# RESOLUTIONFLOW — KB Accelerator

## Feature Design Document

*Transform static KB articles into interactive troubleshooting and procedural flows with AI-powered document analysis.*

| Field | Value |
|-------|-------|
| **Document** | KB Accelerator — Feature Design & Architecture |
| **Version** | 1.0 — Draft |
| **Date** | March 2026 |
| **Author** | ResolutionFlow LLC |
| **Status** | Design Phase |

---

## Table of Contents

1. Executive Summary
2. Problem Statement & Market Opportunity
3. Feature Overview
4. System Architecture
5. AI Processing Pipeline
6. Data Model
7. API Design
8. Frontend Design
9. Supported Input Formats
10. Conversion Intelligence
11. Pricing & Tier Integration
12. Build Phases & Roadmap
13. Risk Analysis
14. Success Metrics

---

## 1. Executive Summary

KB Accelerator is a new feature for ResolutionFlow that allows MSP teams to upload their existing knowledge base articles and automatically convert them into interactive troubleshooting flows and procedural flows. This solves the cold-start adoption problem, transforms passive documentation into active troubleshooting tools, and delivers immediate value from day one.

> **Core Value Proposition**
>
> MSPs have years of institutional knowledge trapped in static Word docs, PDFs, and wiki articles that nobody reads mid-ticket. KB Accelerator transforms that content into the interactive, branching flows that ResolutionFlow is built around — turning dead documentation into living troubleshooting intelligence.

### Key Capabilities

- Upload KB articles in multiple formats (DOCX, PDF, HTML, Markdown, plain text, copy-paste)
- AI-powered analysis that detects sequential steps, decision points, prerequisites, and resolution outcomes
- Automatic mapping to ResolutionFlow's existing tree schema (troubleshooting flows) and procedural schema (procedure flows)
- Intelligent detection of implicit branching logic buried in prose documentation
- Draft flow output that lands directly in the flow editor for human review and refinement
- Confidence scoring on each generated node so users know where AI interpretation needs attention
- Batch import capability for migrating entire KB libraries

### Strategic Impact

| Impact Area | Description |
|---|---|
| **Adoption** | Eliminates cold-start problem. New users get a library of draft flows on day one instead of building from scratch. |
| **Retention** | Users who import existing KB articles are investing their institutional knowledge into the platform, increasing switching costs. |
| **Revenue** | Pro/Team-gated feature that directly justifies subscription pricing. AI processing costs are per-conversion, aligning expense with usage. |
| **Differentiation** | No competing MSP documentation tool offers AI-powered conversion from static docs to interactive decision trees. |
| **Thesis Validation** | Proves the core ResolutionFlow thesis: documentation and troubleshooting should be the same activity. |

---

## 2. Problem Statement & Market Opportunity

### 2.1 The KB Problem in MSPs

Every MSP has knowledge base articles. They live in ConnectWise, IT Glue, Hudu, SharePoint, Confluence, or simply as Word documents on a shared drive. These articles represent years of accumulated troubleshooting experience and process documentation. The problem is that this content is fundamentally passive. It exists as prose that a technician has to read, interpret, and mentally convert into action steps while simultaneously working a live ticket.

The result is predictable: technicians don't read the KB articles. They ask a senior engineer instead, or they fumble through the issue on their own. The documentation exists but delivers no value because the format doesn't match the workflow.

### 2.2 The Cold-Start Problem

ResolutionFlow solves the format problem by making documentation interactive. But it introduces a new problem: a new ResolutionFlow customer has zero flows. Building troubleshooting trees from scratch is time-consuming. The customer has to invest significant effort before they see value, and most MSPs don't have that patience.

> **The Gap**
>
> MSPs have the knowledge (in KB articles). ResolutionFlow has the format (interactive flows). KB Accelerator bridges the gap by converting one into the other automatically.

### 2.3 Competitive Landscape

No MSP-focused tool currently offers AI-powered conversion from static documentation to interactive troubleshooting workflows. IT Glue and Hudu offer structured documentation but no interactive execution. ConnectWise's KB is search-based and static. This is a genuine whitespace opportunity.

