Software Optimization as a Service (SOaaS): Claude scans your codebase and workflows to identify bottlenecks, anti-patterns and inefficiencies, then generates optimised replacements with before/after benchmarks — improving performance, efficiency and code quality.
Identifies O(n²) or worse patterns and proposes O(n log n) or O(n) replacements with proof-of-correctness rationale and benchmarks.
Numba JIT Acceleration
For Python-heavy numerical workloads, Optimizer converts eligible loops to Numba @njit(parallel=True) kernels — our specialism from building pyvar.com.
Database Query Optimisation
Identifies N+1 queries, missing indices, full table scans and suboptimal join orders. Generates optimised SQL or ORM code with EXPLAIN plan comparisons.
Memory Profiling & Reduction
Identifies excessive object creation, memory leaks and inefficient data structures. Suggests generators, streaming patterns and columnar formats (PyArrow/Polars).
Parallel Processing Patterns
Converts sequential workloads to Ray actors or Dask arrays for multi-core or cluster parallelism — with the same pattern used in pyvar's Monte Carlo engine.
Before/After Benchmarks
Every generated optimisation ships with a pytest-benchmark report showing the measured speedup. You see the gain before committing a single line.
Typical results
Measured performance gains
Monte Carlo loop
96× faster
SQL N+1 query
80× faster
DataFrame join
60× faster
Matrix multiply
45× faster
API response time
–70% p99
Results vary by codebase. Benchmarks shown are illustrative of typical outcomes from our pilot clients.
What's included
Every optimisation, proven first
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A before/after benchmark report for every generated optimisation
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Optimised code delivered as a pull request against your own repository
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Coverage from algorithmic complexity down to database queries and infrastructure