We build machine learning models, AI agents and integration work that solve specific business problems rather than chase the latest demo. The thread that runs through every engagement: a data foundation that's clean enough for the model to be trustworthy.
Models that score every customer or account on retention risk and lifetime value, refreshed on a cadence the business can act on. Outputs feed directly into customer success and marketing workflows so the team prioritizes outreach where it matters most rather than working a flat list. Fewer surprise churn events, more proactive saves and marketing dollars concentrated on the customers worth keeping.
Score the likelihood that an account or contact will convert, upgrade or respond, then push the score into the systems sales and marketing already use. We tune the model to the action you actually want to drive (book a meeting, accept a renewal, expand a seat) so the output is operationally useful, not just statistically interesting. A sales motion that runs against a ranked list every morning, and a marketing function that targets people likely to act.
Time-series and ensemble models for revenue forecasting, demand planning and capacity scheduling. We pick the right approach for the data shape (Prophet, ARIMA, gradient-boosted trees or hybrid statistical-LLM methods), then operationalize it on a cadence finance and ops can plan against. The result is a forecast that explains its own assumptions, surfaces confidence intervals leadership can interpret and updates automatically as new data lands.
Personalized recommendation engines for ecommerce, content and product surfaces. Collaborative filtering, content-based or hybrid approaches tuned to your catalog and user base, with the evaluation framework to tell whether the model actually moves a business metric. The result is meaningful lift in conversion, AOV or engagement on the surfaces where recommendations live, not a model that's technically interesting but operationally invisible.
Purpose-built agents and autonomous workflows for marketing, ops, sales and service tasks that have clear inputs and clear success criteria. We ground them in your data, your taxonomy and your decision rules, with custom prompts, retrieval layers, evaluation frameworks and human-in-the-loop checkpoints where the cost of being wrong is high. Teams get hours back each week from tasks that no longer need a human in the loop, and a specific operational capability that compounds as the data and feedback loop mature.
Retrieval-augmented generation systems that ground LLM outputs in your documentation, contracts, customer records and operational knowledge. We design the retrieval layer (vector, hybrid or graph), tune chunking and embedding strategies for your content and build the evaluation harness that catches hallucinations before they reach a customer. The result is an AI layer that answers from your business's actual knowledge instead of generic training data.
Connect AI directly to your CRM, warehouse and operational tools using Model Context Protocol and the integration patterns stabilizing around it. The model meets your data where it lives, with current-state context and the right access controls, rather than working from a stale snapshot or a copy in the prompt. An AI layer that gives accurate answers about the business as it is right now, not as it was last quarter.
Thirty minutes with a 829 Analytics partner. You leave with a prioritized view of what to build first, what's worth waiting on, and the business metric anchoring each move. Whether or not we end up working together.