# Generative and multimodal reporting canonical_url: https://www.3.14financialcontents.com/en/solutions/informational-reporting-in-text-voice-and-video/ markdown_url: https://www.3.14financialcontents.com/en/solutions/informational-reporting-in-text-voice-and-video/index.md language: en content_type: solutions status: published description: From data and information to reporting in text, voice and video. ## Qué podemos enseñar - **Cómo funciona** — We explicitly separate sources, informational representation, calculation, content selection, NLG planning, linguistic realisation and multimodal rendering. This separation makes it possible to change models, languages, audiences or formats without turning the whole reporting chain into a single generative call. Data and constraints travel as typed structures and can be reused by different forms of output. - **Qué podemos mostrar** — The system can preserve the relationship between an output and the data, sources, calculations and transformations that took part in generating it. We can examine the input information, the normalised structures, the calculations, the content selection, the narrative plan, the linguistic output and the validation results. This makes it possible to analyse not only what the system produced, but also what information it used and what transformations it applied to produce it. - **Cómo se evalúa** — We do not treat quality as a single score. Properties that can be checked exactly — figures, signs, dates, names, units, periods, relationships or structural constraints — are validated through code where possible. Coverage, semantic fidelity, coherence, terminology, naturalness and fitness for the audience require different evaluation mechanisms. Model-based evaluators can be part of the system, but they do not replace deterministic checks where those exist. - **Límites** — Structured generation reduces errors and improves control, but it does not automatically make any output correct. The final quality also depends on the quality and currency of the data, on the selection rules, on the calculations performed, on the available context and on the criteria used to evaluate the output. Voice and video additionally bring constraints of their own regarding pronunciation, synchronisation, visual selection, timing and correspondence between modalities. We design these layers according to the domain, the risk and the level of autonomy each system actually needs. - **Estado** — evaluation We combine information processing, calculation, NLG, generative models and validation inside an architecture where each component takes on what it can do best. ## From structured information to multimodal generation. **Generation begins before any drafting.** The system gathers information, normalises data, identifies entities and relationships, calculates whatever can be calculated and builds a structured representation of the available content. From that representation it can decide what information is relevant, how to organise it, what level of detail each audience needs and what constraints it must preserve during generation. Generative models come into that architecture as components of linguistic realisation, explanation and adaptation, not as substitutes for the data, calculation, knowledge or validation layers. A single informational base can produce a report, a briefing, an alert, a narrative, subtitles or a video piece without rebuilding the whole process from scratch for each format. ## What we build ### Ingestion and normalisation of information **Integrating heterogeneous sources into a usable representation.** We connect structured data, APIs, documents, events, publications and other sources. We normalise formats, entities, dates, units, relationships and context so that the rest of the system can work on a common base. ### Informational representation **Separating the available information from the way it will later be communicated.** We structure facts, metrics, entities, events, relationships, periods, context and metadata through typed schemas. Deterministic calculations and transformations are carried out before the linguistic generation whenever they can be resolved exactly in code. ### Selection and NLG planning **Deciding what to communicate before deciding how to express it.** The system selects information according to objective, audience, context and moment. It can establish priorities, depth, order, narrative relationships, length and structure before asking the model for the linguistic realisation. Content planning stays separate from drafting. ### Controlled generation **Using generative models where they contribute linguistic flexibility.** The models turn structures and content plans into natural language conditioned by terminology, format, audience, language, length and content constraints. The architecture avoids delegating to the model tasks that can be better resolved through structured data, logic or deterministic calculation. ### Adaptation by audience and context **The same information may need different explanations.** From a common representation, versions can be generated with different depth, register, language, duration, structure or level of prior knowledge. The system distinguishes between information that must be preserved and aspects of the communication that can be adapted. ### Multimodal realisation **Text, voice and video as different ways of expressing a single informational base.** The system can produce reports, scripts, narrations, audio, subtitles, graphics and video. Each modality introduces constraints of its own regarding duration, pacing, pronunciation, segmentation, synchronisation and correspondence between language and visual elements. ### Validation and evaluation **Checking data, content and linguistic realisation in different layers.** We can validate figures, dates, names, signs, units, periods, entities, relationships and structural constraints against the data the system used. Separately, we evaluate properties such as coverage, coherence, semantic fidelity, terminology, naturalness, fitness for the audience and correspondence between modalities. ## An architecture that separates information, planning and generation. **Not every decision inside a generative system has to be generative.** ### Deterministic Calculations · figures · dates · units · identifiers · exact transformations ### Structured Entities · events · metrics · relationships · context · metadata · content plans ### Generative Drafting · explanation · synthesis · reformulation · adaptation · narrative composition ### Evaluated Coverage · consistency · terminology · semantic fidelity · naturalness · multimodal fitness ## One information system, multiple reporting formats - Automated, recurring reporting - Alerts and briefings based on data or events - Reports and executive summaries - Explanations generated from structured information - Narratives adapted by audience - Multilingual and localised reporting - Generated audio and narrations - Subtitles and synchronised versions - Video generated from a single informational base - Multi-format systems where text, voice and video share data, context and controls ## Architectural principle > First we structure the information. Then we decide how to communicate it. Data, calculations, entities, context and constraints are kept separate from the linguistic realisation. Generative models contribute flexibility where it is useful, while logic, structures and validators control whatever can be resolved more precisely outside the model. ## One informational base. Many realisations. Separating information from linguistic realisation makes it possible to reuse a single representation across different products and channels. The same set of data can feed a twenty-second briefing, a long report, an explanation for a general audience, another for specialists, different languages, an audio narration and an audiovisual piece. **What changes is the way of communicating. The underlying information can remain common.** ## Components of the architecture ### Data and information Ingestion · APIs · normalisation · structured extraction · typed schemas · entity resolution · temporal context ### Representation and knowledge Entities · relationships · events · metrics · taxonomies · terminology · context · metadata ### NLG Content selection · document planning · conditioned generation · structured generation · templates · LLMs · constraints ### Calculation and control Deterministic transformations · rules · calculations · validation of figures · units · dates · entities · consistency ### Linguistic evaluation Semantic fidelity · coverage · entailment · terminology · coherence · criterion-based evaluation ### Multimodal TTS · ASR · subtitling · temporal alignment · synchronisation · audiovisual composition ### Operation Observability · versioning · traceability · continuous evaluation · human-in-the-loop according to risk ## Generation is only one layer of the system. The most useful reporting systems do not begin by asking a model what it should write. They begin by organising data, context, calculations and communication objectives. From there, NLG and generative models make it possible to turn that information into explanations, reports and narratives adapted to different people, languages and media. **We design the complete chain: from the data and the information to their expression in text, voice and video.** --- https://www.3.14financialcontents.com/llms-en.txt · https://www.3.14financialcontents.com/llms-full-en.txt