AI-powered audits that score legacy integration feasibility and ROI for energy deals.
Sales teams struggle to prove ROI for energy management software to enterprise clients because legacy systems fail to integrate, causing stalled deals.
ProofPulse scans client-provided legacy system docs or APIs to audit integration risks and project ROI. It outputs scored reports with mitigation steps and savings forecasts. Sales teams use this to de-risk deals and accelerate enterprise sign-offs.
Sales teams selling energy management software to enterprise organizations
AI-driven legacy system analysis specific to energy hardware, predicting integration pitfalls others miss.
supportive
Upload PDFs/specs of legacy systems for AI audit.
0-100 score on ease, risks, and timeline.
Project savings based on audit + usage inputs.
Detailed risks with fix recommendations.
Track and compare multiple client audits.
Direct API endpoint scan for live assessment.
User-added risks for team knowledge base.
Brand reports as client's own.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| company | text | Yes |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| client_name | text | No |
| score | int | No |
| audit_data | jsonb | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
| members | jsonb | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| team_id | uuid | No |
| description | text | No |
Relationships:
/api/auditsRun new audit
/api/audits/:idFetch audit details
/api/auditsList audits
/api/risksAdd custom risk
No teams
Standard AI
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 40 | 4% | $32 | $384 |
| Month 6 | 250 | 8% | $400 | $4,800 |
Target energy sales Slack communities, email outreach to 30 reps via Hunter.io from energy software sites, free audits for testimonials.
Conversation intel
Ignores technical integration
Deep tech audits for energy at lower price
AI models fine-tuned on energy legacy data, user-contributed risk DB for network effects.
Legacy systems aging amid net-zero pushes; AI now viable for doc analysis.
AI hallucination in audits
Human-reviewed prompts + user feedback loop
Slow AI processing
Caching + edge functions
Success: 70% WOY interest
Success: 50% retention
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