Due Diligence AI Analyst - Cyber Sierra
Due Diligence AI Analyst
150 questions. 15 minutes. Answered in the format they sent.
Our Due Diligence AI Analyst reads any Excel questionnaire format, generates policy-cited draft responses using your own policies and past answers, and returns the completed file in the original layout.
Cited in: IMDA Model AI Governance Framework for Agentic AI
Cited in: World Bank WDR 2026 on AI for Development
2 weeks → 15 min
first draft time, live customer demo
Any Excel format
multi-tab, weighted, non-standard. No template required
80 per year
questionnaires cleared by a 7-person GRC team
The Problem
The First Draft Is Where the Time Goes
01
Every customer sends a different Excel format
Multi-tab, weighted scoring, conditional fields. Each questionnaire needs manual interpretation before anyone can start answering it.
02
80 questionnaires a year. 7 people. BAU does not stop.
A 7-person GRC team managing 80 annual questionnaires spends most of its capacity on first drafts, not higher-value compliance work.
03
Sales is waiting. GRC is still on the first draft.
When deals require a completed questionnaire before the next meeting, a 2-week drafting cycle is not a compliance problem. It is a revenue problem.
04
The same question. Different answers from different analysts.
The same question appears in questionnaires from different customers. When different analysts draft it independently, answers drift. That inconsistency surfaces in audits.
How Our Due Diligence AI Analyst Works
Upload the questionnaire as received. Connect your policy library. Get a completed draft returned in the original Excel format with every answer cited to source.
01
Upload the questionnaire as received
Drop in the Excel file exactly as the customer sent it. Our AI Analyst automatically parses the structure, identifying question columns and answer columns across any tab layout with no template mapping required.
02
Connect your policies and past responses
Point it at your policy library and historical assessment answers. You choose which policy version and past response set to reference so no outdated data gets cited without your approval.
03
Generate a draft response for every question
Every question is answered by referencing your specific policies, controls, and past responses simultaneously. Each answer includes the source citation: which policy, which section, which version.
04
Review answers before sending
The draft is returned as a structured view for human review. You approve, adjust, or override before anything is submitted. Our AI Analyst handles the first draft. Your team owns the final response.
05
Export in the original format with links preserved
The completed questionnaire is returned in the original Excel layout: tabs intact, links preserved, structure unchanged. You submit exactly what they asked for with no reformatting on your end.
app.cybersierra.co / dd-analyst Questionnaire structure analysis
investor-questionnaire.xlsx
3 tabs · non-standard layout
Parsing
Tab 1 · Security
42 questions detected
Tab 2 · Privacy
38 questions detected
Tab 3 · Weighted Scoring
70 questions detected
Built for What Manual Drafting Cannot Scale
Six capabilities that make it possible to answer due diligence questionnaires at volume without sacrificing accuracy, consistency, or format fidelity.
Format Flexibility
Any Excel Format. No Template Required.
It reads any Excel structure automatically: multi-tab, weighted scoring, non-standard columns, embedded links, with no column mapping needed. Our AI Analyst identifies question and answer fields regardless of how the sender structured the file.
Policy-Grounded Responses
Every Answer Cites Its Source
Responses reference your own policies and past answers, not generic compliance text. Each answer cites the specific policy document, section, and version behind it, making every response defensible and auditable on request.
Answer Consistency
Same Question. Same Answer. Every Time.
When the same question recurs across questionnaires, our AI Analyst draws on your response history to produce a consistent answer. Drift caused by different analysts drafting independently is eliminated at generation, not caught during review.
Pipeline Protection
Questionnaires That Block Deals Get Cleared First
A backlog of 80 questionnaires with a 7-person team means some deals wait weeks for a draft response. Our AI Analyst prioritises high-priority questionnaires and returns a complete draft in minutes, so sales is not waiting on GRC.
Self-Verifying Accuracy
Every Answer Checked Twice Before You See It
Every draft answer passes through two validation nodes before you see it: an LLM judge checking for unsupported claims and metrics-based verification measuring faithfulness and relevance. Answers failing both nodes are rewritten automatically.
Context Graph
It Knows Which Documents Are Current
A structured graph maps your policies, certifications, and assessments with version and expiry metadata. Our AI Analyst references only current, relevant documents through deterministic logic rather than guessing what is recent from training data.