---

## 3. Feature Overview

### 3.1 User Journey

1. User navigates to KB Accelerator from the ResolutionFlow dashboard (dedicated tab or sidebar action).
2. User uploads a file (DOCX, PDF, HTML, MD, TXT) or pastes raw text content directly.
3. System analyzes the document structure and displays a preview of detected elements: title, problem statement, steps, decision points, prerequisites, and resolution outcomes.
4. User selects target flow type: Troubleshooting Flow (branching decision tree) or Procedure Flow (linear steps with optional intake form).
5. AI processing pipeline generates a draft flow mapped to ResolutionFlow's schema.
6. User reviews the draft in a side-by-side view: original document on the left, generated flow preview on the right.
7. User can accept, edit, or regenerate individual nodes before finalizing.
8. Finalized flow is saved and appears in the user's flow library, ready for use or further editing in the standard flow editor.

### 3.2 Two Conversion Modes

**Troubleshooting Flow Conversion**

Best for: diagnostic articles, if/then troubleshooting guides, articles with multiple resolution paths.

- AI identifies the root question or symptom being diagnosed
- Decision nodes are created at each branching point ("if X, try Y; otherwise try Z")
- Resolution nodes capture final outcomes and fix instructions
- The branching tree maps to the existing node/option schema in the trees table

**Procedure Flow Conversion**

Best for: step-by-step guides, setup procedures, onboarding checklists, runbooks.

- AI extracts sequential steps in order
- Variable placeholders are detected (server names, IPs, usernames) and mapped to `[VAR:name]` tokens
- An intake form schema is auto-generated from detected variables
- Steps are enriched with detected warnings, time estimates, and verification checks
- Output uses the procedural `tree_type` with the `intake_form` JSONB schema from migration 035

---

## 4. System Architecture

### 4.1 High-Level Architecture

KB Accelerator integrates into the existing ResolutionFlow stack without introducing new infrastructure. It leverages the FastAPI backend for orchestration, the existing AI service (same infrastructure as the assistant chat) for document analysis, and outputs directly into the existing tree/node schema.

**Processing Pipeline Overview**

```
1. UPLOAD        →  2. EXTRACT       →  3. ANALYZE        →  4. GENERATE      →  5. REVIEW
File upload or      Text extraction     AI analysis of       Map to tree/        Side-by-side
text paste via      from DOCX/PDF/      structure, steps,    node schema or      review, edit,
API endpoint        HTML/MD/TXT         decision points      procedure schema    and finalize
```

### 4.2 Component Responsibilities

| Component | Responsibility | Integration Point |
|---|---|---|
| **Upload Service** | File validation, format detection, size limits, virus scanning hook | FastAPI endpoint, S3/local temp storage |
| **Extraction Service** | Convert uploaded files to normalized plain text with structural metadata | python-docx, PyMuPDF, BeautifulSoup, markdown-it |
| **AI Analysis Service** | Prompt engineering pipeline that identifies document structure and converts to flow schema | Anthropic API (Claude), existing AI service infrastructure |
| **Flow Generator** | Maps AI analysis output to tree/node database records with proper relationships | SQLAlchemy models, existing tree/node CRUD services |
| **Review UI** | Side-by-side document vs. flow preview with per-node editing | React frontend, existing flow editor components |
| **Batch Processor** | Queue-based processing for multi-article imports | Celery/Redis or async FastAPI background tasks |

---

## 5. AI Processing Pipeline

The AI pipeline is the core intelligence of KB Accelerator. It operates in two phases: structural analysis (understanding the document) and flow generation (converting that understanding into a ResolutionFlow-compatible schema). This two-phase approach allows for human review between analysis and generation.

### 5.1 Phase 1: Document Analysis

The first AI call analyzes the extracted text and returns a structured JSON document describing what was found. The prompt is carefully engineered to identify MSP-specific patterns.

