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How the ORIS AI BoQ Engine Works?

From Bill of Quantities file upload to carbon assessment in ORIS

The ORIS AI BoQ Engine is an entry point into ORIS that removes the manual effort of recreating an existing Bill of Quantities. Instead of copying data from your files into ORIS by hand, the AI reads your existing BoQ file, extracts the relevant materials, quantities and units, and maps them to the selected carbon database so you can start measuring carbon impact immediately.

What the AI BoQ Engine Does?

Step-by-Step: From File to Carbon Assessment

Validating AI Suggestions


The AI does not create a new BoQ. It reads the data you already have and brings it into ORIS in a structured, usable form.

What the AI BoQ Engine Does?

When you upload a BoQ file into the ORIS AI BoQ Engine, the AI:

  • Reads the file structure and identifies material descriptions, quantities and units across all sheets and sections.
  • Groups elements into a hierarchy based on the structure it finds within the content of the file, such as header rows, section titles and row organisation.
  • The number of groups and hierarchy levels is not fixed, the AI builds as many as your file structure contains.
  • Proposes an extraction of the relevant line items as a pre-filled BoQ, ready for your review.
  • Maps the extracted material descriptions to entries in your selected carbon database using AI-based text comparison.
  • Provides a confidence score for each proposed material match, so you can decide whether to accept, adjust or override the suggestion.

Important

The AI BoQ Engine does not organise, clean or correct your data. The quality of the extraction depends entirely on the quality and consistency of the source file. Well-structured files with clear naming produce more accurate results and require less manual correction.


Step-by-Step: From File to Carbon Assessment

1

Upload your BoQ file

Select your Excel, PDF or GAEB file and upload it to the ORIS AI BoQ Engine. The AI will begin scanning the file to identify its structure, materials, quantities and units.

2

Review the extraction

Once processing is complete, the AI presents the extracted line items grouped by the hierarchy it detected in your file. Review the proposed structure, check that materials and quantities have been read correctly, and remove any irrelevant rows such as blank lines or non-material items.

3

Select your grouping and carbon database

Choose which sections or sub-packages to include in your assessment. Then select the target carbon database for your project region, for example the ORIS Global Database or a country-specific database.

4

Run AI Mapping

The AI matches your extracted material descriptions to entries in the selected carbon database. You can map all items at once using bulk mapping, or review and map line by line. Each match comes with a confidence score. Override any suggestion that does not fit your project context.

5

Initialise the project in ORIS

Once you are satisfied with the mapping, use the extracted and mapped data to create a new project directly in ORIS. The BoQ, quantities and mapped materials are carried over automatically, so you can proceed straight to calculating the carbon footprint.

Important

The AI BoQ Engine does not organise, clean or correct your data. The quality of the extraction depends entirely on the quality and consistency of the source file. Well-structured files with clear naming produce more accurate results and require less manual correction.

Validating AI Suggestions

The AI BoQ Engine is designed to accelerate your workflow, not to replace technical judgement. Before initialising your project, always validate the following.

MATERIAL MATCH ACCURACY

  • Confirm that the correct material has been selected for each line item. Where several materials could match, the AI selects one by default. Always verify it is the appropriate choice for your project context. For example, the AI may select a warm mix asphalt where your project uses a hot mix; these carry different carbon values and the distinction matters.
  • Check that units have been read and converted correctly.
  • Confirm that construction activities (A5) have not been included in material calculations unintentionally.

DATABASE COMPLETENESS

The AI can only match to materials that exist in the selected carbon database. If a material is not covered by that database, no confident match is possible regardless of how well the file is structured. This is a database coverage limitation, not a file quality issue. In these cases, always check whether a different material entry in the database is a reasonable proxy before falling back to a custom entry.

CUSTOM MATERIALS

When no suitable match is found in the selected database, the AI may assign a Custom material placeholder. This means no relevant carbon footprint value is applied automatically. In this case:

  • Source the carbon footprint value yourself, from an EPD, a published dataset, or another reliable reference.
  • Enter the value manually in the material definition.
  • Document where the value came from, for example in the description field of the material, so the source is transparent and traceable for anyone reviewing the assessment.

Tip

For materials where the AI confidence is lower, accept the closest available match and adjust the material or emission factor manually once the project has been initialised in ORIS.

Tip: When a material specification is missing

In early design phases, material specifications are not always defined. When this is the case, apply conservative assumptions by selecting a material likely to overestimate rather than underestimate the carbon impact. This avoids underreporting emissions and can always be updated as the design develops. Consider building a set of typical material assumptions for common structural elements used in your projects, so your team has a consistent starting point when specifications are incomplete.