Operations Algorithms Suite
Ten productized services for established businesses with real operational data — customer intelligence, commercial optimization, classical operations research. Math-anchored, ML-aware, decision-output.
This is the broadest suite in the catalogue — ten products across three tiers, applied to established businesses with real operational data. The thesis: most operating problems have well-understood mathematical structure, and the path from data to better decisions runs through that structure faster than through pure ML.
Customer intelligence is the ML half — clustering, churn, recommendation. Commercial optimization is the ML/forecasting half — demand, pricing. Operations research is the classical half — linear programming, transport problems, scheduling. We're deliberately attached to the classical algorithms in the third tier; they're explainable, auditable, and often outperform ML for well-defined optimization problems.
Across all three tiers, the bar is the same: deployable models, not academic outputs. Decision-output, not dashboard.
Customer intelligence
Clustering, churn, personalization, benchmarks. The customer-data half — what the business knows about who buys, how often, and what's next.
- I·01Flagship
Customer Clustering & LTV Engine
An unsupervised clustering layer over the client's own customer-and-transaction data — surfaces behavioral segments using RFM and broader signal sets, attaches a lifetime-value forecast per segment, feeds marketing and retention decisions.
8–12 week build · quarterly model refresh - I·02
Churn Prediction & Retention Engine
A classification layer per customer — churn probability with confidence bands, risk-driver attribution (why the model thinks this customer is at risk), and retention-action recommendations tied to the predicted risk drivers.
6–10 week build · ongoing operation - I·03
Real-Time Personalization API
An API that returns the next-best-product, next-best-content, or next-best-offer per user — fed by the client's own behavior data, augmented (with consent) by Subscription Economy Benchmarks contextual signal where the use case warrants.
8–12 week build · ongoing model maintenance - I·04
Subscription Economy Benchmarks API
An API and dashboard with anonymized aggregate subscription benchmarks across categories — sourced from SubMagician's consumer base, presented as aggregate cohort metrics with explicit privacy boundaries and use restrictions.
API subscription · monthly benchmark refresh
Commercial optimization
Demand, inventory, pricing. The revenue half — what to stock, what to charge, when to promote.
- II·01
Demand Forecasting & Inventory Engine
A forecasting layer at SKU grain — combines time-series baselines with external signal (macro indicators, social-and-search signal, weather, consumer-behavior priors) — feeds inventory and replenishment decisions with documented uncertainty.
10–16 week build · ongoing forecast cadence - II·02
Dynamic Pricing & Promotion Engine
An elasticity-and-promotion model — per-segment price elasticity (or discount-elasticity for negotiated B2B), promotion-cannibalization modeling, recommended timing and depth that maximizes net revenue.
8–12 week build per category · ongoing iteration
Operations research
Classical OR applied to real operating problems — routing, scheduling, production planning, cost optimization. Deterministic, explainable, auditable.
- III·01
Logistics & Routing Optimizer
A routing-and-distribution optimization engine — solves vehicle routing problem (VRP) variants for the business's fleet and constraints, produces daily route assignments with documented cost reduction vs. the prior method.
8–14 week build · ongoing operation - III·02
Workforce Scheduling Engine
A workforce-scheduling optimizer that produces shift assignments against the business's constraints (skills, availability, labor law, demand forecasts) — runs weekly or daily depending on the cadence, with manager-side UI for review and override.
8–12 week build · ongoing operation - III·03
Production & Resource Planner
A linear-programming model for the business's production-planning problem — what to make, how much, in what mix, allocated to which demand. Run weekly or per planning cycle, with documented improvement vs. the prior method.
10–16 week build · ongoing operation - III·04
Enterprise SaaS Spend Optimization
An audit-plus-optimization engagement that maps the company's SaaS stack against actual usage, identifies redundant vendors and over-provisioned licenses, models the consolidation scenarios, recommends the contract renegotiations that would maximize savings.
4–8 week audit · optional ongoing optimization
How a operations algorithms suite engagement runs.
Each product has its own engagement shape — typical length, what you get, who staffs it. Across the suite, the constants are the same.
- Lead
- Bogdan
- Cadence
- Async-first
- Engagement
- Per product
- Bar
- Production
Principal engineer. Most architecture and most code ships through one keyboard.
Weekly check-ins, written updates between, calls when the decision needs the room.
Shapes listed on each product page — typically multi-week builds with ongoing operation.
Async correctness, capacity under burst, observability at every boundary.
If this fits the operation, the next step is one call.
We'll talk scope and fit. If we're not the right fit, you'll know fast.
Bogdan and team · async-first · OP—2026