**Analysis Prompt Strategy**

- System prompt establishes the AI as an MSP documentation specialist that understands IT troubleshooting workflows
- The extracted text is provided with any structural metadata (headings, lists, numbered steps) preserved
- AI is instructed to return a strict JSON schema identifying: document type, title, problem statement, prerequisites, sequential steps, decision points, resolution outcomes, and detected variables

**Detection Targets**

| Element | What AI Looks For | Example in KB Article |
|---|---|---|
| **Document Type** | Whether the article is diagnostic (troubleshooting) or procedural (step-by-step) | *"Troubleshooting Outlook connectivity" vs "Setting up a new domain controller"* |
| **Problem Statement** | The root issue or task being addressed | *"Users report that Outlook keeps disconnecting from Exchange"* |
| **Prerequisites** | Things that must be true before starting | *"Ensure you have Domain Admin credentials and the server is on the network"* |
| **Sequential Steps** | Ordered instructions that must happen in sequence | *"Step 1: Open Server Manager. Step 2: Add Roles and Features..."* |
| **Decision Points** | Conditional logic, if/then/else branches | *"If the user is on Windows 10, check the registry. On Windows 11, go to Settings..."* |
| **Variables** | Instance-specific values that change per execution | *Server names, IP addresses, usernames, license types, domain names* |
| **Warnings/Cautions** | Risk indicators or critical notes | *"WARNING: This will restart the DNS service and cause brief connectivity loss"* |
| **Resolution Outcomes** | End states that indicate the problem is solved | *"Outlook should now maintain a persistent connection to Exchange"* |
| **Verification Steps** | How to confirm a step or procedure worked | *"Run nslookup to verify DNS resolution is working correctly"* |

### 5.2 Phase 2: Flow Generation

The second AI call takes the structured analysis from Phase 1 and generates the actual flow structure, mapped directly to ResolutionFlow's schema. The output format differs based on the target flow type.

**Troubleshooting Flow Output**

- Root node with the problem statement as the question text
- Decision nodes with options array matching the existing node schema (question, options with label and next_node_id)
- Resolution nodes at leaf positions with solution text and tags from the six-dimension tagging system
- Each node includes a `confidence_score` (0.0–1.0) indicating how certain the AI is about the mapping

**Procedure Flow Output**

- Ordered steps array with rich metadata (type, content, warnings, time estimates, verification checks)
- Auto-generated `intake_form` schema from detected variables, with field types inferred (text for names, ip_address for IPs, select for known enumerations)
- `[VAR:name]` tokens injected into step content wherever variables were detected
- Section headers generated from logical groupings in the source document

### 5.3 Confidence Scoring

Every generated node includes a confidence score that communicates how certain the AI is about its interpretation. This is critical for the review step — it tells the user exactly where to focus their attention.

| Score Range | Label | UI Indicator | Meaning |
|---|---|---|---|
| **0.9 – 1.0** | High Confidence | Green left accent | Direct mapping from explicit steps or clear logic in the source |
| **0.7 – 0.89** | Medium Confidence | Amber left accent | Reasonable inference, but some ambiguity in the source material |
| **0.5 – 0.69** | Low Confidence | Red left accent | Significant interpretation required; user should carefully review |
| **< 0.5** | Needs Review | Red left accent + flag icon | AI made a best guess but recommends manual editing |

> **Design Note: Left Accent Border Pattern**
>
> The confidence indicators use the left accent border pattern established in the ResolutionFlow design system. This provides visual consistency with step status indicators and documentation callouts already in the UI.

---

## 6. Data Model

KB Accelerator introduces two new database tables and extends the existing tree model. All new tables follow the existing migration pattern and use the same base model infrastructure.

