The field produces more frameworks every year
Frameworks, guidance documents, methodology updates, peer reports. Keeping up is a job in itself, and you are never quite sure whether the version you built on last quarter still holds.
Act
Magnify your impact with sustainability intelligence
Bring the field's knowledge together with your own data and goals, so a small team can move with the confidence of a big one.
“We keep producing more and more guidance, but our problem isn't a lack of information; it's the human capacity to navigate it.”
Nadine McCormick
WBCSD
Generic AI leaves the verifying to you
A general-purpose assistant answers in seconds, and then the checking starts: can I trust this source, is it current, does it apply to my sector? Verifying information from AI is time-consuming work.
Guidance in one tool, your data in another
A framework rule only becomes a decision once you check it against your own materials, suppliers, and jurisdictions. That context sits in your sustainability data, the guidance sits in a copilot or a PDF, and today you are the connection between the two.
“With other AI tools I was really slow because I kept doubting the sources. With Spirefly it was much faster, because we don't have to be a little paranoid about whether the information is reliable. It saves a lot of mental energy.”
What it is
Three kinds of knowledge, in one system
Sustainability Intelligence is what emerges when three kinds of knowledge work together. Each exists somewhere today, in a spreadsheet, a PDF, or someone's inbox. What is missing is the connection.
Your company
Your targets, your material topics, and the initiatives you already have in motion.
Sustainability expertise
Frameworks and their methodology, plus 1,000+ peer sustainability reports.
Your data
Your products, your suppliers, and your actual purchase volumes.
The Spirefly WorkbenchAnswering a real question needs all three at once, and that connection is what makes the work fast.
Spirefly understands your company
Your context, carried into every conversation
Upload your sustainability report, describe your goals and industry. The system builds a company profile it carries into every conversation: your targets, your material topics, your current initiatives.
It answers in your context, and gets proactive over time, surfacing what changed since last year and flagging a finding that connects to a goal you set months ago.
You
What are the most effective ways for us to cut emissions in our supply chain?
For a food business like yours, three levers move the most:
- Engage your top dairy and beef suppliers on feed and manure practices
- Switch two high-emission ingredients to lower-carbon sourcing
- Move refrigerated transport onto your renewable-electricity partners
Together they cover about two-thirds of the gap to your −30% Scope 3 by 2030 target.
Sources
- YOUR TARGET−30% Scope 3 by 2030
- SBTi FLAGForest, Land and Agriculture sector guidance
- YOUR DATASupplier emissions, FY2024
Spirefly understands the field
What the field has learned, at hand
Frameworks and their methodology (SBTN, TNFD, GHG Protocol, CSRD, EUDR), the implementation guides behind them, and 1,000+ peer sustainability reports across food, retail, fashion, and manufacturing.
Reference a threshold and it cites the source. Design a nature policy and see how leading companies structured theirs. Every draft starts from what the field has already learned.
You
How should we approach double materiality for our first CSRD report?
CSRD requires a double-materiality assessment under ESRS 1: you report a topic if it is material to your impacts or to your financials. Most companies in your sector pair a value-chain impact screen with a financial-risk review, then set a scoring threshold per topic. Danone and Unilever both describe this two-lens method in their first ESRS statements.
Sources
- ESRS 1Double materiality: general requirements
- DANONE 2024First ESRS sustainability statement
- UNILEVERESRS double-materiality approach
Spirefly understands your data
Your footprint, down to the supplier
The intelligence layer works on your products, your materials, your suppliers, and your actual purchase volumes. That is sensitive commercial data, so it stays in a private environment that is yours alone and is never used to train the model.
You work with it in plain language. Ask a question and get the driver, the breakdown, and a chart of your own data back, with no formulas to write.
You
What are Verde Farms' emissions, and where do they come from?
Verde Farms accounts for 12,480 tCO₂e, about 6% of your scope 3. It concentrates on-farm: enteric fermentation and feed production drive most of it, with manure management, processing and transport making up the rest.
