PRISM is the tax-aware optimization core that scales to an entire personalized book — deterministically, auditably, with no per-seat solver license. Every figure below is measured on real US-equity data (March–April 2026), stated with its regime, with losing cases kept in.
Nobody runs an exact solver across a 100,000-account book before the open — it times out long before. PRISM does, deterministically, and stays fast at every universe size.
Personalized accounts grow faster than AUM. Throughput per core decides how many accounts an ops team can run, and whether the nightly batch clears on time.
As beta commoditizes, after-tax outcome is how direct-indexing and SMA desks compete. This is where PRISM is measured strongest — and where simpler approaches quietly leave money on the table.
| Approach | Tax budget captured · $5M, 192 names | Six-figure alpha at 5,000+ names? | After-tax win rate vs PRISM |
|---|---|---|---|
| PRISM | ~$238k (full budget) | Yes | — |
| Open-source solver baseline | ~$3k at scale | No | PRISM wins ~88% |
| Rules-based tax-loss harvesting | partial | No | PRISM wins ~95% |
Tax figures are measured on real-calibrated books; the robust real metric (tax captured) is measured directly. Your numbers are computed live on your data during the pilot.
A sophisticated buyer trusts the vendor who states their non-fit first. So here it is, both sides.
The same evidence behind the claims above, plotted straight from the recorded runs — factor-constrained QP on real US-equity universes, March–April 2026, p50 of five timed runs. Lower is better on solve time; speedup is versus Gurobi 13.0.1 on CPU.
| Solver | Runtime p50 (ms) | Gap vs Gurobi | Stability (CV) |
|---|---|---|---|
| PRISM (GPU) | 138.7 | < 0.00001% | 11.7% |
| Gurobi 13.0.1 (CPU) | 208.1 | reference | 19.2% |
| HiGHS (CPU) | 466.9 | < 0.00001% | 10.0% |
| Clarabel (CPU) | 768.6 | < 0.00001% | 12.0% |
| cuOpt (GPU) | 870.0 | 0.0014% | 8.0% |
| OSQP (CPU) | 11,311 | < 0.00001% | 10.0% |
Figure 3 · Structured QP at 5,000 real assets. PRISM is the fastest exact-quality solver in the field — 1.5× under Gurobi and 3.4× under HiGHS — while staying within a tight stability band. We keep the honest qualifier in: cuOpt posts a lower run-to-run CV, and Gurobi remains the correctness reference every other engine is measured against.
| Solver | QP Speed | Replay | Transition | Quality | Stability | Max Scale |
|---|---|---|---|---|---|---|
| PRISM (GPU) | 10 | 10 | 10 | 10 | 9 | 10 |
| Gurobi (CPU) | 7 | 6 | 3 | 10 | 7 | 8 |
| HiGHS (CPU) | 6 | 5 | 0 | 9 | 7 | 5 |
| cuOpt (GPU) | 5 | 2 | 1 | 2 | 6 | 5 |
| OSQP (CPU) | 2 | 2 | 1 | 3 | 5 | 4 |
| Clarabel (CPU) | 4 | 1 | 0 | 9 | 3 | 4 |
Figure 6 · Competitive overview — six solvers across six dimensions, scored 0–10 on real-data benchmarks (March 2026); higher is better. PRISM leads on speed, transition workflows, and scale; Gurobi and Clarabel match it on single-problem quality; cuOpt edges run-to-run stability. No solver wins every axis — which is the point of showing all six.
In this buyer set, trust is the product and compliance is the gate. PRISM ships the de-riskers up front.
Content-hashed, bit-reproducible outputs — re-derivable for any audit or exam date.
Wash-sale handling and lot-level tax accounting, validated by an internal test suite — 18 tests pass.
An exact-reference comparator ships so you can check PRISM against the exact optimum on your own data.
Every trade traces to explicit constraints and tax logic — no opaque ML in the trade path.
A benchmark is useful only when it tells you whether an engine can clear your workload with acceptable quality, latency, and reliability — not when it collapses to one headline number.
A 30-day, buyer-owned matched-workload pilot: PRISM vs your current stack, on your universe, constraints, costs, and tax rules — a pass/fail metric you set before we start. You get a full results pack (every account, losses shown), deterministic audit logs, and an ROI computed with your real numbers.
Request a matched-workload pilot →