### 6.1 New Table: `kb_imports`

| Column | Type | Nullable | Description |
|---|---|---|---|
| **id** | UUID | No | Primary key (gen_random_uuid) |
| **organization_id** | UUID FK | No | Foreign key to organizations table |
| **created_by** | UUID FK | No | Foreign key to users table (who initiated the import) |
| **source_filename** | VARCHAR(500) | Yes | Original filename if file upload (null for text paste) |
| **source_format** | VARCHAR(20) | No | Enum: docx, pdf, html, md, txt, paste |
| **source_text** | TEXT | No | Extracted plain text content from the source document |
| **source_metadata** | JSONB | Yes | Structural metadata from extraction (headings, lists, etc.) |
| **analysis_result** | JSONB | Yes | Phase 1 AI analysis output (detected elements) |
| **target_type** | VARCHAR(20) | No | Enum: troubleshooting, procedural |
| **generated_flow** | JSONB | Yes | Phase 2 AI generation output (flow schema before commit) |
| **tree_id** | UUID FK | Yes | Foreign key to trees table (set after user finalizes) |
| **status** | VARCHAR(20) | No | Enum: uploaded, extracting, analyzing, reviewed, generating, completed, failed |
| **confidence_avg** | FLOAT | Yes | Average confidence score across all generated nodes |
| **error_message** | TEXT | Yes | Error details if status = failed |
| **processing_time_ms** | INTEGER | Yes | Total processing time in milliseconds |
| **created_at** | TIMESTAMPTZ | No | Auto-set on creation |
| **updated_at** | TIMESTAMPTZ | No | Auto-updated on modification |

### 6.2 New Table: `kb_import_nodes`

Stores individual generated nodes/steps before the user commits them to the actual tree. This allows per-node editing during the review phase without touching the live flow data.

| Column | Type | Nullable | Description |
|---|---|---|---|
| **id** | UUID | No | Primary key |
| **kb_import_id** | UUID FK | No | Foreign key to kb_imports |
| **node_order** | INTEGER | No | Position in the generated flow (0-indexed) |
| **node_type** | VARCHAR(20) | No | Enum: question, resolution, step, section_header, warning |
| **content** | JSONB | No | Node content (question text, step text, options, etc.) |
| **source_excerpt** | TEXT | Yes | The specific text from the source document that this node was derived from |
| **confidence_score** | FLOAT | No | AI confidence in this node's accuracy (0.0–1.0) |
| **user_edited** | BOOLEAN | No | Whether the user manually modified this node during review |
| **user_approved** | BOOLEAN | No | Whether the user explicitly approved this node |

### 6.3 Tree Model Extension

The existing trees table gets one new nullable column to link back to the import that created it. This enables analytics and provenance tracking.

**New column:** `kb_import_id` (UUID FK, nullable) — references `kb_imports.id`. Null for manually-created trees.

---

## 7. API Design

All KB Accelerator endpoints live under the `/api/v1/kb-accelerator` prefix and follow existing authentication, organization scoping, and error handling patterns.

### 7.1 Endpoints

| Method | Endpoint | Description |
|---|---|---|
| **POST** | `/api/v1/kb-accelerator/upload` | Upload a file or submit pasted text. Returns kb_import_id and starts extraction. |
| **GET** | `/api/v1/kb-accelerator/{id}` | Get import status, analysis results, and generated flow data. |
| **GET** | `/api/v1/kb-accelerator` | List all imports for the current organization with pagination and status filter. |
| **POST** | `/api/v1/kb-accelerator/{id}/analyze` | Trigger Phase 1 AI analysis on extracted text. Async — poll status via GET. |
| **POST** | `/api/v1/kb-accelerator/{id}/generate` | Trigger Phase 2 flow generation from analysis results. Requires target_type. |
| **PATCH** | `/api/v1/kb-accelerator/{id}/nodes/{node_id}` | Edit a specific generated node during review (content, approve, reject). |
| **POST** | `/api/v1/kb-accelerator/{id}/commit` | Finalize the import: create actual tree and node records from generated data. |
| **DELETE** | `/api/v1/kb-accelerator/{id}` | Cancel and clean up an in-progress or abandoned import. |
| **POST** | `/api/v1/kb-accelerator/batch` | Submit multiple files for batch processing. Returns array of kb_import_ids. |

### 7.2 Upload Endpoint Detail

**POST /api/v1/kb-accelerator/upload**

Accepts multipart/form-data for file uploads or application/json for text paste. Validates file size (max 10MB), format, and performs basic content extraction before returning.