- Enteric fermentation52%
- Feed production26%
- Manure management12%
- Processing & transport10%
Sources
- YOUR DATAVerde Farms · FY2024 supplier records
- GHG PROTOCOLScope 3 Category 1: purchased goods
“I presented my strategy to management with confidence. My strategy is now rooted in trusted sources and it's specific, actionable, and defensible.”
Use AI safely
Built to speed up your thinking
Review before you commit
When an output goes into a board deck, a filing, or a public commitment, human review is expected. You check specific claims against traceable sources.
Know where the boundary is
The knowledge base covers what frameworks require and how to apply them. For a specific legal question about a contract or jurisdiction you need a lawyer, and the system tells you when you are there.
Use gaps as a guide
If your footprint data is incomplete, the system surfaces where the gaps are and which ones matter most, so you know where better data would sharpen the picture.
Follow uncertainty signals
When a finding rests on a model estimate, the system says so. Explicit uncertainty markers tell you where to push for confirmation before going public.
Across the journey
Active from your first measurement to your final disclosure
Water use · 2025m³
41,200
what does this mean for us?
→ 82% sits in high-risk basins
→ commodities grown in vulnerable ecosystems
→ sourcing cost and supply continuity risk
water footprintinterpreted
Understanding what a finding means to you
Ask what 82% of water use in high-risk basins means for a retailer like you, and get the answer in the same conversation.
Your data
+Framework knowledge
Strategy drafttarget
−30% Scope 3 by 2030
grounded in
- ✓SBTi Net-Zero Standard
- ✓1,000+ peer reports
draftdefensible ✓
Drafting a strategy, grounded in data and peer practice
It grounds your draft targets in framework requirements and peer practice, so they hold up to management.
Your data
+Peer reports
+Framework knowledge
VSME · B3disclosure
Energy and GHG emissions
216,635 tCO₂e, of which31% renewable.
sources
- ▤avero_2025.csvdatabase
- ▤climate-policy.pdfupload
draftframework-checked ✓
Preparing for disclosure
It checks your language against framework requirements and flags where a claim outruns the data.
Your commitments
+Framework knowledge
+Your data
Ready to see the intelligence layer?
Book a demo and we'll show you how frameworks, methodology, and 1,000+ peer reports work in the Workbench.
Frequently asked questions
What is sustainability intelligence?
Sustainability intelligence is software that answers what your sustainability data means and what to do about it. Spirefly's intelligence layer combines knowledge of your company, knowledge of the field (frameworks, methodology, and peer reports), and knowledge of your own footprint, so a small team can produce analysis it can cite and defend.
How is this different from just asking ChatGPT about my sustainability data?
Three differences matter in practice. The knowledge base is curated for sustainability, so answers are consistent and citable. The system works on your actual data. And everything it produces is traceable: you can see what data it drew on and what methodology it applied. When someone challenges a number in a board meeting, you have an answer.
What counts as teaching the system about my company, and how much work is that?
A single session. You upload your most recent sustainability report, or describe your goals verbally, confirm the key targets and material topics, and the system has what it needs. Later sessions build on the same profile, so you do not re-brief it. As your data accumulates across measurement cycles, the context gets richer and the proactive signals more specific.
We don't have a complete footprint yet. Can we still use Sustainability Intelligence?
Yes. The intelligence layer works with whatever data you have. Partial data produces analysis with flagged gaps, which is useful in itself: you see where the unknowns are and which gaps matter most. Many teams use it to decide where to prioritise data collection before their next measurement run.
What happens to our data, and is it used to train the model?
No. Your footprint figures, supplier information, and uploaded documents stay yours and are not used to train or improve the underlying model. You work in a private environment, and what the system learns about your company stays within your account.
Can the system help us compare ourselves to peers in our sector?
For qualitative benchmarking, yes, today. The knowledge base includes 1,000+ peer sustainability reports, so you can see how others structured their nature policies, what language they use in disclosures, and which commitments have become standard. Quantitative benchmarking against a sector average is still in development.
Can we export what the system produces?
Yes. Analyses, dashboards, and documents produced in the Workbench can be exported and shared.
Still have a question?
Send it to our team and we'll reply by email.