Now accepting early access

Your Gurobi models,
5x faster.
Automatically.

Optimaze analyzes your solver logs and commits targeted code improvements — big-M tightening, symmetry breaking, parameter tuning — directly to your repo as a PR.

Have a Gurobi model to test? We're taking early repos now.

Get started

Two commands. That's it.

bash
$ pip install optimaze
$ optimaze optimize model.py --repo https://github.com/you/repo --key YOUR_KEY

Analyzing solver logs...
Detected big-M with M=1e8 in constraint "capacity"
Tightened to M=2,450 (problem-specific bound)
PR #14 opened on GitHub — 37% speedup
Run Model
Analyze Logs
AI Suggests Fix
PR on GitHub

↻ Repeat until no more improvements

00%
Typical speedup
00x
Best case improvement
0
Improvement types
0
Bottleneck rules
Capabilities

What it tries

Optimaze applies targeted reformulation techniques based on your model's specific bottlenecks.

Big-M Tightening

Replace oversized M values with tight, problem-specific bounds. Dramatically strengthens LP relaxation.

Symmetry Breaking

Add ordering constraints on interchangeable variables. Prunes equivalent branch-and-bound subtrees.

Valid Inequalities

Add cutting planes that tighten the LP without cutting the optimum. Reduces branch tree size.

Branching Priorities

Guide branch-and-bound by prioritizing key decision variables. Can cut node count by 50%+.

Warm Starting

Provide initial feasible solutions from greedy heuristics. Enables aggressive pruning from iteration 1.

Smart Parameter Tuning

Automatically set MIPFocus, Cuts, Presolve, Heuristics, Symmetry, and Method based on your model's profile.

Lazy Constraints

Mark rarely-binding constraints as lazy. Reduces LP size during search for faster exploration.

Ready to speed up your models?

Have a Gurobi model to test? We are looking for early MVPs to analyze and tune directly in your repo.

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