**Request Body (File Upload)**

- **file**: UploadFile (required) — the KB article file
- **target_type**: string (optional) — "troubleshooting" or "procedural" (can be set later)

**Request Body (Text Paste)**

- **content**: string (required) — raw text content
- **title**: string (optional) — suggested title for the import
- **target_type**: string (optional) — "troubleshooting" or "procedural"

**Response (201 Created)**

- **id**: UUID — the new kb_import record ID
- **status**: "uploaded" or "extracting" (extraction may start immediately)
- **source_format**: detected format of the uploaded content

---

## 8. Frontend Design

### 8.1 Entry Points

KB Accelerator is accessible from two locations in the existing UI to maximize discoverability:

- **Dashboard action button:** a prominent "Import KB Article" button in the flow library header, next to "Create New Flow"
- **Sidebar navigation:** dedicated "KB Accelerator" item in the main navigation with a sparkle/lightning icon to communicate AI-powered functionality

### 8.2 Upload Screen

Clean, focused upload interface with two input modes:

- Drag-and-drop zone for file uploads with format badges showing supported types (DOCX, PDF, HTML, MD, TXT)
- Text paste tab with a full-width textarea and title field for direct content entry
- Target type selector (Troubleshooting Flow / Procedure Flow) with visual cards showing the difference
- "Let AI decide" option for target type that uses the analysis phase to recommend the best fit

### 8.3 Analysis Preview Screen

After Phase 1 analysis completes, the user sees a breakdown of what the AI detected:

- Document title and detected type with AI recommendation badge
- Detected elements displayed as color-coded cards: steps (blue), decision points (amber), warnings (red), variables (green), resolutions (emerald)
- Source text excerpts linked to each detected element so the user can see exactly what triggered the detection
- "Proceed to Generation" and "Re-analyze" action buttons

### 8.4 Review Screen (Core Experience)

The review screen is the most important UI in KB Accelerator. It's where the user validates AI output and builds trust in the system.

**Layout: Two-Panel Side-by-Side**

- Left panel: Original document text with detected elements highlighted inline (color-matched to the generated nodes)
- Right panel: Generated flow preview showing the tree structure (for troubleshooting) or step list (for procedures)
- Clicking a node in the right panel highlights its source excerpt in the left panel, and vice versa
- Each node shows its confidence score via the left accent border pattern (green/amber/red)

**Per-Node Actions**

- **Approve** (checkmark): Marks the node as reviewed and accepted
- **Edit** (pencil): Opens inline editing for the node's content, question text, options, etc.
- **Regenerate** (refresh): Re-runs AI generation for just this node with optional user guidance
- **Delete** (trash): Removes the node from the generated flow
- **Add Node** (plus): Insert a manual node between existing ones

**Bulk Actions**

- "Approve All High Confidence" — one-click approval for all nodes scoring 0.9+
- "Commit to Library" — finalizes the flow and creates the actual tree record

> **UI Principle**
>
> The review screen should feel like a code review, not a form. The user is reviewing AI-generated work with the power to accept, modify, or reject each piece. The side-by-side layout with source attribution builds trust by showing the AI's reasoning.

---

## 9. Supported Input Formats

| Format | Library | Structure Preserved | Notes |
|---|---|---|---|
| **DOCX** | python-docx | Headings, lists, tables, bold/italic emphasis | Most common format for MSP KB articles in SharePoint and shared drives |
| **PDF** | PyMuPDF (fitz) | Text extraction with layout awareness, headings via font size | Second most common; handles scanned docs with OCR fallback via Tesseract |
| **HTML** | BeautifulSoup | Full semantic structure (h1-h6, ul/ol, tables, code blocks) | Covers Confluence, IT Glue, and web-based KB exports |
| **Markdown** | markdown-it | Headings, lists, code blocks, emphasis, links | Common in developer-oriented documentation and GitHub repos |
| **Plain Text** | Built-in | Line breaks and indentation only; AI infers structure | Lowest fidelity but important for copy-paste and email-sourced docs |
| **Paste** | Built-in | None — AI infers all structure from content | Zero-friction entry point for quick conversions |

> **Extraction Quality Hierarchy**
>
> DOCX and HTML provide the richest structural metadata, giving the AI the most to work with. PDF extraction is good but lossy (formatting information is approximate). Plain text and paste require the AI to infer all structure from content alone, which reduces confidence scores but still produces usable output for well-written articles.

