Bringing AI into your product: recommendations, search, and content generation

AI delivers personalized recommendations: suggesting the user's next action, product, or content based on their behavior and the behavior of audiences with similar interests or needs.

Examples of use: suggesting the next step, recommending similar products or related content, reminding users of useful features when their activity drops, prompting them to try new features.

Price depends on the number of recommendation types, the volume and quality of available data, the complexity of A/B testing, and integration with an analytics system to measure results.

AI searches by the meaning of a query rather than an exact keyword match: users find what they need regardless of phrasing.

Examples of use: searching for products in a catalog using a descriptive query, finding articles and answers in a knowledge base or FAQ without exact phrasing, searching data by meaning rather than by tags alone.

Price depends on the volume of the database to be indexed, languages, the complexity of integrating with existing search, and whether real-time indexing is needed when content changes.

Content generation directly within the product interface, based on data already in the system: document templates, product descriptions, brief summaries and reports.

Examples of use: proposals and letters based on client data, SEO-optimized product descriptions from given parameters, brief report summaries, translating content into multiple languages.

Price depends on the number of generation types, the complexity of prompt engineering, languages, and whether Human-in-the-Loop validation is needed before publication.

For clients working in Spiro: adding AI functionality to your company's existing business processes.

Examples of use: detecting anomalies and suspicious requests, automatic alerts for deviations from the norm, categorizing and prioritizing tasks, analytics with explanations for changes in performance, natural language answers instead of manually searching through reports.

Price depends on the number of AI features, the complexity of existing processes, the volume and quality of data for analysis, and whether natural language query capability is needed.