Hire an LLM Engineer, Portland

Hire an LLM engineer in Portland.

This is for the sustainability reporting manager at Nike in Beaverton, Columbia Sportswear in Portland, or Adidas North America in Portland. Your team produces quarterly sustainability reports that aggregate data from hundreds of suppliers: emissions factors, material certifications, audit results. You write compliance disclosures for GRI and CDP frameworks. The current process: 2 to 3 weeks per quarterly report cycle, with 2 full-time staff during peak.

An LLM pipeline that reads supplier data, calculates the required metrics, and drafts GRI-formatted disclosure sections reduces this to a 3 to 4 day review cycle. Your team reviews and corrects a draft rather than writing from scratch. The challenge: GRI and CDP use defined terms that cannot be paraphrased. We build the exact-language enforcement into the generation step.

Pricing is scoped to your data sources, frameworks, and reporting cadence, we quote a fixed price after reviewing your current process.

How we work

Describe your current sustainability reporting cycle: which frameworks, which data sources, and your team size during peak.

Your specific reporting problem.

The data aggregation problem: your sustainability team requests emissions and materials data from 200 to 500 suppliers every quarter. The data arrives in different formats, at different times, with inconsistent column structures. Normalizing this data into a single aggregated dataset takes 5 to 7 business days of analyst time. Some of that work is unavoidable (data quality issues require human judgment), but most of it is format normalization that should be automated.

The disclosure drafting problem: once the data is aggregated, a GRI 302-1 (Energy consumed within the organization) disclosure has a specific format requirement. It must break down energy by type (fuel, purchased electricity, heating/cooling), report in joules or multiples thereof, and use the GRI-defined term "non-renewable sources" not a paraphrase. Writing this from the aggregated data takes 2 to 4 hours per disclosure section across 15 to 20 required sections.

An LLM pipeline built for GRI and CDP reporting handles both problems. The supplier data normalization runs continuously in the background, so by reporting week the data is already aggregated and clean. The disclosure drafting runs overnight: the pipeline reads the aggregated data, calculates the required metrics, and produces a GRI-formatted draft for each disclosure section.

The key requirement is that the pipeline uses GRI-defined terms exactly, it cannot substitute synonyms or paraphrase defined terms without changing the GRI meaning. We build term enforcement as a post-generation validation step, and the reviewer sees which terms were enforced and which sections need attention.

The pipeline components.

Supplier data normalization

Column-header matching for Excel/CSV variants, PDF extraction for questionnaire responses. Normalizes to a standard schema regardless of source format.

Metric calculation layer

Aggregates supplier data to calculate required GRI and CDP metrics: scope 1/2/3 emissions, materials by type, energy by source. Calculation logic is auditable.

GRI disclosure drafting

Section-level prompts for each required disclosure. GRI-defined terms enforced. Disclosure format matches standard requirements.

CDP response generation

CDP disclosure language with data provenance tracking. Self-reported vs. verified data distinguished in the output with appropriate qualifier language.

Term enforcement validation

Post-generation check that required GRI terms are present and no prohibited paraphrases appear. Reviewer sees validation results inline.

Review and edit interface

Side-by-side view of source data and generated disclosure. Reviewers can edit and regenerate specific sections with correction prompts.

How the build works for sustainability reporting.

The starting point is your most recent completed sustainability report and the underlying supplier data that fed it. We use the finished report as the calibration target: the pipeline needs to produce output that matches the format and language of your approved report, not a generic GRI document.

We work through the GRI framework version you report against (2021 is current for most large apparel companies) and map each required disclosure to the data fields in your supplier questionnaire. Fields that do not exist in the current questionnaire are flagged before build starts, they need to be added to the next questionnaire cycle.

Build time: 6 to 10 weeks. The first output for review is typically available at week 4 for the data normalization and metric calculation layers, with disclosure drafting completed by week 8.

Questions from Portland sustainability reporting teams.

