Transcription & AI reporting
A local transcription and summarization workflow designed to reduce documentation workload and produce meeting reports teams can use.
Read the case study : Transcription & AI reporting03
Document automation
Document automation: from incoming document to verified deliverable.
Turn transcription, extraction or summarization tasks into a coherent workflow, with checks and a review step suited to your documents.
This work fits your situation if…
From a recording to written minutes
A recording has to become a structured report: topics discussed, decisions and actions. The workflow must let users correct the transcript and review the document produced.
Extracting recurring information
Case files, letters or reports contain fields to be re-entered into a tool. You need to define the expected formats, the mandatory information and how missing items are handled.
Preparing a sourced summary
Several documents need to feed a memo or a draft. Users must be able to trace where each piece of information comes from and tell established points from those still to be checked.
What the engagement can deliver.
The scope and deliverables are agreed together during scoping.
- A defined document workflow
- Accepted inputs, processing steps, output formats, review points and final destination. Sensitive data and retention rules are examined from the scoping stage.
- A processing pipeline fitted to the task
- File reading, transcription or character recognition (OCR) where needed, extraction and output. Deterministic rules and models are chosen according to the task.
- Checks people can use
- Missing fields, inconsistencies and items to review are flagged. Links back to the source documents and behavior on failure are built into the workflow.
- Integration and acceptance testing with your teams
- Export or connection to your tool, a review interface and a set of representative cases. Review time and output quality are among the criteria to measure.
A clear sequence.
Observe a real case file
Describe the current manual steps and gather a few anonymized examples: a typical case, an incomplete case and a difficult one. Define the expected result with the people who review it.
Test the workflow
Build a first pipeline on that scope. Compare outputs with the expected results, examine errors and adjust the checks and the review interface.
Connect it to daily work
Integrate inputs and outputs, document exceptions and organize onboarding. Assess the benefit across the full workflow, review included.
Case studies that show the work.
Plaidoria — sourced legal drafting
A product for preparing legal briefs that checks its references against official sources. The lawyer reviews and approves the draft; this case illustrates automation paired with domain-specific checks.
Read the case study : Plaidoria — sourced legal draftingBefore we start.
Which documents can be processed?
The scope depends on your formats and the result you need: text documents, scanned files or audio recordings, for example. An anonymized sample lets us check their legibility and the processing required before committing to any coverage.
Can the tool integrate with our current application?
That depends on its data exchange interfaces and the access available. Scoping examines the existing APIs, imports and exports to choose an integration proportionate to the need.
How do we know the automation is useful?
We define a set of case files and a baseline: expected quality, processing time and correction time. The comparison is made on the approved deliverable, including failure cases and review work, not just on generation speed.
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Next step
Let’s start from your need.
Describe the type of documents, the repetitive operations and the deliverable you expect. For a first contact, a described example is enough; you can keep confidential documents within your own environment.