Data and document optimisation for Artificial Intelligence and LLM models

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Although many today use LLMs in their day-to-day operations, the real engine that turns these models into truly useful tools is data. And, more specifically, business data: the raw material that makes each service unique and valuable.

At GesvaltData we already have the 80 % of our datasets feeding the LLMs we use on a daily basis. We are experts in shaping internal data models into information ready to be exploited by LLMs.

We have incorporated all our appraisals, valuations and valuations into our models. desktopThe automation is fed by real, verified, high-value data, quality controls and audits, so that the automation is fed by real, verified, high-value data.
The result: faster decisions, more efficient processes and business insight that no generic model can match.

What is data optimisation for AI and LLM models?

Data optimisation for AI and LLM models is the process of transforming, structuring and enriching a company's raw data so that it can be interpreted and used by artificial intelligence systems. It is not just about digitising documents or having an organised database, but about preparing each piece of data with a semantic context that allows a generative model to understand, relate and exploit it to the fullest.
In a business environment, this optimisation makes the difference between getting generic answers or getting accurate analysis and conclusions based on high-value internal information. 

From unstructured data to AI-exploitable knowledge

Most organisations accumulate large volumes of information in scattered formats: PDFs, spreadsheets, emails, scanned reports. Without a clear structure, this data cannot be automatically exploited by LLMs.
Our job is to convert that mass of information into a corpus ordered and contextualised, ready to feed artificial intelligence.

Technical and semantic readiness to integrate information into generative models

It is not enough to provide access to documents: it is necessary to carry out semantic standardisation, resolve ambiguities, unify terminology and enrich the data with meta-information. It is this preliminary work that allows a generative model to provide relevant, accurate and contextualised answers.

What services do we offer to prepare your data and documents for IA?

At GesvaltData we cover the entire data preparation and optimisation cycle so that you can integrate artificial intelligence into your business without technical friction.

Curation and structuring of databases for virtual assistants and AI engines

We review, clean and organise information to eliminate redundancies, errors and obsolete data, creating structures ready to feed into LLM models.

Embedding generation, normalisation and semantic disambiguation

We apply advanced techniques to represent the meaning of data, unify concepts and avoid misinterpretation by the model.

Preparation of documentary corpus from PDFs, contracts, simple notes, etc.

We digitise, process and structure all types of documents so that they can be securely consulted and exploited by IA.

Exploiting unstructured information through LLMs

We create workflows that allow you to query, search and extract key information from sparse data using natural language.

Development of intelligent agents for portfolio queries, reports or market analysis

We implement virtual assistants capable of providing contextualised answers to specific questions about assets, operations and corporate documentation.

What are the benefits of preparing your data for generative models?

Increased performance and accuracy in conversational assistants

More relevant and specific answers thanks to trained data with its own context.

Instant access to knowledge hidden in documents

Retrieve key information in seconds, without the need to manually review hundreds of pages.

Automation of internal processes with accurate and contextual responses

From validations to automatic reporting based on real data.

Time savings in document analysis, reporting and support tasks.

It reduces repetitive work hours and frees up resources for higher value tasks.

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Use cases of data and document integration with AI

Trained virtual assistants with internal regulations or simple notes

It automatically responds to technical, legal or administrative queries.

Natural language queries on asset portfolios or technical documents

It allows any user, without technical knowledge, to obtain accurate information.

Generative AI for pre-analysing contracts and legal documents

Automatically detects key clauses, inconsistencies and risks.

What is the data preparation process for IA and LLMs like?

Initial analysis and assessment of the type of data or documents

We identify sources, formats and quality of information available.

Curation, normalisation and semantic structuring

We transform sparse data into a homogeneous, AI-understandable set.

Training or integration with LLMs model

We adapt the data so that it can be optimally consumed by the chosen model.

System validation, tuning and deployment

We test performance, correct deviations and implement the solution.

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Do you want to turn your data into smart knowledge?

Transform your information into a real competitive advantage.

Frequently asked questions on data optimisation for artificial intelligence

What is the difference between a structured database and one optimised for LLMs?

A structured database contains data that is organised, but not necessarily ready for an LLM to understand and relate. Optimisation adds context, semantics and relationships between elements.

Yes, provided they are processed by OCR, structured and enriched with appropriate metadata.

We work with both open and private models, from GPT and Claude to solutions on-premise.

We offer both options, depending on your security and compliance requirements.

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