Inconsistent submissions
Important facts arrive in different formats, with uneven completeness and evidence quality.
Proposed workflow concept · Finance
Custom AI Workflow Automation for AT Swiss Ventures Deal Review
BiTech Digital designed a proposed six-stage DealFlow concept that turns variable founder submissions into structured, evidence-linked briefs while keeping people responsible for screening, diligence and investment decisions.
Concept status: the figures below describe the proposed system design—not claimed production ROI.
6connected stages
1centralized pipeline
Completeactivity logging
3human approval gates
Project overview
A Financial Workflow Automation Concept Built Around Evidence
The concept demonstrates how founder intake, pitch-deck extraction, approved enrichment, transparent screening and partner review can operate as one traceable process. Automation prepares the evidence; investment professionals retain the decision.
IndustryVenture capital and finance
ServiceCustom AI workflow automation
Project typeProposed DealFlow concept
ProcessSix-stage review workflow
Control modelThree approval gates
The challenge
Fragmented Founder Submissions Make Consistent Review Difficult
Opportunities can arrive through forms, referrals, email, shared documents and pitch decks. Reviewers repeatedly locate company facts, identify missing files, assess thesis alignment, assign follow-up and rebuild context for partner review.
Distributed context
Notes, documents, enrichment results and review decisions can become separated across tools.
Opaque scoring is unsafe
A triage score must expose its criteria and sources—it cannot become an automated investment verdict.
Centralized deal pipeline
One Queue From Intake Through Diligence and Decision
The proposed pipeline creates a consistent operating record for every opportunity. Stages, owners, missing documents, review tasks and activity history stay visible as the deal moves forward.
- Validate required fields and expected submission files.
- Create one structured opportunity record.
- Assign reviewers and track outstanding requests.
- Synchronize approved stage changes with the CRM.
Exception-aware workflow
Low Confidence, Missing Data and Failed Enrichment Become Visible Work
The concept deliberately includes uncertain extraction and an enrichment failure. These conditions do not disappear inside the model: they pause, retry under a defined policy or create a review task with the relevant evidence attached.
- Retain source references and confidence for extracted fields.
- Apply limited retries to approved enrichment services.
- Route unresolved exceptions to a named person.
- Log each transition, retry and approval.
How the concept works
Six Connected Stages With People at the Control Points
This proposed architecture demonstrates how AI workflow automation can keep extraction, scoring, routing, and exception handling inspectable while preserving human approval for every investment decision.
Validate
Check fields, expected documents, file safety and basic formatting.
Extract
Structure company, team, market, traction and funding information with evidence.
Enrich
Add approved context and handle provider failures under a defined retry policy.
Screen
Apply versioned criteria and expose the component-level score breakdown.
Route
Consider confidence, gaps and risk signals before assigning the next reviewer.
Prepare
Assemble verified evidence, open questions and diligence work for partner review.
Transparent decision support
Thesis-Fit Scoring Organizes Review—It Does Not Make the Decision
Reviewers can inspect each screening component, its source information, confidence and criteria version. The interface highlights missing documents, extraction uncertainty, risks and the recommended next verification step.
- Explainable criteria rather than a single opaque score.
- Evidence links and confidence beside extracted facts.
- Clear separation between recommendation and approval.
- Partner or committee ownership of the final decision.
Design outcomes
A Reviewable Operating Model for Venture Deal Preparation
Because this is a proposed concept, the outcome is an accountable architecture rather than a claimed time or conversion result. Each stage has a purpose, evidence trail, owner and exception path.
Evidence-linkedextraction and screening inputs
Versionedscreening criteria
Exception-awareretry and review paths
Human-ownedinvestment decisions
Is this a live production case study?
No. It is explicitly presented as a proposed workflow concept showing how a six-stage investment-review process could be structured.
Can the score approve or reject an investment?
No. The score supports triage and review prioritization. Partners and committee members retain the final decision.
How are extraction mistakes handled?
Each field retains source and confidence information. Low-confidence results pause for human verification.
Can it connect to an existing CRM?
A production implementation could update approved CRM fields and stages through scoped, permission-based integrations.
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