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.
- Engagement
- 8–12 week build · ongoing model maintenance
- Built for
- Heads of growth · Product leads · Engineering teams
Web and app surfaces recommend the same items to everyone or worse, items chosen by manual merchandising. The data exists to do better — purchase history, browsing patterns, contextual signal — but the recommendation system that ties them together hasn't been built.
What this is
A personalization API engagement for businesses that want recommendations to actually move the metric. Three layers:
- Behavioral signal ingestion. Per-user click, purchase, dwell, return — fed into the recommendation backbone.
- Recommendation modeling. Neural collaborative filtering as backbone, gradient-boosted reranker for the final candidate list, contextual bandit for production exploration.
- API serving. Low-latency serve infrastructure with the API contract documented for the client's engineering team.
How it's built
PyTorch for the neural backbone, LightGBM for the reranker, contextual-bandit infrastructure (LinUCB or Thompson-class). Online serving via FastAPI with a Redis-class feature store. Subscription Economy Benchmarks integration where permitted — the integration is explicit and consented per engagement.
What you get
- The recommendation model trained on the client's data.
- The API contract and serving infrastructure.
- Documentation of the SubMagician integration (if used) including consent boundary.
- A/B test infrastructure for measuring lift.
- Quarterly model refresh and online-bandit monitoring.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 8–12 week build · ongoing model maintenance
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Operations Algorithms Suite
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.
- Same suite · Operations Algorithms Suite
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.
What buyers ask about this one.
How does the SubMagician data integration work?
Per-engagement, with explicit consent in scope. SubMagician users have consented to anonymized aggregate use of their subscription-pattern data; that aggregate becomes a contextual signal layer for the personalization model. For client engagements where this is permitted, the model sees not just 'this user clicked X' but 'users with this subscription footprint typically next-engage with Y.' Where the client's compliance posture excludes external data, the model uses client-data only.
What's the modeling stack?
Neural collaborative filtering as the baseline, gradient-boosted reranking for the final candidate list, contextual bandit for the exploration-exploitation tradeoff in production. Per-engagement, the stack adapts to the latency and scale requirements.
How do you handle cold-start users?
Cold-start uses the segment-level model from Customer Clustering & LTV Engine if it's deployed, otherwise falls back to a popularity-with-stratification baseline. Where the SubMagician signal is permitted, cold-start cases get a meaningful uplift from the subscription-footprint context.
What's the latency commitment?
Sub-100ms p99 at typical scale (single-digit-million users, single-digit-thousand RPS). At higher scale or for sub-50ms requirements, the engagement scopes the architectural changes required.
Pricing?
Scoped to traffic, model complexity, and integration depth. Discovery call covers all three.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026