#1 Solve
Solver-backed plan generation
A CP-SAT constraint model built on a real pharmaceutical production-planning problem generates the base plan — stages, batches, lines, and resources — instead of weeks of manual construction.
Pharmaceutical production planning
Plan with a solver.Replan with AI.Know what changed.
PlanningMachine builds the production plan with a real CP-SAT model, then lets AI-assisted replanning change it without losing what came before.
See it on your problem.
Tell us about your products, lines, and planning cycle. A demo is a guided session with the team on synthetic data, not a self-service trial.
Request a demo
- Send the form
- We reply to set a time
- A guided session on synthetic data
#2 Keep
Plans are durable business objects
Every published plan is a Planning Scenario with identity, evidence, validation, and lineage. Review, compare, rename, supersede, restore, or select one as the working baseline.
#3 Replan
Change without erasing the baseline
Exact edits follow a deterministic path; broader changes run through isolated solver runs. Both publish a new scenario with explicit provenance — the source plan is preserved, not overwritten.
#4 Inspect
See the impact before committing
Schedule, inventory, materials, maintenance, and validation views expose what a plan or replan changes across every planning dimension, side by side with the scenario it came from.
#5 Ask
Ask the plan questions
Chat workflows analyze scenarios, explain evidence, and prepare typed changes. Deterministic validation and human review keep publication authority — language never becomes a hidden control plane.
#6 Deploy
Built for pharmaceutical planning teams
English and Persian presentation with defined right-to-left behavior, and dedicated, isolated single-tenant deployment for each organization.