How does the pipeline handle GRI-specific disclosure language requirements for Nike?: GRI standards require specific defined terms and disclosure structures that cannot be paraphrased. GRI 301-1 (Materials used by weight or volume) has a specific format requirement: the disclosure must separate renewable from non-renewable materials and report in tonnes or kilograms. The pipeline builds GRI-aware prompts that instruct the model to use the exact defined terms from the applicable GRI standard, produce the disclosure in the required format, and cite the underlying data source for each reported figure. A post-generation validation step checks that required terms are present and that the disclosure structure matches the standard's format.
Can the pipeline extract emissions data from supplier questionnaires in multiple formats?: Supplier data arrives in inconsistent formats: Excel spreadsheets with different column structures, PDF questionnaire responses, email attachments, and web form submissions. We build a supplier data normalization layer before the LLM step: Excel and CSV files are parsed with column-header-matching logic that handles format variation, PDFs are processed with Textract, and email attachments are handled by a pre-processing step that routes to the appropriate parser by file type. The LLM sees a normalized data structure, not the raw supplier format.
How do you handle the gap between supplier-reported data and third-party verified data for CDP disclosure?: CDP disclosures distinguish between supplier-reported data, estimated data, and third-party verified data. The pipeline tracks data provenance: each data point carries a tag indicating its source type. When generating a CDP disclosure, the model is instructed to use the appropriate qualifier language for each data type. Figures from third-party verified sources use different language than self-reported figures. The disclosure reviewer sees the provenance tags alongside the generated text so they can verify that the qualification language matches the actual data source.
What does the 3-4 day review cycle look like versus the current 2-3 week cycle for Columbia Sportswear?: The current cycle at Columbia Sportswear or Adidas North America looks like this: sustainability analysts spend 5 to 7 business days collecting and normalizing supplier data, then 3 to 5 days drafting the disclosure sections, then 3 to 4 days in legal and management review. With the LLM pipeline, the data collection and normalization step runs in the background continuously, so by the reporting deadline the data is already aggregated. The draft generation runs overnight. The 3 to 4 day review cycle covers reviewing and correcting the generated disclosures rather than writing them from scratch.

Ready to compress your quarterly reporting cycle?

Share your most recent sustainability report and describe your current data collection process. We will identify the automation opportunities and send a scope within two business days.

Hire an LLM engineer in Portland.

Pricing is scoped to your data sources, frameworks, and reporting cadence, we quote a fixed price after reviewing your current process.

Your specific reporting problem.

The pipeline components.

Supplier data normalization

Column-header matching for Excel/CSV variants, PDF extraction for questionnaire responses. Normalizes to a standard schema regardless of source format.

Metric calculation layer

Aggregates supplier data to calculate required GRI and CDP metrics: scope 1/2/3 emissions, materials by type, energy by source. Calculation logic is auditable.

GRI disclosure drafting

Section-level prompts for each required disclosure. GRI-defined terms enforced. Disclosure format matches standard requirements.

CDP response generation

CDP disclosure language with data provenance tracking. Self-reported vs. verified data distinguished in the output with appropriate qualifier language.

Term enforcement validation

Post-generation check that required GRI terms are present and no prohibited paraphrases appear. Reviewer sees validation results inline.

Review and edit interface

Side-by-side view of source data and generated disclosure. Reviewers can edit and regenerate specific sections with correction prompts.

How the build works for sustainability reporting.

Build time: 6 to 10 weeks. The first output for review is typically available at week 4 for the data normalization and metric calculation layers, with disclosure drafting completed by week 8.

Questions from Portland sustainability reporting teams.

How does the pipeline handle GRI-specific disclosure language requirements for Nike?

GRI standards require specific defined terms and disclosure structures that cannot be paraphrased. GRI 301-1 (Materials used by weight or volume) has a specific format requirement: the disclosure must separate renewable from non-renewable materials and report in tonnes or kilograms. The pipeline builds GRI-aware prompts that instruct the model to use the exact defined terms from the applicable GRI standard, produce the disclosure in the required format, and cite the underlying data source for each reported figure. A post-generation validation step checks that required terms are present and that the disclosure structure matches the standard's format.

Can the pipeline extract emissions data from supplier questionnaires in multiple formats?

Supplier data arrives in inconsistent formats: Excel spreadsheets with different column structures, PDF questionnaire responses, email attachments, and web form submissions. We build a supplier data normalization layer before the LLM step: Excel and CSV files are parsed with column-header-matching logic that handles format variation, PDFs are processed with Textract, and email attachments are handled by a pre-processing step that routes to the appropriate parser by file type. The LLM sees a normalized data structure, not the raw supplier format.

How do you handle the gap between supplier-reported data and third-party verified data for CDP disclosure?

CDP disclosures distinguish between supplier-reported data, estimated data, and third-party verified data. The pipeline tracks data provenance: each data point carries a tag indicating its source type. When generating a CDP disclosure, the model is instructed to use the appropriate qualifier language for each data type. Figures from third-party verified sources use different language than self-reported figures. The disclosure reviewer sees the provenance tags alongside the generated text so they can verify that the qualification language matches the actual data source.

What does the 3-4 day review cycle look like versus the current 2-3 week cycle for Columbia Sportswear?

The current cycle at Columbia Sportswear or Adidas North America looks like this: sustainability analysts spend 5 to 7 business days collecting and normalizing supplier data, then 3 to 5 days drafting the disclosure sections, then 3 to 4 days in legal and management review. With the LLM pipeline, the data collection and normalization step runs in the background continuously, so by the reporting deadline the data is already aggregated. The draft generation runs overnight. The 3 to 4 day review cycle covers reviewing and correcting the generated disclosures rather than writing them from scratch.

Ready to compress your quarterly reporting cycle?

Share your most recent sustainability report and describe your current data collection process. We will identify the automation opportunities and send a scope within two business days.