We help Wellness & Performance teams put predictive models into production. Predictive LTV and churn models that prioritize retention spend and shape the subscription flows.
Restriction is the design input here, not the surprise.
Predictive LTV and churn models that prioritize retention spend and shape the subscription flows.
Assume restriction as the starting condition, then build the engine that does not depend on ad approval. Lifecycle carries the revenue when paid is constrained. Predictive LTV and churn models decide where retention spend goes, and the subscription flows are built against the prediction rather than a flat rule.
A model that lives in a slide deck has never changed a single decision. Artificial intelligence only counts when it runs in production, changes what your team does next, and shows up in revenue you can measure against a clear baseline. Everything else is expensive theater.
Scoring, churn, LTV, segments, and recommendations built to convert. We train models on your closed-won history and customer behavior: lead scoring, churn propensity, lifetime value, and demand forecasting, plus catalog-trained recommendations that raise order value and repeat purchase. Scores retrain as your funnel shifts and land in your CRM views.
Your own language models, grounded in your data, running in your cloud. We deploy internal LLMs and retrieval pipelines over your documents, knowledge base, and warehouse. Your data stays inside your environment, behind your access controls, powering search, drafting, and answers your team can trust.
Sentiment, transcription, and classification across text, media, and calls. We build pipelines that classify tickets, reviews, calls, and leads, score sentiment, and route what matters to the right owner. Speech-to-text, video understanding, and image analysis turn media libraries and feedback into searchable, reportable data you can act against.
If it is a data problem attached to revenue, we have built it. Fraud detection, pricing and promotion optimization, anomaly detection: we scope the use case and backtest it against your own history. We tell you before you pay whether the data can carry a model that beats what you do today.
We inventory your data, systems, and current decision-making, then rank candidate AI use cases by revenue at stake and data readiness, whether that is a predictive model, an internal LLM, or a language or vision pipeline. You end the month with a scored roadmap, a named first build, and an honest read on any foundation work it needs. We commit to the highest-value problem before writing a line of code.
We assemble clean training and grounding data, build the first solution, and validate it against your own history and real workloads to prove it beats your current approach. You see the expected lift and the baseline it cleared before anything goes near production. If the data cannot support the build, we tell you immediately and adjust the plan.
The first build deploys into your stack, and its output flows into the CRM, ad platforms, or internal tools where decisions get made. A clear baseline goes live so we can measure real incremental impact. You finish the quarter with AI in production, a first read on impact, and the next build already scoped.
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