Evidence layer
See the form of the work before discussing an engagement.
This evidence pack demonstrates how Novarra structures decision-ready outputs. Every example is fictional and exists to show method, materiality and reporting discipline—not a client relationship or transaction outcome.
Illustrative sample — not client work
Three public deliverables
Inspect the work in the form a decision-maker would receive it.
Each dedicated sample is browser-viewable, explicitly fictional and structured around evidence, materiality and the next decision.
Deal Technology Due Diligence
Red Flag Extract
Evidence, severity, investment implication and recommended action in one decision view.
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Portfolio Technology Assurance
Board Heatmap
Cross-portfolio priorities linked to board questions, ownership and escalation.
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AI & Technology Readiness
Executive Assessment
Readiness dimensions, executive implications and the priority actions needed before scale.
Open sample →
Sample 01
Technology due-diligence executive summary
Fictional B2B software company being assessed for a growth investment. The illustrative investment thesis assumes continued enterprise expansion and faster product delivery.
Illustrative — medium confidence
Illustrative technology verdict
Proceed with conditions
The fictional scenario supports continued diligence subject to explicit technology conditions and a funded post-close plan.
Strengths
- Core product architecture can support near-term growth with targeted remediation.
- Customer-facing product and roadmap are coherent with the stated market direction.
- Engineering team has strong product knowledge and acceptable delivery cadence.
Conditions
- Reduce deployment and database key-person dependency within 100 days.
- Formalize privileged-access governance and production access review.
- Fund the highest-priority scalability remediation before the next major customer cohort.
Unresolved items
- Long-term data-warehouse economics require validation against the three-year operating model.
- AI product roadmap is not yet supported by a sufficiently mature data-governance model.
Sample 02
Red-flag and remediation register
A useful red flag connects the evidence to the business consequence, investment implication and next action.
| Severity | Finding | Evidence | Investment implication | Recommended action |
|---|---|---|---|---|
| High | Deployment and database administration depend heavily on one senior engineer. | Illustrative interviews, access map and operating runbook review. | Execution and resilience risk; absence or departure could delay releases and recovery. | Dual ownership, runbook completion, privileged-access review and tested recovery handover. |
| High | Privileged production access is broader than the target operating model requires. | Illustrative identity-role export and management confirmation. | Raises the probability and impact of accidental or unauthorized production change. | Reduce standing privilege, introduce named roles and quarterly access attestation. |
| Medium | One analytics vendor is deeply embedded in reporting and customer workflows. | Illustrative architecture and contract dependency review. | Potential switching cost and pricing exposure as transaction volumes scale. | Quantify exit cost, add contract guardrails and document the replacement path. |
| Medium | AI roadmap is ahead of data-governance maturity. | Illustrative roadmap, data inventory and governance review. | AI delivery could stall or create avoidable privacy, quality and model-risk exposure. | Sequence data ownership, permitted-use rules and model controls before broad rollout. |
Sample 03
Portfolio technology heatmap
A family-office view should make cross-portfolio priorities visible without pretending that a red/amber/green cell replaces the underlying evidence.
| Fictional company | Architecture | Cyber | AI / Data | Leadership | Delivery | Exit readiness |
|---|---|---|---|---|---|---|
| Atlas | Amber | Red | Amber | Red | Green | Amber |
| Beacon | Green | Amber | Red | Green | Amber | Amber |
| Cedar | Amber | Green | Amber | Amber | Green | Green |
| Delta | Red | Amber | Green | Amber | Red | Amber |
All company names and statuses above are fictional. Heatmap status would normally be traceable to detailed findings and agreed materiality thresholds.
Sample 04
100-day technology plan
Diligence becomes more valuable when material findings are converted into ownership, sequencing and evidence of closure.
Days 0–30
Stabilize ownership and access
- Assign accountable owners for critical platforms and recovery procedures.
- Reduce privileged access and establish production-access review.
- Confirm remediation budget and executive sponsor.
Closure evidence: Named owners, approved access matrix, funded action plan.
Days 31–60
Remove material execution bottlenecks
- Complete deployment/database handover and recovery tests.
- Address highest-risk scalability constraint.
- Implement minimum AI/data governance controls.
Closure evidence: Recovery test, architecture benchmark, approved AI/data control set.
Days 61–100
Move into measured assurance
- Establish board technology KPIs and risk cadence.
- Close or reclassify critical red flags.
- Set 12-month roadmap and investment milestones.
Closure evidence: Board pack, risk closure evidence, approved roadmap.
Sample 05
Board technology KPI pack
Board technology reporting should support challenge and escalation—not just produce more operational metrics.
2 open / 1 closing
Critical technology risks
Are the red risks reducing on the agreed timetable?
Recovery test: pass
Availability / resilience
Can the company recover the services that matter most?
92% named-role coverage
Privileged access
Is high-risk access moving toward the agreed control target?
84% roadmap commitments met
Delivery predictability
Is execution becoming more predictable as the company scales?
3 of 5 priority controls operating
AI / data governance
Is governance keeping pace with the AI roadmap?
78% of 100-day plan funded
Technology investment
Are material remediation items properly resourced?
Values are fictional examples, not Novarra or client performance data.
Sample 06
AI readiness matrix
The score is a navigation aid. The useful evidence is the reason behind the score and the actions needed to change it.
Strategy
Clear use cases tied to commercial priorities.
Data
Ownership and permitted-use rules require work.
Architecture
Integration path is viable but not yet standardized.
Governance
Model-risk and approval controls are immature.
People
Capable team; specialist ownership remains thin.
Security & privacy
Baseline controls exist; AI-specific controls are incomplete.
Sample 07
Illustrative diligence timeline
A compact transaction review can be structured around decision framing, evidence intake, deep review, synthesis and an investment read-out.
Day 0
Scope and decision framing
Confirm the decision, access, materiality threshold, workstreams, stakeholders and reporting route.
Days 1–3
Evidence intake
Review the data room and available technical evidence; identify gaps and management questions.
Days 3–7
Deep review and management challenge
Test the most material architecture, product, security, AI/data, vendor, people and delivery assumptions.
Days 7–9
Synthesis
Translate findings into red flags, investment implications, remediation priorities and unresolved items.
Days 9–10
Decision read-out
Deliver the executive view, answer decision-maker questions and hand material items into post-close priorities.
Evidence before engagement
Use the sample pack to judge whether the reporting style fits your decision process.
A real engagement would tailor the evidence perimeter, materiality threshold, workstreams and outputs to the transaction or portfolio context.