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Process automation

Scripts, scheduled jobs and UI automation for work that depends on manual repetition today, including between systems that offer no integration.

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What usually improves

  • Less rework and fewer manual steps
  • Standardized results, no matter who runs the process
  • Less reliance on informal knowledge
  • A record of what was run, ready for audit

How much each point improves depends on the environment: volume, structure and data maturity. That is why no percentage is promised here. The assessment measures the current situation and defines, before any work starts, how the gain will be measured.

Diagnosis

Signs this solves your problem

  • Someone spends hours every week copying data between systems.
  • An important check depends on someone looking at two screens.
  • Emails, attachments and files are organized by hand.
  • The process only works when one specific person is around.

Delivery

What gets done

  • Scripts and scheduled jobs

    Python, PowerShell and VBA to read, transform and generate files, running at the right time.

  • UI automation

    RPA and Selenium when the system offers no direct integration.

  • Automatic cross-system checks

    Screen reading with OCR, normalization and explicit rules to flag discrepancies while the work is happening.

  • Logs and evidence

    Every run recorded, to review what was done and repeat audits with the same criteria.

Most-used tools

  • Python
  • PowerShell
  • VBA
  • RPA
  • Selenium
  • OCR

Projects

Related projects

Described anonymously: context, approach and outcome, without exposing the client or the implementation.

Automatic cross-system checks with OCR

Context
Decisions and identifiers shown in different systems, with no integration between them, had to be compared quickly to catch discrepancies.
Approach
Screen content captured with OCR, identifiers normalized and compared against rules, and inconsistencies flagged while the work is happening.
Outcome
Faster verification and an objective layer of checking, with discrepancy types classified to cut false alarms.
  • OCR
  • Python
  • UI automation
  • Business rules

Audit and reconciliation of people databases

Context
Two operational databases differed in names and in which records were present, and there was no way to confirm that everyone was on both sides.
Approach
A full comparison of the lists, data normalization, exact and fuzzy name matching, and an audit file as the output.
Outcome
Real differences separated from spelling variations, less manual checking and a reusable basis for future audits.
  • Excel
  • Python
  • pandas
  • openpyxl
  • Matching rules

Automated radar for technical updates

Context
Following several technology areas by hand meant too many sources and the risk of missing relevant updates.
Approach
A pipeline for collection, deduplication, classification, embeddings and relevance scoring, with daily and weekly reports written by a local model.
Outcome
Scattered monitoring turned into a structured technical intelligence process, isolated and validated before running on its own.
  • Python
  • SQLite
  • RSS and APIs
  • Embeddings
  • Local model

Contact

Recognize any of these signs?

Describe how the problem shows up day to day. The first conversation is for understanding the situation and saying honestly whether and how help is possible.

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