improving data models, strengthening analytics, or modernising front‑end tools.

Yes. We are frequently brought in to ‘rescue’ data or systems that are not delivering value. We focus on understanding root causes such as data structure, architecture, governance, usability, or adoption; and then provide a clear, robust path forward 

Quality, reliability, and security are built into our systems from the outset, rather than added later. 

We do this through: 

• Rigorous data modelling and architecture design, ensuring systems are robust, scalable, and analytically sound 

• Structured testing and user acceptance cycles, including functional testing and user acceptance testing (UAT) 

• Secure cloud hosting, including Microsoft Azure environments aligned with recognised security best practices 

• Role-based access control, authentication, and audit trails, ensuring appropriate access and accountability 

• Clear documentation and user training, supporting consistent use and long-term sustainability 

Our systems are designed to be maintainable, adaptable, and secure over time, allowing them to evolve as programme needs, reporting requirements, and data volumes change – without compromising data quality or security.

A helpful starting point includes: 

• What decisions, reporting, or analysis the system must support 

• Key users and workflows 

• Existing data sources and formats 

• Constraints such as timelines, compliance, or security requirements 

If these are not yet clear, we can help define them. 

Yes. We’re happy to discuss your needs and provide a no‑obligation quotation.

Please contact Scriptoria via our main enquiries (enquiries@scriptoria.co.uk) route and ask for the Data, Software & AI team.