Solution
Intelligent document processing
Pipelines that extract document text and tables, classify content and produce structured records for databases, APIs and applications.
Architecture and implementation
Data extraction
Data extraction from files, scanned pages and structured content.
Classification and models
AI models and classification rules for identifying documents and structuring their information.
Application integration
Integration of extracted data with databases, APIs and application workflows.
Design decisions
Development of an extraction pipeline combining an output schema, format checks and references to source documents. An absent value remains distinct from one calculated or suggested by the model.
Illustrative implementation
Processing can extract document fields, retain supporting passages and route ambiguous cases for review before integration.
Engineering outputsExtraction pipeline, data schemas, checks and review interface
A Google Cloud platform brings market feeds and information extracted from financial documents into a BigQuery warehouse. One application is building a history of financial instruments and portfolios for analysis in Power BI and use by applications through APIs.
Apache Airflow
BigQuery
Cloud Run
Cloud Storage
dbt
GitHub
Power BI
Terraform
Vertex AI
Components and flows
- Source files retained in Cloud Storage and loaded into raw BigQuery tables
- dbt transformations and data quality checks before publishing fact and dimension tables
- Document extraction through Vertex AI, analysis in Power BI and application access through Cloud Run APIs
A private platform connects a research agent to a document index and a local Llama model, with retrieval filtered by access permissions. For example, it can support review of a contract file by locating relevant passages in the firm’s documents and reference texts, then preparing an answer with source references.
Docker
.NET
Microsoft Entra ID
Infomaniak
LangChain
Linux
Llama (Meta)
Model Context Protocol
Microsoft 365
PostgreSQL
Python
Components and flows
- Extraction of Microsoft 365 documents and reference collections, followed by text and vector indexing in PostgreSQL
- LangChain agent using a Llama model and read-only MCP search tools, with permission filtering and bounded tool calls
- Available context checked before generation, with references presented in a .NET application
Development and integration
- Analysis of PDFs, images and structured files
- OCR, extraction and classification
- Data schemas and exception handling
- Pipelines and integration interfaces
Systems involved
- Document management, email and storage
- ERP, CRM and databases
- AI services and business applications
Service lineArtificial intelligence →
Services by sector
Explore the engineering services offered across different sectors.
Banking and insurance
Document AI and analytical services
Data extraction and classification from financial or insurance documents, with source-passage references and field validation. Document search and API-connected agents operate within defined action and access permissions.
Retail and e-commerce
AI for catalogues and documents
Attribute extraction from supplier documents, catalogue search and assistants connected to authorised information. Generated data is validated before publication in commercial applications.
To go deeper
Four questions about the EU AI Act for a Swiss company
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Four AI agent workflows and their acceptance criteria
An agent connects information analysis with an organisation’s tools. Four examples show the integrations, expected results and checks needed to evaluate that work.
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