Less busywork.
Smarter systems.
More time for customers.

Start with work that is repeated, slow, or difficult to scale: finding internal answers, reviewing documents, handling requests, or updating business systems. Alpstek assesses the workflow, available data, and expected benefit to identify a useful starting point. Sometimes a simpler integration or rules-based automation is the better choice.

We start by reviewing your existing applications, including CRM, ERP, document, and support systems. Connection options depend on available APIs, licenses, data quality, and access permissions. We identify these dependencies before proposing a solution, so you understand what can be reused and what needs to change.

An assistant typically helps someone find information, summarize content, or prepare a response. An agent can also use connected tools to carry out steps in a workflow. The distinction is about what it is allowed to do: actions, access limits, and human approval points should be defined for each use case.

Bring one business challenge, the tools involved, and an example of the result you want. A description of the current process is enough to begin; you do not need a finished AI strategy. Share any budget or timing priorities, and use anonymized examples rather than confidential records in the initial inquiry.

Data handling is part of the solution design: what information is needed, who can access it, where it is processed, and how long it is retained. Model-provider terms and training settings must be checked for the selected service. Privacy, security, and any industry requirements should be agreed before connecting live data.

AI can make mistakes, so validation needs representative tasks, expected answers, and tests for exceptions. Depending on the use case, controls can include source references, data checks, approval steps, and a handoff to your team. We define success measures such as time saved, correction rates, completion rates, and operating cost.

A focused pilot helps assess one workflow before a wider rollout. Scope, timing, and pricing depend on the data, integrations, and review requirements. The proposal should separate delivery fees from recurring model, hosting, and software costs, with clear checkpoints before committing to the next phase.

Deployment planning should cover documentation, team training, access ownership, and a process for handling issues. Monitoring and ongoing support are defined in the project agreement. As workflows, data, or AI models change, the solution may need new evaluations and updates to keep it useful.