Replace the chain of notebooks, spreadsheets and manual file edits.

Keep the existing URL. Change the visible name.

A research workflow often begins as a sensible notebook. Six months later it contains absolute paths, copied cells, a hand-edited CSV, two versions of the exclusion list and one plot that works only when cells are run out of order.

We turn that workflow into the smallest reliable system the team can rerun and inspect.

• New instrument exports are renamed and combined by hand.
• The same QC plots are rebuilt for every batch.
• A changed column silently shifts the analysis.
• Results cannot be traced back to raw files and configuration.
• One person knows the correct notebook order.
• A collaborator needs a runnable tool rather than a repository tour.
• Publication or staff handover has exposed undocumented steps.

Input contracts

Expected file types, required columns, units, identifiers, timestamps and permitted missingness. Invalid inputs fail with a useful message.

Ingestion and validation

Parsing, schema checks, duplicate detection, metadata joins, checksums and conversion to an analysis format such as Parquet.

QC

Automated summaries, exception reports, visual checks and machine-readable reason codes.

Analysis

Functions or workflow steps with explicit parameters, tests and deterministic outputs where the method allows it.

Reporting

Versioned tables, figures, manifests and export packages generated from the same run.

Handover

Dependency lock, configuration examples, synthetic or approved sample data, run instructions and an ownership plan.

A bounded pipeline may need pandas or Polars, PyArrow, Pydantic, pytest and a command-line interface. A dependency graph with dozens of files may justify Snakemake. A service with concurrent users may need an API and database. Airflow is a poor answer to a weekly folder of twelve CSV files.

The build should remain proportionate to the team that will own it.

project/
├── README.md
├── pyproject.toml
├── config/
│   ├── default.yaml
│   └── study_a.yaml
├── data_examples/
│   └── synthetic_session.csv
├── src/
│   └── project_name/
│       ├── ingest.py
│       ├── schemas.py
│       ├── quality.py
│       ├── analysis.py
│       └── report.py
├── tests/
│   ├── test_ingest.py
│   ├── test_quality.py
│   └── test_analysis.py
└── outputs/
    └── run_manifest.json

The output directory is generated. The raw input is never overwritten.

• Scientific interpretation
• Changes to exclusion logic
• Unexpected new file types
• Borderline image or signal quality
• Material changes to the protocol
• Claims made from the output

Automation should expose those decisions. It should not bury them in a default.

• Working pipeline or tool
• Source code and tests
• Data schema and configuration
• Automated QC report
• Run manifest and provenance
• Reproducible figures or tables
• Error handling and logs
• Deployment or execution instructions
• Technical handover

Will you turn a notebook into production software?

We first identify who will run it, how often, on what infrastructure and what failure costs matter. Some workflows need a tested command-line package. Others need a service. “Production” without an operating context is a decoration.

Can you work inside our repository?

Yes, subject to access, security and IP terms. We can also deliver a separate package for client review and integration.

Do you provide maintenance?

Reserved support can cover dependency updates, new input variants, bug fixes and agreed extensions. Maintenance is scoped against a named backlog.

Can an LLM automate the document steps?

Sometimes. Extraction quality, source traceability, review queues and failure handling are designed first. An LLM is one component, not the workflow description.

Show us the current run from raw file to final figure.

A screen recording, repository tree and two representative inputs are often enough to identify the manual joins and hidden state.

Scope a pipeline build

• Convert a notebook into a reproducible pipeline

How Services engagements are scoped