Artificial Intelligence
Artificial intelligence solutions for business
ALS Innovation supports companies and institutions across every phase of an artificial intelligence project, from the initial assessment through to production and subsequent maintenance. We analyse each case to determine where the technology adds value and design the corresponding solution.
An end-to-end approach to the project
We analyse processes, available data and business objectives before any development begins. The result is a map of use cases prioritised by return, cost and technical feasibility.
Models that anticipate demand, prices, default risk or customer behaviour, trained on the organisation’s own data and validated against real outcomes before entering production.
RPA, intelligent workflows and system integration to reduce repetitive manual workload and the errors associated with manual data entry.
Virtual assistants and chatbots for query resolution, incident management and request processing, integrated into existing service channels.
We develop our own software and adapt it to each client: a project management and invoicing ERP, time tracking and presence management, and AccouRate® for financial analysis, alongside bespoke developments where the activity requires them.
All our developments comply with the GDPR and are supported by an information security management system certified to ISO/IEC 27001, with access controls, traceability and continuous improvement.
Identifying and prioritising the use case
Every project starts from a specific business objective: the decision to be improved, the information available to support it and the outcome expected. On that basis we determine the appropriate technical approach and the scope of the solution, so that the development addresses a measurable need from the outset.
Audit of available data
We review what information the organisation captures, at what quality and in which systems it resides, and establish which sources can be put to use for each case.
Prioritisation by expected return
We rank use cases by estimated economic impact, development effort and execution risk. Starting with a bounded, measurable case facilitates validation of subsequent projects.
Bounded proof of concept
We validate the hypothesis with a reduced scope and success criteria defined in advance, before committing the budget for a full development.
From proof of concept to production
Moving from a proof of concept to a production solution requires integration with existing systems, performance monitoring over time, and training for the teams that will use it on a regular basis. We take on the full project cycle, including maintenance after go-live.
Integration with existing systems
We connect the solution to the ERP, CRM, data warehouse or spreadsheets in use, so that it fits into the current workflow without duplicating tasks or disrupting daily operations.
Monitoring and retraining
We establish the tracking metrics and review schedules that maintain the solution’s performance as the data and the organisation’s activity evolve.
Training and change management
We train the teams that will work with the tool and support the organisational transition, a decisive factor in the effective adoption of any solution.
Working methodology
Assessment
We analyse processes, data and objectives, and deliver a report setting out the feasible use cases, the estimated impact of each, and a prioritised roadmap.
Solution design
We define the architecture, data sources, success criteria and scope of each phase, with timelines and budget agreed before work begins.
Development and integration
We build the solution in short, reviewable iterations, integrating it with existing systems and validating results against real data at each phase.
Go-live and follow-up
We deploy the solution, train the teams and establish the monitoring required to maintain performance as the data or the organisation’s activity changes.
FAQs
Frequently asked questions about AI projects
How is an artificial intelligence project budgeted?
The budget is set according to the agreed scope. We recommend starting with the assessment, which identifies and quantifies the feasible use cases and allows each development to be budgeted accurately before it is undertaken.
What data is needed to get started?
We work with the information the organisation already generates. Certain use cases draw on extensive history, such as demand forecasting or risk scoring, while others build on pre-trained models requiring only a limited volume of proprietary data, such as conversational assistants or document information extraction.
How long does it take to see a return?
Automation projects return within weeks, given that the hours saved are measurable from go-live. In predictive projects the return consolidates once the model has been validated against real outcomes.
How is data protection ensured?
All our solutions comply with the GDPR. We operate an information security management system certified to ISO/IEC 27001, with a risk-based approach, access controls and continuous improvement. Where the case requires it, we design architectures in which data does not leave the client’s infrastructure.
Do you work only with large companies?
No. We work with corporations, SMEs and startups. What matters is the volume of repeated decisions taken within the organisation and the information available to support them.
Is it possible to start with a project of limited scope?
That is the approach we recommend. A bounded use case, with measurable success criteria and a short timeline, validates the approach and establishes the basis for taking on larger projects.
ALS Innovation
Tell us which processes carry the greatest workload, or which decisions are currently taken without sufficient information. We will assess whether artificial intelligence can add value in your case, and how.