Built for clean-energy diligence

Stop using general-purpose AI for investment-critical decisions.

General-purpose AI can help draft, search, and summarize. But complex clean-energy diligence requires more: a system that can organize large data rooms, apply a consistent framework, identify gaps, and trace findings back to evidence.

See what CEARTscore does differently Ask your current AI

A structured path to the decision

Organize the evidenceDocuments · filings · project materials
Apply the frameworkCriteria · benchmarks · weights
Inspect the findingsSource links · gaps · follow-up
Build the deal recordStructured outputs · shared knowledge

Built for the full data room

Analyze and organize large, complex deal materials in one diligence environment.

Evidence you can inspect

Connect findings to the underlying source material and page.

A framework you control

Set criteria, weights, thresholds, and minimum scores for your team.

Intelligence that compounds

Keep analysis connected to a reusable, searchable deal record.

General-purpose AI vs. CEARTscore

General-purpose AI

Useful for individual tasks

Summarizes documents and responds to prompts.
Output can depend heavily on prompt design and session context.
Often requires manual synthesis, formatting, and handoffs.
May require separate processes to preserve context across workstreams.

Helpful tool. Not a full diligence system.

CEARTscore

Built for a repeatable diligence process

Organizes large data rooms and applies a structured evaluation framework.
Structured templates and scoring support more consistent comparison.
Produces structured outputs designed for review and sharing.
Connects findings to an ongoing, queryable deal record.

Diligence intelligence built around the deal.

Compare the capabilities that matter in diligence

The issue is not whether general-purpose AI can assist. The question is how much manual process, prompting, review, and separate tooling your team must add before its output is dependable enough for the decision at hand.

What matters in diligence	General-purpose AI	CEARTscore
Analyze a large data room	May require documents to be divided into batches, with repeated prompting and manual synthesis.	Built to process and reason across large, multi-document data rooms.
Clean-energy diligence knowledge	General knowledge; the user supplies and maintains the relevant framework.	Industry-specific frameworks for clean-energy projects, filings, and risk patterns.
Source support	Responses may require manual validation and source reconciliation.	Findings link to the relevant document and page.
Identify missing information	Primarily analyzes what is provided.	Flags apparent gaps, missing materials, and follow-up questions.
Compare projects consistently	Free-form outputs can be difficult to compare across deals.	Uses structured, benchmarked evaluation for project and portfolio comparison.
Create an investment score	Requires manual criteria and prompt design.	Configurable weighted scoring with client-defined thresholds.
Reliability of outputs	Results can vary with prompt wording, session history, and model behavior.	Structured templates and ongoing evaluation support consistent outputs.
Protect deal context	Requires careful configuration and use of the selected provider.	Partitioned data-room boundaries designed for confidential diligence workflows.
Preserve institutional knowledge	Analysis may remain in disconnected chats or files.	Connects outputs to a growing, queryable deal record.
Share work across a team	Often requires separate accounts, exports, and manual coordination.	Supports shared workspaces for internal teams and outside collaborators.
Deliver decision-ready reporting	Text may need manual formatting and reconciliation.	Produces structured reports and alternative summary formats for stakeholders.

Analyze a large data room

General-purpose AIMay require documents to be divided into batches, with repeated prompting and manual synthesis.

CEARTscoreBuilt to process and reason across large, multi-document data rooms.

Clean-energy diligence knowledge

General-purpose AIGeneral knowledge; the user supplies and maintains the relevant framework.

CEARTscoreIndustry-specific frameworks for clean-energy projects, filings, and risk patterns.

Source support

General-purpose AIResponses may require manual validation and source reconciliation.

CEARTscoreFindings link to the relevant document and page.

Identify missing information

General-purpose AIPrimarily analyzes what is provided.

CEARTscoreFlags apparent gaps, missing materials, and follow-up questions.

Compare projects consistently

General-purpose AIFree-form outputs can be difficult to compare across deals.

CEARTscoreUses structured, benchmarked evaluation for project and portfolio comparison.

Create an investment score

General-purpose AIRequires manual criteria and prompt design.

CEARTscoreConfigurable weighted scoring with client-defined thresholds.

Reliability of outputs

General-purpose AIResults can vary with prompt wording, session history, and model behavior.

CEARTscoreStructured templates and ongoing evaluation support consistent outputs.

Protect deal context

General-purpose AIRequires careful configuration and use of the selected provider.

CEARTscorePartitioned data-room boundaries designed for confidential diligence workflows.

Preserve institutional knowledge

General-purpose AIAnalysis may remain in disconnected chats or files.

CEARTscoreConnects outputs to a growing, queryable deal record.

Share work across a team

General-purpose AIOften requires separate accounts, exports, and manual coordination.

CEARTscoreSupports shared workspaces for internal teams and outside collaborators.

Deliver decision-ready reporting

General-purpose AIText may need manual formatting and reconciliation.

CEARTscoreProduces structured reports and alternative summary formats for stakeholders.

Explore CEARTscore capabilities

From information to investment judgment

Compare the same five parts of the diligence process. Each approach can support the team; the difference is how the evidence, evaluation framework, and shared deal record are organized.

