AI automation development for business workflows
Connect the steps between receiving information and getting useful work done. We build custom automation for agencies and businesses that need to reduce repetitive processing, coordinate systems, and keep people involved in important decisions.
Start with a repeatable business task
Good starting points include collecting information from documents, organizing incoming requests, transferring records, and preparing recurring reports. We map the existing process, including exceptions and the person responsible for each decision. That makes it possible to separate predictable rules from tasks that may benefit from AI interpretation.
Design for the exceptions as well as the routine
An automation needs a defined response when data is missing, a connected tool is unavailable, or an AI result needs review. We scope validation, approval steps, and error handling alongside the successful workflow. The goal is a process your team can understand and operate, with clear boundaries around what the system can do.
Fit the workflow into your agency or business
A new automation may connect existing applications rather than replace them. We review the available APIs, data formats, access permissions, and handover needs before agreeing the architecture. Delivery can begin with one workflow and expand after the first version has been reviewed against representative examples.
Related project experience
Business Data & CSV Integration Pipeline
A FastAPI backend powering automated data transfers from Quickbase to Salesforce, with CSV asset storage in Azure and Wasabi. The project connects business-system workflows with cloud file storage through a custom integration layer.
View in portfolioAI-Enabled Wholesale Management App
An AI-enabled management application for a wholesale business. The project brings business management and AI functionality into a shared application designed around the wholesaler's operational workflow.
View in portfolioQuestions about this service
No. Rules, scheduled jobs, and conventional integrations may be sufficient. AI is considered where the task needs interpretation, such as working with varied text or documents, and its behavior can be evaluated.
The quote depends on the workflow, connected systems, volume, review requirements, and deployment environment. Hosting, API subscriptions, and model usage are considered separately from development where applicable.