OpenAI integration
OpenAI Integration and Automation Services
OpenAI is most useful when the model is one step inside a real business process, not the process by itself. We connect it to the systems that hold your data, constrain what it is allowed to do, and keep a person in the loop where a wrong answer is expensive.
What we build
AI inside existing workflows
Classification, extraction, summarization and drafting inserted into the tools your team already uses instead of creating another standalone chatbot nobody opens.
Grounded assistants
Assistants that answer from approved company material, retrieve the relevant context first, and escalate when the answer is not supported by the source material.
Structured outputs for automation
Model responses shaped into predictable fields that downstream systems can validate before creating records, routing work, or triggering follow-up.
Human review at the risky step
The model prepares the answer or action, but financial, contractual, customer-facing, or otherwise high-cost decisions wait for an explicit approval.
Where it stops
Every tool has limits worth knowing before you build on it. These are the ones that change a decision.
- A model can produce a plausible answer that is wrong. Anything consequential needs grounding, validation, or a human review step rather than trust by default.
- Prompts are not a substitute for system design. If the source data is inconsistent or the workflow has no clear owner, adding a model makes the ambiguity faster rather than fixing it.
- Latency and usage cost rise with context size and call volume, so stuffing every available document into every request is usually both slower and more expensive than targeted retrieval.
- Outputs can vary even when inputs are similar. Workflows that require strict determinism should use ordinary code or rules for the deterministic part and reserve AI for interpretation.
OpenAI in production
Real engagements, anonymized—see the full write-ups in our case studies.
Worth reading first
How to build an AI agent that answers customer questions
A step-by-step method: what it may answer, where the facts come from, how it hands over to a person, and how to know it is right before you launch.
How to share credentials safely, by credential type
The safest handover moves no secret at all. Which access to grant, which secrets must travel, how to send those, and what to revoke afterwards.
How to build effective, safe, and failure-ready automations
Early validation, differentiated error handling, safe retries, and clear notifications so automations fail safely in production.
OpenAI FAQ
- Where does OpenAI create the most value in an automation?
- At the step where a person is currently reading unstructured information and turning it into a decision-ready format: classifying an email, extracting fields from a document, summarizing a call, drafting a reply, or deciding which queue should receive a request. The surrounding workflow should still be ordinary software so the model has a narrow, testable job.
- Can an OpenAI workflow take actions automatically?
- Yes, but the safest design separates interpretation from execution. The model can propose the action and return structured fields; the workflow validates those fields and decides whether to execute automatically, ask for approval, or escalate. That keeps a model mistake from becoming an irreversible business action.
- Do we need a chatbot to use OpenAI?
- No. Many of the highest-return uses are invisible to the end user: classifying inbound requests, extracting data, preparing reports, drafting internal summaries, and helping another automation understand free text. A chat interface is only useful when conversation is actually the best interface for the job.
Need help automating a process?
Tell us about the repetitive work on your team—we’ll map a practical custom automation plan.