AI consulting grounded in analytics and product engineering
Our team’s predictive analytics and text-mining experience predates today’s widespread use of generative AI. That experience matters because a useful AI system still depends on the question, the data, the method, and the decision someone will make from its output. A convincing demonstration is the beginning of an evaluation, not the end of the work.
Historical Nike work illustrates the depth of that practice. Product-focused text analysis connected cleat-related language, dependency words, sentiment, and source comments. It distinguished reports about physical durability from a problem placing a customized order. Those require different responses from product, service, and commerce teams; one overall sentiment score would obscure the distinction.
We also engineer our own products: SatelliteAI for search and content intelligence, HeySatellite for business data and knowledge workflows, and ODIN within that intelligence ecosystem. This gives our consulting work practical context around integrations, permissions, evaluation, and maintaining an application beyond its first demo. Product experience and historical client analysis are described separately in the linked work.
Choose the method that fits the business question
A revenue forecast, a source-backed document answer, and a campaign brief are different problems. Predictive models estimate defined outcomes from available observations. NLP helps examine language and its context. Generative AI produces or transforms content. Rules and conventional software remain useful when the task is explicit and repeatable.
We assess these options together. For example, a customer insight workflow might extract product themes from comments, reconcile them with service records, and present an analyst-reviewed findings brief. The extraction, statistical analysis, and explanation each need their own checks. Asking a language model to invent the entire analysis would not provide the same evidence.
Connect AI to enterprise data and existing systems
The integration often determines whether a pilot becomes useful daily work. We review source ownership, available APIs, freshness, record definitions, and the permissions of the people or tools using the result. A connected assistant should only retrieve the material its user is allowed to access, and an automated process should have a clearly bounded ability to change another system.
Our broader practice includes AEM, Oracle Eloqua, Adobe Analytics, GA4, HubSpot data, Salesforce connections, Databricks, AWS, SQL, and reporting platforms. The appropriate connection is scoped for each engagement. We can work alongside an existing engineering or marketing operations team and provide the data preparation, application layer, or workflow integration the project needs.
Evaluate a pilot against the work people actually do
We agree a baseline and acceptance criteria before selecting examples for a pilot. Depending on the application, evaluation can cover forecast error, retrieval relevance, factual support, task completion, review effort, response time, and operating cost. A representative test set includes ordinary requests and the ambiguous or incomplete inputs that users encounter in practice.
The result should support a decision: proceed, revise the scope, improve the data, or stop. A pilot can expose missing permissions or inconsistent records as readily as it demonstrates a model’s ability. We document these findings so the next investment decision is based on observed behavior rather than a polished presentation.
Plan for ownership after implementation
A deployed workflow needs an owner, a support process, and a way to assess changes to data, models, integrations, and instructions. We scope those responsibilities with your team. Documentation can include accepted use, approval rules, failure handling, evaluation examples, release checks, and the steps for investigating an incorrect result.
Commercial decisions follow the scope. Cost and timing depend on data access, integration complexity, evaluation requirements, the intended users, and the level of operational support. We can begin with a defined assessment or a focused implementation rather than asking an organization to commit to a broad transformation before the first use case has been examined.
