Graph database for process tracking

Problem Context

Data teams in finance and enterprise use SQL databases to track internal processes like workflows, dependencies, and data lineage. But SQL struggles with complex relationships, forcing teams to either fight bad designs or abstract everything behind APIs. This creates technical debt, wasted dev time, and poor visibility into critical process data.

Pain Points

Teams waste hours forcing SQL to handle graph-like data, leading to messy schemas and slow queries. Non-technical stakeholders resist graph databases because they seem overkill, and manual workarounds (like Excel or custom scripts) break under scale. The result is a fragile system where process tracking becomes a bottleneck, not a tool.

Impact

Failed SQL designs cost teams *weeks of rework- and lost productivity. Poor process visibility leads to missed dependencies, delays in workflows, and *frustration- when data isn’t trustworthy. In finance, this can even risk compliance or audit failures if lineage isn’t tracked properly.

Urgency

This problem can’t be ignored because SQL isn’t going away, but neither are the graph-like needs of process data. Teams hit a wall when they realize SQL can’t efficiently model dependencies, and the longer they wait, the harder it is to fix without a major rewrite.

Target Audience

Data engineers, actuaries, and process owners in *finance, healthcare, and enterprise IT- who manage workflows, data lineage, or internal system dependencies. Any team that relies on SQL but needs *graph-like relationships- for process tracking would face this issue.

Solution Approach

A *lightweight SaaS wrapper for graph databases- that lets teams model process data (workflows, dependencies, lineage) without managing a full graph DB. It provides *pre-built schemas and queries- for common finance/enterprise use cases, so users can start in minutes—not months. The product sits between SQL and a managed graph DB, translating graph queries into SQL-friendly outputs for reports.

Key Features

1. *SQL-friendly API- – Lets teams query graph data using familiar SQL syntax, so reports and dashboards work without changes.
2. *No-code workflow builder- – Non-technical users can define processes and dependencies via a drag-and-drop UI.
3. Automated lineage tracking – Monitors changes in process data and flags broken dependencies in real time.

User Experience

A data engineer imports their process data (e.g., a finance workflow) into the tool using the pre-built schema. They define relationships (e.g., ‘Step A depends on Step B’) via the UI or API. The system then automatically tracks dependencies, flags risks, and lets them query the data in SQL. Non-technical teams see a dashboard of process health, while engineers get a clean API for integrations.

Differentiation

Unlike raw graph databases (too complex) or SQL (too limited), this tool *specializes in process tracking- with finance/enterprise templates. It hides graph DB complexity behind a simple API and UI, so teams don’t need graph DB experts. Competitors either require manual setup (Neo4j) or fail to model relationships well (SQL).

Scalability

Starts with *team-based pricing- ($79/mo for 5 users) and scales with *seat additions- or *advanced features- (e.g., AI-driven dependency analysis). Enterprises can white-label the API for internal tools, and the product grows with the user’s process complexity (e.g., adding more workflows or integrations).

Expected Impact

Teams *save 10+ hours/week- on manual process tracking and avoid costly SQL rewrites. Process visibility improves, reducing errors and delays. Finance teams gain audit-ready lineage data, and engineers spend less time fighting bad designs. The tool pays for itself in one failed SQL project avoided.