ChatGPT Application Development
GPT-powered assistants grounded in your data and connected to your systems
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- OpenAI GPT models via the API
- Retrieval over your documents and data
- Function calling into your systems
Six kinds of GPT application
Different surfaces, one architecture: a model, your data, your APIs, and the engineering that keeps it accurate.
Customer support assistants
Internal copilots
Assistants inside your product
Custom GPTs and Actions
Conversational data access
Content and document workflows
What separates a demo from a production assistant
A prompt and an API key make a demo. These five parts make something you can put in front of customers.
- 01
Grounding
Retrieval over your documents, tickets and records, so answers come from your material and cite it.
- 02
Actions
Function calling into your APIs to look things up and do things, with confirmation where it matters.
- 03
Guardrails
Topic limits, structured outputs, refusal rules and escalation to a human.
- 04
Evaluation
A test set of real questions, re-run on every change, with accuracy targets agreed up front.
- 05
Operations
Logging, cost per conversation, latency, model routing and review queues.
Agents, custom models, other model providers and the data engineering underneath are covered on the AI development page.
The use cases that justify an assistant
An assistant is worth building when the same questions or the same drafting work arrive all day. These are the patterns we see most.
Repetitive support questions
Order status, policy questions, how-to guidance, answered from your own content with a clean handoff when needed.
Knowledge trapped in documents
Contracts, manuals, SOPs and past tickets that staff search for daily.
Drafting at volume
Replies, summaries, product descriptions and reports that follow a house style.
Onboarding and guided setup
Helping new users through a product without reading documentation.
From use case to a monitored assistant
Step 1: Use case and content audit
The questions the assistant must handle, the content it can answer from, and what happens when it should not answer.Scope and evaluation setStep 2: Grounded prototype
A working assistant over your real content, measured against the evaluation set.Measured prototypeStep 3: Actions and integration
Function calling into your systems, the interface (web, in-product, ChatGPT), and handoff to humans.Integrated assistantStep 4: Guardrails and launch
Topic limits, structured outputs, logging, cost controls and a staged rollout.Live with monitoringStep 5: Improve
Conversation review, evaluation updates, content gaps filled, cost tuned.Rising accuracy, falling cost
Clients on AI products built with SyncScrum
Published feedback from clients we have built AI content and transcription products with.
“I highly recommend Sync Scrum. They are diligent, reliable, and efficient, delivering high-quality results with proactive communication, making collaboration seamless for building AI products successfully.”
“I had the privilege of working with Sync Scrum on an AI transcription tool. Their rapid expertise, proactive mindset, and innovative proposals consistently impressed, reflecting deep understanding and initiative.”
ChatGPT application questions, answered
What is a ChatGPT application?
Software that uses OpenAI's GPT models through the API to hold conversations, answer from your content, draft text or take actions in your systems. It can be a customer-facing assistant, an internal copilot, a custom GPT inside ChatGPT, or an assistant embedded in your own product.
Is a ChatGPT app the same as a chatbot?
A traditional chatbot follows scripted decision trees. A GPT-based assistant understands free-form questions and can be grounded in your documents and connected to your systems. The engineering around it, retrieval, guardrails, evaluation and integration, is what separates a demo from something you can put in front of customers.
Can it answer only from our own content?
Yes. Using retrieval-augmented generation the assistant answers from your documents, help centre, tickets or database, cites sources, and declines when the answer is not in your material. This is how we build support and internal-knowledge assistants.
Can the assistant take actions, not just answer?
Yes. With function calling the assistant can look up an order, create a ticket, update a record or trigger a workflow through your APIs, with human confirmation where the action matters.
Which models do you use?
OpenAI's GPT models are the default for this service. Where a client prefers another provider or an open-weight model for cost, privacy or hosting reasons, our broader AI development service covers that; the architecture is largely the same.
How do you keep it safe and accurate?
Grounding in your data, structured outputs, guardrails on topics and actions, an evaluation set that is re-run on every change, logging, and human review queues for sensitive cases. Accuracy targets are set before launch.
Where does it run and what does it cost to operate?
It runs as a service alongside your application, on your cloud. Operating cost is mostly model usage; we reduce it with caching, model routing and token budgets, and we monitor cost per conversation from day one.
How does this relate to your AI development service?
ChatGPT application development is one specialism within SyncScrum's AI development work. The AI development page covers the wider picture: agents, retrieval systems, automation, custom models and the data engineering underneath.
Where this connects
The full AI engineering capability this service is part of: agents, RAG, automation, custom models.
Assistants built as features inside a multi-tenant product.
The smallest GPT-powered product worth launching to real users.
Tell us what people keep asking. We will show you what an assistant could answer, and what it should not.
A use-case call is free, and it usually ends with a clear yes, no or not-yet.