---

## 10. Conversion Intelligence

This section details the specific AI patterns and heuristics used to convert different types of KB content into flows. This is where KB Accelerator's real value lives — the ability to interpret messy, inconsistent MSP documentation and produce structured, actionable flows.

### 10.1 Detecting Implicit Branch Logic

The hardest challenge is identifying decision points that aren't explicitly written as if/then statements. MSP KB articles often bury branching logic in prose.

**Pattern Examples**

| KB Article Text | AI Interpretation |
|---|---|
| *"For Windows 10 machines, navigate to Settings > Update. For Windows 11, go to Settings > Windows Update."* | Decision node: "What Windows version?" with two branches leading to different step sequences |
| *"If the issue persists after restarting the service, escalate to Tier 2."* | Decision node after the restart step: "Did the restart resolve the issue?" with Yes (resolution) and No (escalation) paths |
| *"Note: Domain-joined computers use Group Policy. Workgroup computers need manual configuration."* | Decision node: "Is the computer domain-joined?" with two parallel procedure paths |
| *"Try clearing the DNS cache first. If that doesn't work, check the hosts file."* | Sequential diagnostic flow: DNS cache clear > verification > hosts file check, with early exit if first step resolves |

### 10.2 Variable Detection

For procedure flow conversion, the AI identifies values that would change between executions and maps them to `[VAR:name]` tokens with appropriate intake form field types.

| Detected Pattern | Variable Name | Form Field Type | Example Value |
|---|---|---|---|
| IP addresses (192.168.x.x) | `[VAR:ip_address]` | ip_address | 192.168.1.10 |
| Server/computer names | `[VAR:server_name]` | text | DC01 |
| Domain names | `[VAR:domain_name]` | text | contoso.local |
| Usernames/email | `[VAR:username]` | text | jsmith@contoso.com |
| License types | `[VAR:license_type]` | select (enum) | E3, E5, F1 |
| OU paths | `[VAR:ou_path]` | text | OU=Users,DC=contoso,DC=local |
| Port numbers | `[VAR:port]` | number | 443 |
| Subnet masks | `[VAR:subnet_mask]` | ip_address | 255.255.255.0 |

---

## 11. Pricing & Tier Integration

KB Accelerator is a premium feature that justifies Pro and Team subscription pricing. The AI processing has a real per-conversion cost (Anthropic API usage), so tiering aligns expense with revenue.

| Capability | Free | Pro ($19/mo) | Team ($15/user/mo) |
|---|---|---|---|
| **Single article import** | 3 lifetime conversions | Unlimited | Unlimited |
| **Batch import** | Not available | Up to 10 articles | Up to 50 articles |
| **Text paste** | Included in 3 conversions | Unlimited | Unlimited |
| **Target type selection** | AI decides only | Manual + AI | Manual + AI |
| **Review & edit** | Basic (approve/reject) | Full (edit, regenerate, add) | Full + team review |
| **Confidence scoring** | Shown | Shown + filter/sort | Shown + filter/sort |
| **Import history** | Last 3 only | Full history | Full history + audit log |
| **Supported formats** | TXT and paste only | All formats | All formats |

> **Free Tier Strategy**
>
> The free tier offers 3 lifetime conversions with limited formats. This is enough for a user to experience the value and see KB Accelerator work on their actual documentation. The restriction to TXT/paste on free tier also reduces extraction library dependencies for free-tier infrastructure cost optimization.

---

## 12. Build Phases & Roadmap

### Phase 1: Foundation (Weeks 1–3)

Core pipeline with single-article import and basic review.

- Database migrations: `kb_imports` and `kb_import_nodes` tables, tree model extension
- Upload endpoint with text paste and TXT file support
- Text extraction service (plain text only in Phase 1)
- AI analysis prompt engineering and Phase 1 pipeline
- AI generation prompt engineering and Phase 2 pipeline (troubleshooting flow output only)
- Basic review UI: list view of generated nodes with approve/reject
- Commit endpoint that creates actual tree/node records

### Phase 2: Rich Formats & Procedures (Weeks 4–6)

Full format support and procedure flow conversion.