Where Other Approaches Break Down
Every GRC team receiving 50+ questionnaires a year has tried at least one of these. Here is specifically what fails, and what our Due Diligence AI Analyst does instead.
| Approach | What Breaks | What the AI Analyst Does |
|---|---|---|
| Manual GRC Team Drafting | A single questionnaire takes 2 weeks of analyst time to interpret, locate the right policies, draft answers, and check consistency with past responses. At 80 per year, this is not sustainable. | Our Due Diligence AI Analyst returns a policy-cited draft for any questionnaire in minutes, in the original Excel format. |
| Generic AI Writing Tools | Generic AI produces generic answers. Without access to your actual policies, controls, and response history, it cannot produce org-specific, defensible responses or return the completed file in the original format. | It draws on your own policies and past assessment history to generate cited, org-specific answers returned in the sender's exact Excel layout. |
| Template-Based Systems | Pre-built response templates only work when the incoming questionnaire matches the template structure. Non-standard formats, multi-tab layouts, and custom column arrangements break them immediately. | It reads any Excel structure automatically with no template mapping, no preprocessing, and no manual interpretation before drafting begins. |
| Outsourced Questionnaire Services | External services introduce a handoff delay, a confidentiality risk, and a per-questionnaire cost model that scales directly with volume. At 80 questionnaires a year, the economics do not hold. | It runs inside your own environment. Your data never leaves, and the cost does not scale with questionnaire volume. |
Results From Live Deployments
Observed outcomes from production deployments. Government-validated accuracy controls.
2 weeks → 15 min first draft time per questionnaire
A global financial institution received a complex investor questionnaire in a proprietary multi-tab Excel format that previously required a 2-week manual first draft. Our AI Analyst completed all 150 questions in 15 minutes and returned the answers in the original file layout.
100+ hours → 15 min validated by Singapore's IMDA in its official AI governance framework
Singapore's Model AI Governance Framework for Agentic AI (v1.5, May 2026) features Cyber Sierra's due diligence technology as a case study. The framework documents a deployment at a financial institution where a task that previously required more than 100 manual hours was completed in 15 minutes.
Cited in WDR 2026 consulted by the World Bank for its flagship report on AI for development
The World Bank consulted Cyber Sierra for World Development Report 2026: Artificial Intelligence for Development. The WDR is the World Bank's most influential policy publication and shapes AI strategy for governments, central banks, and multilateral institutions globally.
70 to 80% of questionnaire task time is the first draft
The first draft is where analyst time is consumed: locating the right policies, interpreting non-standard formats, and drafting answers that are consistent with past responses. Our AI Analyst compresses this stage from weeks to minutes, leaving the team to handle review and sign-off.
80 per year questionnaires cleared by a 7-person GRC team
An enterprise GRC team managing 80 due diligence questionnaires annually was spending most of its capacity on first drafts while sales timelines slipped. Our AI Analyst now handles the drafting so the team focuses on review, approval, and higher-value compliance work.
Frequently Asked Questions
Does it work with non-standard Excel formats?
Yes. It reads any Excel questionnaire structure automatically: multi-tab layouts, weighted scoring, conditional logic, non-standard column arrangements, embedded links. There is no template to prepare and no column mapping to configure before drafting begins.
If the format is unusual enough that the structure analysis needs a manual override, that is surfaced in the review interface before any answers are generated. You are not committed to a draft you have not seen.
How does it decide which policies to reference?
You control which policies and which version of each policy our AI Analyst references for any given assessment. It does not automatically default to the most recent file: you specify the source set, which means outdated policies are not cited without your explicit instruction.
The underlying Context Graph tracks document version, expiry date, and provenance metadata for every policy file. Our AI Analyst references documents deterministically based on structured metadata rather than relying on the model's memory of what is recent, a design validated by Singapore's Model AI Governance Framework for Agentic AI.
Will the same question get the same answer across our team?
Yes, and that is one of the main reasons teams adopt it. When different analysts draft answers to the same question independently, responses drift across questionnaires. Our AI Analyst draws on your full response history and ensures the same question gets the same answer regardless of who triggers the generation.
How does it verify the answers are accurate?
Every draft answer passes through a reflection architecture with two independent validation nodes before it reaches you. An LLM judge checks for unsupported or fabricated claims. A metrics-based node evaluates faithfulness, context relevance, and response relevance against your source documents.