I build AI workflows that connect your data to a useful action inside your existing software. That can mean extracting information from documents, retrieving answers from approved knowledge, normalizing records, or building an agent that calls your APIs with defined permissions and review steps.

What’s included • Workflow mapping: define input sources, required outputs, access permissions, approval steps, and the tasks worth automating. • Data and knowledge setup: connect documents, business records, or approved knowledge sources and prepare them for the workflow. • AI processing and integrations: build the extraction, retrieval, classification, or agent logic and connect it to your APIs, CRM, email, calendar, or other systems. • Control and failure handling: validate outputs, set human review points, handle retries and timeouts, and record workflow status so failures can be investigated. • Testing and delivery: check representative inputs and edge cases, deploy the agreed workflow, and provide source code, configuration notes, and a handover walkthrough.

How I work I start with one measurable workflow and agree the acceptance criteria before building. We identify where AI is useful and where a rule or human approval is the better fit. I lead technical delivery, with a team and development resources available when the scope needs additional support.

Delivery estimate A focused automation typically takes 2 to 4 weeks. Workflows involving multiple systems, voice integrations, or substantial data preparation are estimated by milestone. The quote defines the implementation scope; usage and third-party service costs are agreed separately.

Relevant project experience GridIronGC uses OpenAI-assisted data normalization within a player-conversion pipeline. OriginSustain includes OpenAI API and RAG support within its reporting workflow. Our team’s EaseLearn delivery includes AI-assisted learning tools. These examples show AI as part of a product workflow, with application logic and integrations around it.

To start, share one workflow, sample inputs, the expected output, and the systems it needs to connect to. We will agree how to check correctness, handle failures, and measure the result before expanding the scope.