- DOCX extraction via python-docx with structural metadata
- PDF extraction via PyMuPDF with layout analysis
- HTML extraction via BeautifulSoup
- Markdown extraction via markdown-it
- Procedure flow generation with variable detection and intake form generation
- Side-by-side review UI with source-to-node linking
- Per-node editing and regeneration in the review screen
- Confidence scoring visualization with left accent borders

### Phase 3: Polish & Scale (Weeks 7–9)

Batch import, UX refinement, and tier enforcement.

- Batch upload endpoint and queue processing
- Import history dashboard with status tracking
- Tier gating enforcement (free tier limits, format restrictions)
- "Approve All High Confidence" bulk action
- Analytics: conversion success rate, average confidence, most-used source formats
- Drag-and-drop file upload zone with format badges
- "Let AI decide" target type recommendation

### Phase 4: Advanced Intelligence (Future)

Stretch goals and post-launch enhancements.

- OCR fallback for scanned PDFs via Tesseract
- Multi-article correlation: detect when multiple KB articles describe the same issue from different angles and suggest merging
- Incremental re-import: detect when a source KB article has been updated and suggest flow updates
- ConnectWise/IT Glue/Hudu direct API integration for pulling articles without manual export
- Tag auto-assignment using the six-dimension tagging system based on article content analysis
- Template marketplace: share anonymized, high-confidence converted flows with the ResolutionFlow community

---

## 13. Risk Analysis

| Risk | Severity | Impact | Mitigation |
|---|---|---|---|
| **Poor quality KB input** | Medium | AI produces low-confidence flows that require extensive manual editing, reducing perceived value | Confidence scoring sets expectations. "Needs Review" flags prevent silent bad output. Free tier lets users test before committing. |
| **AI hallucination in flow logic** | High | Generated flows contain incorrect troubleshooting paths that could lead technicians astray | Mandatory review step before commit. Source attribution shows exact text the AI based each node on. No auto-publish. |
| **API cost overruns** | Medium | High usage of AI analysis burns through API budget faster than subscription revenue covers | Per-conversion cost tracking. Tier limits on batch size. Prompt optimization to minimize token usage. |
| **Extraction library maintenance** | Low | python-docx, PyMuPDF, etc. may have breaking changes or security issues | Pin versions. Phase 1 starts with text-only to defer library dependency. Each format is an independent module. |
| **User trust gap** | High | Users don't trust AI-generated flows and abandon the feature after trying it once | Side-by-side source view builds trust. Confidence scoring is transparent. Start with high-quality conversion on well-structured articles to build initial trust. |
| **Scope creep** | Medium | Feature grows to include direct PSA integration, real-time sync, and other complex functionality before core is proven | Phased roadmap with clear scope boundaries. Phase 4 items are explicitly deferred until post-launch data validates demand. |

---

## 14. Success Metrics

These KPIs determine whether KB Accelerator is delivering value to users and justifying its development investment.

| Metric | Target | Why It Matters |
|---|---|---|
| **Conversion completion rate** | > 70% | Percentage of started imports that reach "committed" status. Below 70% suggests the review step is too burdensome or quality is too low. |
| **Average confidence score** | > 0.75 | Across all generated nodes. Indicates the AI pipeline is producing reliably accurate output. |
| **Time from upload to commit** | < 10 minutes | The full cycle should feel fast. If users are spending 30+ minutes editing, the AI isn't saving enough time. |
| **Free-to-Pro conversion rate** | > 15% | Users who use their 3 free conversions and then upgrade. This validates that experiencing the feature drives subscription revenue. |
| **Repeat usage (Pro/Team)** | > 3 imports/month | Users who import once and never again didn't find sustained value. Repeat usage indicates the feature is part of their workflow. |
| **Node edit rate** | < 30% | Percentage of generated nodes that users edit before committing. Lower is better — means AI output is usable as-is. |
| **Imported flow usage rate** | > 50% | Percentage of committed flows that get used in actual troubleshooting sessions within 30 days. Unused flows mean the conversion produced shelfware. |

---

*End of Document*

ResolutionFlow LLC — March 2026
