Forecasts from real data. Not developer estimates.
Spaces projects delivery dates and costs from actual execution data — not story points, not sprint velocity, not gut feel. You get a projection the moment you build a plan. As tasks complete, real data replaces initial estimates and the forecast updates automatically.
Projections that learn from your team
Your first projection comes from plan structure and model pricing. As tasks complete, real execution data — duration, cost, iterations, agent, model — replaces those defaults. The more your team ships, the more grounded the forecast becomes.
Iteration-cycle tracking
Spaces logs every implement → review → fix cycle per task. Over time, this reveals which categories of work need more rounds and which converge quickly — shaping future projections.
Per-agent profiles
Different agents perform differently. The system tracks which agents are fast at which task types — and what they cost — so projections reflect your actual team.
Cost calibration
Initial estimates use published model pricing. As real token usage accumulates per task type, projected costs shift from list-price math to empirical patterns.
Task-type segmentation
Not all tasks are equal. The system learns relative complexity — which categories take longer, which models are more efficient for which work — from your own completion history.
As more tasks complete, the prediction interval narrows and confidence increases
A forecast from day one that updates itself.
There are no story points for AI-driven work. No sprint velocity to extrapolate from. The first time a stakeholder asks "when will this be done?", most teams guess. Spaces gives you a projection the moment you create a plan — grounded in task structure, dependencies, and model pricing — that recalculates every time a task completes.
Forecast
The moment you build a plan, Spaces generates a delivery projection — timeline and cost — from the dependency graph, task types, and model pricing. Before any work begins, you have an answer.
Record
As tasks complete, Spaces captures actual duration, LLM token spend, iteration count, agent ID, and model used. Zero manual input — the data comes from execution itself.
Recalculate
Every completed task triggers an updated projection. Real data replaces initial estimates — best, expected, and worst case — grounded in what your team actually delivered, not what anyone guessed.
Timeline and cost, together
Spaces forecasts both timeline and cost together — because shipping on time but 3x over budget isn't a win.
Timeline
When will this plan finish? Best, expected, and worst-case completion dates — recalculated on every task completion, aware of your dependency graph and critical path.
Cost
What will the remaining work cost? Per-task projected LLM spend based on real token usage, broken down by model — updated as actuals come in.