Making document workflows more reliable
How do you prototype a document ingestion pipeline that is traceable, rerunnable and evaluated before deployment?
3500 € per session, excl. VAT
In short
Two days, totalling 14 hours excluding breaks, to design a document ingestion prototype: collection, extraction, normalisation, enrichment and quality control. Participants test recovery from errors, measure results against an annotated corpus and prepare a pilot. Generative AI is used only where its contribution can be verified.
Why this training?
A fragile ingestion process propagates duplicates, missing metadata and poorly extracted text all the way through to document search. Without stable identifiers or an execution log, correcting a document sometimes requires the entire corpus to be reprocessed. Generative AI can suggest classifications and extract fields, but it can also invent a missing value. A controlled pipeline separates deterministic transformations, model suggestions, documentary evidence and human validation. Running case study: Aveltrec Inspection, a fictitious organisation, receives PDF reports, scans and revised instruction documents. Its training corpus contains duplicates, illegible tables and conflicting versions. The prototype must populate a test document environment without losing sources or access restrictions. Transfer to the workplace: Have the pilot scope validated by those responsible for document management, IT and security, then rerun the protocol on an authorised corpus. Decide whether to extend it based on the anomalies, quality measurements and operational constraints observed.
Objectives of this training
- Map the sources, permissions and events that trigger ingestion.
- Define a data contract and versioning rules.
- Assemble a prototype with controlled extraction and enrichment.
- Test recovery, duplicates and non-compliant documents.
- Measure quality and formalise the conditions for the pilot.
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Documentation managers and document engineers managing multi-format workflows.
- Data engineers and AI leads responsible for prototyping an ingestion pipeline.
Prior skills
- Understand metadata, document formats and access principles.
- Be able to modify a script or configure an automated workflow.
- Have practical experience with structured outputs from generative AI and their verification.
What you will work on
Define the scope of the flows
Objective: Define the scope of the prototype. Sources, formats, frequency, permissions and destination.
Establish a data contract
Objective: Stabilise the expected outputs. Identifiers, digital fingerprints, versions, mandatory fields and provenance.
Extract without concealing losses
Objective: Check text and structure. Native extraction, optical character recognition, tables and pagination.
Set controls for generative enrichment
Objective: Obtain verifiable fields. Structured outputs, missing values, evidence and malicious instructions in documents.
Assemble the pipeline
Objective: Run a complete batch. Steps, validations, intermediate storage and propagation of permissions.
Manage changes
Objective: Avoid inconsistent processing. Idempotence, duplicates, new versions and deletions.
Trigger incidents
Objective: Verify recovery. Delays, limited retries, quarantine and checkpoints.
Evaluate quality
Objective: Quantify defects. Field accuracy, completeness, rejections and comparison without AI.
Prepare for pilot operation
Objective: Make operation observable. Sanitised logs, alerts, durations, consumption and retention.
Decide on the pilot
Objective: Make the case for a limited trial. Responsibilities, stopping criteria, rollback and human validation.
What you take away
- Ingestion contract and architecture diagram.
- Executable prototype and version-controlled configuration.
- Reference corpus and test report.
- Recovery procedure and pilot plan.
Method and assessment
- Short instructional sessions followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of the outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare a fictitious or anonymised corpus, with annotated expected results. Check access to an isolated environment and to the tools authorised by the organisation.
Observable criteria: Each output retains its identifier, version, source and access restrictions. ; Rerunning the same batch creates no additional duplicates. ; Injected errors are logged and routed for recovery or quarantine. ; Extraction and completeness metrics are calculated against the reference annotations. ; The pilot plan specifies acceptance thresholds, responsible parties and rollback..
Frequently asked questions
Will the pipeline be in production?
No, the outcome is a prototype and a pilot plan.
Does AI need to process every document?
No, deterministic processing remains the preferred approach where it is sufficient.
Which tools are needed?
Authorised tools for extraction, orchestration and testing, depending on the context.
Can internal documents be used?
Only if they are anonymised and authorised; otherwise, the fictitious corpus is used for the exercises.
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