Agency Profitability Tracker

Part of the worlds-biggest-software-project initiative.

Real-time project margin visibility for agencies that refuse to discover profitability problems after the invoice has shipped.

Agencies in creative, marketing, and professional services routinely operate blind: project profitability is calculated after delivery, when the opportunity to course-correct has passed. The Agency Profitability Tracker connects time data, resource costs, overhead allocation, and revenue into a live margin view, giving operational leaders the signals they need mid-project. Industry benchmarks show that agencies with real-time utilisation tracking report profitability 20-30% higher than those relying on retrospective analysis.

Why Agency Profitability Tracker?

Retroactive-only reporting is the norm. Most agencies discover a project was unprofitable only after it has been delivered. Tools that calculate margins in real time -- as hours are logged -- enable intervention, not just retrospection.
Overhead allocation is still manual and imprecise. The majority of existing solutions require hand-configured allocation rules. Few auto-calculate burden rates from facility costs, benefits, and taxes, leading to inaccurate margin figures across the board.
Pricing opacity in the incumbent market. Leading PSA platforms (Kantata, Accelo) require sales calls for pricing and impose seat minimums ($20-$90/user/month, 5-10 seat minimums). Accelo's complex customisation can cost $2K-$10K in consulting fees before a team even starts.
No estimation feedback loop. Existing tools track actuals but do not systematically compare them to estimates to improve future accuracy. Agencies repeat the same estimation errors project after project.
Fragmented tooling. Lightweight trackers (Harvest, Toggl) lack resource scheduling and overhead allocation. Full PSA suites (Kantata, Scoro) bundle more than agencies need at enterprise prices. The middle ground -- focused profitability tracking with smart defaults -- is underserved.

Key Features

Real-Time Project Financials

Live margin dashboard comparing budgeted vs. actual costs (labour, subcontractors, expenses) against project revenue
Burn-rate alerts when projects drift off-budget
Effective hourly rate visibility: revenue divided by hours delivered vs. quoted rate
Budget vs. actuals comparison with visual indicators

Resource Utilisation and Capacity

Billable vs. non-billable hour breakdown by person, role, team, client, and project
Capacity forecasting to identify over- and under-servicing
Visual heatmaps for spotting utilisation imbalances
Scenario planning to model staffing changes and their cost impact on margins

Overhead and Cost Allocation

Configurable allocation rules for shared costs (rent, software licences, management time)
Role-based cost modelling using fully loaded cost per role (salary + benefits + overhead share)
Subcontracting and vendor cost tracking alongside internal resources
Automated overhead engine that learns allocation patterns from historical data

Forecasting and Intelligence

Forward-looking utilisation and revenue models based on current pipeline and resource schedule
Margin forecasting from early execution signals (percentage complete vs. percentage budget burned)
Proposal risk scoring based on scope, team skill match, and historical estimation errors
Estimation accuracy feedback loop to systematically improve future estimates

Client and Service Line Analytics

Profitability segmentation by client size, complexity, and retention value
Service line margin targets with variance tracking and alerts
Client lifetime value correlated with profitability history
Role-level margin targets and accountability dashboards

AI-Native Advantage

Where incumbents show what happened, this project uses AI to surface what is about to happen and what to do about it. ML-powered margin forecasting detects emerging overruns weeks earlier than manual trend analysis. Automated overhead allocation learns from historical general-ledger data to calibrate burden rates without manual configuration. Proposal risk scoring evaluates new work before kickoff by matching scope, team skills, and delivery method against similar past projects -- replacing gut-feel estimation with data-driven confidence scores. Dynamic rate optimisation recommends billing rates based on demand, utilisation levels, and margin targets.

Tech Stack & Deployment

The project targets self-hosted and cloud deployment models. The only open-source incumbent with on-premise capability is Odoo (LGPL), which provides a project profitability module within its broader ERP but lacks the resource scheduling depth and AI-native features this project prioritises. Core integrations include accounting tools (QuickBooks, Xero), time tracking platforms (Harvest, Toggl), project management tools (Jira, Asana), and CRM systems (Salesforce). An API-first architecture enables custom integrations and extension.

Market Context

Well-run agencies target 15-25% net profit margins, yet many operate below this range due to poor visibility. The professional services automation market spans tools priced from $2.50/user/month (Resource Guru) to opaque enterprise pricing (Kantata, Kimble). Mid-market agencies -- the primary buyers -- are caught between lightweight time trackers that lack financial depth and enterprise PSA suites that impose minimum seat counts and require implementation consulting. The demand signal is strong: industry benchmarks from Iota Finance (2026), tMetric, and Deltek confirm sustained interest in profitability tooling across creative, marketing, and consulting verticals.

Project Status

This project is in the research and specification phase. Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.