Diligence activity	Traditional diligence	Other AI tools	CEARTscore
Organize the data room	Teams organize documents and coordinate separate workstreams manually.	Retrieval and document conversation help users explore uploaded materials; organization may require separate tools.	Organizes large, multi-document data rooms within a structured diligence environment.
Build the initial analysis	Analysts read the materials and assemble the initial synthesis.	Generates initial summaries; teams may need to reconcile outputs across prompts and documents.	Applies structured evaluation templates, linking findings to evidence and flagging apparent gaps.
Compare projects	Teams manually reconcile findings and assumptions across deals.	Free-form results may need additional formatting and reconciliation before comparison.	Uses structured, benchmarked evaluation to support project and portfolio comparison.
Keep the team aligned	Findings are distributed across files, email, and team workstreams.	Outputs may remain in separate chats or exports unless a shared process is added.	Connects shared workspaces and outputs to a persistent, queryable deal record.
Apply the decision framework	Analysts define and apply criteria, weights, and scoring through manual processes.	Teams supply the framework through prompts and may need separate scoring tools.	Supports configurable criteria, weights, thresholds, and structured scoring.

Organize the data room

Traditional diligenceTeams organize documents and coordinate separate workstreams manually.

Other AI toolsRetrieval and document conversation help users explore uploaded materials; organization may require separate tools.

CEARTscoreOrganizes large, multi-document data rooms within a structured diligence environment.

Build the initial analysis

Traditional diligenceAnalysts read the materials and assemble the initial synthesis.

Other AI toolsGenerates initial summaries; teams may need to reconcile outputs across prompts and documents.

CEARTscoreApplies structured evaluation templates, linking findings to evidence and flagging apparent gaps.

Compare projects

Traditional diligenceTeams manually reconcile findings and assumptions across deals.

Other AI toolsFree-form results may need additional formatting and reconciliation before comparison.

CEARTscoreUses structured, benchmarked evaluation to support project and portfolio comparison.

Keep the team aligned

Traditional diligenceFindings are distributed across files, email, and team workstreams.

Other AI toolsOutputs may remain in separate chats or exports unless a shared process is added.

CEARTscoreConnects shared workspaces and outputs to a persistent, queryable deal record.

Apply the decision framework

Traditional diligenceAnalysts define and apply criteria, weights, and scoring through manual processes.

Other AI toolsTeams supply the framework through prompts and may need separate scoring tools.

CEARTscoreSupports configurable criteria, weights, thresholds, and structured scoring.

Put your current tool to the test

Ask your own AI whether it is enough for this job.

Paste the assessment prompt into the AI tool your team currently uses. It asks the tool to explain, plainly and technically, whether it can support complex clean-energy diligence without your team manually compensating for gaps in scale, security, collaboration, sourcing, consistency, and domain knowledge.

Takes less than a minute. Paste it into your preferred AI tool and ask for a direct answer.

What the prompt evaluates

Data-room scale and context
Missing-information detection
Deal separation and confidentiality
Shared memory and collaboration
Output consistency and source traceability
Clean-energy domain knowledge and framework control

Read or manually copy the full prompt

I am evaluating whether this AI tool is structurally capable of supporting investment diligence for complex clean-energy transactions, including large data rooms, permit filings, FERC records, and project-specific risk analysis—or whether a purpose-built system is needed.

Please answer as literally and technically as possible about your own capabilities and limitations. Do not give general reassurance or describe what may be possible only with unusually careful prompting. Distinguish between:

• What you can do reliably as part of your normal architecture
• What is possible only if I manually compensate through prompting, uploads, exports, or separate tools
• What you cannot reliably do

For each item below, provide:
1. A verdict: Yes, No, or Only if I compensate manually
2. Why, referring where relevant to context limits, memory, session boundaries, security controls, document handling, and training
3. What I would need to do manually if the answer is not a clear Yes

Evaluate these capabilities:

1. Reason across an entire data room that may contain thousands of documents without losing earlier context or requiring repeated re-uploading and re-prompting.
2. Identify information that appears to be missing from the data room, rather than only summarize what is present.
3. Keep information from separate deals structurally isolated.
4. Support confidential diligence data with controls appropriate to the use case.
5. Preserve outputs as part of a searchable, growing deal record rather than leaving them in isolated chat threads.
6. Support a shared workspace for internal deal teams and external collaborators.
7. Provide predictable cost for a large diligence assignment.
8. Generate stable, repeatable results when given the same evidence and a similar request.
9. Resist misleading or adversarial language embedded in source documents.
10. Create structured, scored outputs that can be compared meaningfully across deals.
11. Cite each material finding to a specific source document and page.
12. Apply maintained clean-energy knowledge, including relevant filings, diligence frameworks, and risk patterns.
13. Allow evaluation criteria, weights, and thresholds to be adjusted without extensive prompt-engineering trial and error.
14. Maintain and calibrate its evaluation approach as market practice changes.

When finished, provide:
• A one-line verdict for each capability
• An overall assessment of whether this tool is a viable stand-alone system for complex clean-energy diligence
• A concise list of the operational gaps my team would need to fill manually