Our Methodology

It's not the model — it's what it's fed and how it's tuned

Climate Finance Intelligence utilizes a purpose-built retrieval-augmented generation (RAG) system to accelerate concept note development. Our methodology ensures accuracy through two key pillars:

Targeted Retrieval: Rather than relying on generic AI models, every response is grounded in a curated corpus of approved funding proposals and peer-reviewed climate science.

Accelerated Drafting: This approach compresses days of manual document review into minutes.

For institutional users, this methodology guarantees faster, highly defensible proposals built on the exact evidentiary standards required by funders.

What They're Saying

"This is a fantastic tool for building confidence in our submissions. It not only validates the relevant information we've already included but also provides a clear roadmap of areas needing more attention."

Caribbean Accredited Entity

"The detailed evaluation of the Theory of Change was particularly informative and helped us significantly improve our proposal's core logic."

Southern Africa Accredited Entity

"What sets this software apart is its deep qualitative feedback. It goes far beyond a simple checklist, providing a specific assessment with concrete recommendations for improvement."

Pacific Accredited Entity

"Many DAEs have a very strong technical and structuring background — but writing in a way that convinces the Donor in English is a real challenge. The linguistic opportunity here is significant, and this tool could address that."

Multilateral Donor Reviewer

"My first impression is that it's really spot on. I compared its analysis against my own review of the concept note from over a year ago and they were quite aligned. That level of consistency with expert judgment is what sets it apart."

Multilateral Donor Reviewer

"There is clearly real potential here. We're trying to improve both efficiency and access — and those are almost two separate directions. There are DAEs that need considerable handholding, and if something can help them translate ideas into English, and into Donor language and finance logic, those different layers of translation are truly useful."

Multilateral Donor Reviewer

Curated precedent corpus

Thousands of approved project files a public chatbot can't see — the actual arguments donor reviewers have already accepted, not generic training data.

A tuned evaluation methodology

Fit-for-purpose models and prompts calibrated to donor criteria, refined over two years of live use against real submissions — not a one-time prompt experiment.

Every finding cited, nothing black-boxed

Output traces to the exact source document and passage — more auditable than a raw chatbot answer, more consistent than one reviewer's undocumented judgment call.

Two years, not twenty. Our edge isn't one senior partner's tenure on your account — it's a system refined across two years of real submissions across many institutions, so what it knows is the aggregate pattern of what gets approved, not one person's memory.

The stakes are high: General-purpose AI isn't built for this

Generic AI infers answers from the open web — hallucinations, no traceability, and an agreeable streak that hands you whatever case your prompt implied. CFI is anchored to documents you own or are authorized to use. Every finding is tied to verifiable source text, with an emphasis on critical evaluation over affirmation.

Built On Your Own Files

CFI is populated with the materials you own or are authorized to use: your past submissions, technical notes, and the external sources you select. It is a strictly private — your dataset is never shared and never used to train AI models.

Every Insight Is Cited

Each response traces to specific source text. Built-in references let you verify exactly where a finding comes from — not inferred, not paraphrased, not invented.

Critical In Evaluation

The platform can only draw on what's in your dataset, so it cannot manufacture support that isn't there. The assistants are configured to evaluate critically rather than just agree.

It's not the model — it's what it's fed and how it's tuned

What They're Saying

The stakes are high: General-purpose AI isn't built for this

See the methodology in action