We can quickly build a digital twin of your firm and test the hard decisions on it first — so you can find the moves that improve the outcomes you care about most, whether that's profit, margin, retention, or succession, before the strategy is real.
Your pricing, your leverage, your compensation, your practice mix, your clients, your partners — a few adaptive rounds isolate the best lever combination and the causal story, and Monte Carlo simulation1 shows how reliable the answer is.
If I give the engine my firm's actual numbers — not an archetype, my firm — which levers should I pull, and in what order, to move profit-per-partner with the least political cost?
You are the input. Not an archetype.
Six observable proxies — not abstract self-ratings — capture how you price, how steep your pyramid is, who owns the book, what you practice, how concentrated your clients are, how hard the partners will fight change.
Your numbers become a full simulation: twelve psychological profiles, a fifteen-step litigation workflow, every partner↔associate seam and its tacitness, the AI Profit Paradox.
Then instead of testing every lever combination blindly, the engine runs a few rounds, each informed by the last — try a lever, see what moved, use that to pick the next experiment.
That is what “adaptive” means here: learn as you go, not test everything — each round is chosen from the result of the last, so the search narrows toward what actually moves profit-per-partner. All of this runs on the fast deterministic engine; the winning combination is then replayed once on real LLM agents to see it in lifelike action.
Not “pull this.” The best lever combination, in order — plus why: which levers interact, so you know comp only helps after you've converted to AFA2, not just “comp helps” — and what each move costs you politically.
Two different jobs, working together.
Adaptive picks what to try. Instead of blindly testing all 32 lever combinations, the optimizer runs a few rounds — main effects → interactions → refinement — each one chosen from the result of the last. It narrows toward what actually moves profit.
Monte Carlo measures how much to trust it. The firm engine is stochastic — attrition, market shocks, political events — so every experiment is averaged across many seeds, and the final recommendation carries a confidence interval.
Adaptive finds the answer. Monte Carlo tells you how reliable it is.
The answer is found cheaply, then pressure-tested for real.
Two different engines, run one after the other — the reason you can trust the recommendation without paying to search with an LLM.
Searching with the LLM would mean hundreds of runs — thousands of dollars and hours.
The deterministic mock does that search for free; the LLM run then pays once to validate the winning combination on your firm's exact specs.
The magnitudes shift between the two engines — the deterministic mock gives the shape of the answer, the LLM run gives lifelike behavior at that setting.
PPP is the AmLaw scoreboard, but it isn't every firm's goal — and chasing it blindly can mean shrinking the partnership to raise the average. You choose the objective; the engine ranks the levers against that, and shows the trade-offs against the rest.
This is what one firm’s configuration file actually looks like — every field, in plain English. You hand the engine these numbers; it hands you back the levers to pull.
| Setting | Value | What it means |
|---|---|---|
| Pricing posture | hourly | Bills by the billable hour today (vs. partial or fully value-based / AFA). |
| Leverage ratio | 3.5 | 3.5 associates per partner — how steep the pyramid is. |
| Origination concentration | 0.4 | Fairly distributed book (0 = everyone originates, 1 = one rainmaker owns it all). |
| Practice mix (transactional) | 0.3 | Mostly litigation (0 = all litigation, 1 = all transactional). |
| Client concentration | 0.25 | Many clients, no single whale (0 = many, 1 = one client is most of revenue). |
| Partner power mix | 0.5 | Balanced governance (0 = cooperative, 1 = rainmakers hold veto power). |
| Baseline PPP | $2.1M | Today’s profit-per-partner — the starting point every lever moves from. |
| Baseline margin | 38% | Today’s matter-profit margin. |
| Tech maturity | 0.4 | Some knowledge-management infra, not fully codified (0 = none, 1 = fully codified). |
…and the levers the engine is allowed to try on that firm:
| Pricing | hourly → afa_native | Convert the billable hour to a fixed-fee (AFA) model. |
| Compensation (comp) | 0.3 → 0.9 | How hard partners are paid to adopt AI (0 = not at all, 1 = fully rewarded). |
| Seams | false → true | Codify the tacit partner↔associate handoffs where work gets lost. |
| Latency | 2 → 1 | Sprints between observing a result and acting on it — lower is faster. |
| Leverage | 3.5 → 5.0 | Reshape the pyramid — more associates per partner. |
Not “here are ten levers.” A written deliverable you can take into the partnership — your firm's numbers, the levers worth pulling and in what order, and the causal story behind them.
“Fix the seams first, then convert pricing to AFA. Don't start with comp — under your hourly billing, paying partners for AI raises adoption but costs profit.”
The adaptive optimizer's first round: pull each lever alone against the baseline and rank by Δ PPP. Later rounds take the top levers into a 2×2 factorial to find interactions, then refine toward the winning combination. The ± spread is the Monte Carlo confidence interval — what separates a reliable lever from noise.
| Lever | Δ PPP | ± spread | Read |
|---|---|---|---|
| Seams (codify tacit handoffs) | +$872k | ±$162k | the partner-judgment wall |
| Pricing (hourly → AFA) | +$567k | ±$39k | flips AI from cost to benefit |
| Leverage (flatten pyramid) | +$0 | ±$16k | moves hours, not PPP |
| Latency (faster loop) | −$23k | ±$16k | marginal |
| Comp (pay partners for AI) | −$55k | ±$0 | raises use, hurts PPP under hourly |
Feed a real firm's six numbers and the optimizer re-runs for your firm — that is the point. These numbers are the archetype proving the mechanism, not the answer.
Adoption is not the problem — pricing and comp are. The AI Profit Paradox: on the billable hour, AI is a net loss (fewer hours, less revenue). On fixed fees, the same AI is a net win. Same tool, same people, same adoption.
The right lever depends on your firm. The billable-hour trap is the most common problem — but it is not universal. What works for one firm is wasted effort for another.
your firm in · adaptive rounds, each smarter than the last · a causal story out