ChatGPT Integration and OpenAI Development for Your Products and Tools
We build OpenAI models into the software you already run: your SaaS product, mobile app, CRM, ERP or internal tools. That means features such as drafting, summarising, extracting data from documents, searching your own knowledge and assistants that call your APIs, designed around your users and connected to your data, not a chat window bolted on the side.
built for
- SaaS companies adding AI features
- Product teams with an existing web or mobile app
- Operations teams buried in documents and email
- Companies rolling out ChatGPT to staff
- CTOs who need OpenAI behind their own login and data rules
Draft a reply to this ticket: Sorry about the duplicate charge. I’ve started a refund and will confirm by email. (illustrative demo response)
key facts
- OpenAI directly or Azure OpenAI in your chosen region
- API or Azure
- API keys and usage billed to your own OpenAI or Azure account
- Your account
- one-time project cost or a dedicated team
- Fixed or team
- signed before we see your data
- NDA
What we deliver
What you get with ChatGPT & OpenAI Integration. Nine parts of the service, each scoped with you.
Most of the work is not the API call. It is the prompts, the retrieval of the right context, structured outputs your code can trust, tests that catch a bad answer before a user does, and the cost and rate-limit controls that keep a feature affordable at scale. That is what we build and hand over.
If you need a customer-facing chatbot on your website or WhatsApp, possibly on a mix of models, see our AI chatbot development service. This page is about putting OpenAI capabilities inside your own products and workflows, through the OpenAI API directly or Azure OpenAI when your data must stay in a particular region.
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GPT features inside your product
Draft, rewrite, summarise, translate and classify inside your app's own screens, with prompts versioned in code and outputs your interface can display safely.
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Structured data extraction
Turn emails, PDFs, forms and scanned documents into JSON that matches your schema, with validation, and send low-confidence results to a person for review.
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Search and answers over your content
Embeddings and a vector index over your documents, help centre or database (RAG), so answers quote your own sources and link back to them.
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Assistants that call your APIs
Function calling lets the model look up an order, create a ticket or update a record through your own APIs, with permissions checked by your backend, not the model.
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Internal assistants for staff
Assistants for HR, IT, sales and operations inside Slack, Teams or your intranet, answering from SOPs and policies, with sign-in through your existing identity provider.
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Custom GPTs for ChatGPT workspaces
For teams already on ChatGPT's business plans, we build custom GPTs with instructions, knowledge files and actions that connect to your systems.
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Speech and image understanding
Transcribe calls and meetings, read screenshots, photos and diagrams, and generate speech where the use case needs it, using OpenAI's audio and vision models.
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Evaluation and guardrails
A test set of real inputs and expected answers runs on every prompt or model change, alongside moderation, input checks and limits on what the model may do.
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Cost, speed and usage monitoring
Caching, routing between larger and smaller models, token budgets and dashboards for usage, errors and spend per feature or per customer.
Why it matters
The difference it makes to your business. Results your team will notice.
ChatGPT & OpenAI Integration is planned around these outcomes, measured against a baseline we agree with you up front.
Discuss your project- 01
AI where your users already work
Features sit in the screens and workflows people use every day, rather than in a separate tool they have to remember to open.
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Answers grounded in your data
Retrieval and citations tie responses to your own documents and records, which makes them easier to check and to trust.
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Running costs you can plan for
Token budgets, caching and the right model for each task keep the monthly API bill predictable as usage grows.
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Code and prompts you own
Prompts, evaluation sets, integration code and documentation are handed over, running on your own OpenAI or Azure account.
How we work
How we deliver ChatGPT & OpenAI Integration
A clear five-step process, so you always know what happens next and what you will get.
Every project starts with a free, no-obligation consultation.
$ appsinai run --pilot-first
Step 1: Pick the use case
We look at your product and workflows and choose one or two features where an OpenAI model saves time or adds something users value.
Step 2: Prove it on real examples
A quick prototype runs on a sample of your real documents or questions, so we can see quality and cost per request before committing to a build.
- gatego / no-go on the results before “Build and integrate”
Step 3: Build and integrate
We build the backend service, prompts, retrieval and API connections, and wire the feature into your app, CRM or internal tools.
Step 4: Test against an evaluation set
Outputs are scored against an agreed set of examples, and edge cases, misuse and failure handling are tested before release.
Step 5: Launch and tune
We release to a small group first, watch usage, cost and feedback, then roll out and keep prompts and models current as OpenAI ships new versions.
↺ results feed the next improvement cycle$
Security & control
What happens to your data when it goes to OpenAI
Before anything is sent to a model, we agree with you which data leaves your systems, where it is processed and how long it is kept.
Ask us about security- 01
Training and retention
By default, OpenAI does not train its models on data sent through its API. We check the current terms and retention settings for your account before go-live.
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Regional hosting
Where data must stay in a region, we use Azure OpenAI deployments in that region and keep your vector index and logs in the same place.
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Minimise and mask
Personal and sensitive fields are removed or masked before a prompt is built, so the model only sees what the task needs.
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Access follows your rules
Retrieval respects each user's permissions, so an assistant never answers from a document the person asking could not open. API keys stay on the server, never in an app.
FAQ
Questions about ChatGPT & OpenAI Integration
Straight answers to what buyers ask us most. Can't find what you need?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyWhat is the difference between ChatGPT integration and an AI chatbot?
ChatGPT integration puts OpenAI models inside your own product or internal tools: a summarise button, document extraction, search over your help centre, or an assistant that updates records through your API. An AI chatbot is a customer-facing conversation on your website, WhatsApp or app, often on a mix of models and with handover to a person. We build both.
See AI chatbot developmentWhich OpenAI models do you use?
We choose per task: a larger GPT model where reasoning quality matters, a smaller and cheaper one for classification and extraction, embedding models for search, and speech or vision models where needed. Models sit behind one service layer, so they can be swapped as OpenAI releases new versions. If another provider suits a task better, we will tell you.
Do you fine-tune GPT models?
Sometimes, but rarely first. Most projects reach the quality they need with good prompts, retrieval over your own data and structured outputs. Fine-tuning helps when you need a consistent format or style across many cases and have enough good examples to train on.
RAG vs fine-tuningHow much does a ChatGPT integration cost?
It depends on the number of features, how many systems we connect to, how much content needs indexing, your security and compliance needs and how much testing the use case requires. OpenAI usage is billed separately to your own account, and we estimate it per request during the prototype.
Estimate your AI project costHow long does it take?
A simple feature can be prototyped within days. Taking one feature, such as summaries or document extraction, to production with tests usually takes a few weeks. An internal assistant with retrieval over several sources and API actions takes longer. You get a timeline after a scoping call.
Is our data safe if it is sent to OpenAI?
Data sent through the OpenAI API is not used to train OpenAI's models by default, and we confirm the retention terms for your account. We send only the fields a task needs, mask personal data where possible and use Azure OpenAI in your chosen region when data residency matters.
Who owns the code and prompts?
You do. On completion and final payment, the integration code, prompts, evaluation sets and documentation we build for you are handed over, as set out in the contract. OpenAI's models stay under OpenAI's terms, and everything runs on your own OpenAI or Azure account, so you are not locked in to us.
Free PDF brochure
ChatGPT & OpenAI Integration brochure
Features, outcomes, delivery process and FAQs in one PDF you can share with your team or management.
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related
Keep exploring
- AI Chatbot DevelopmentCustomer-facing chatbots on your website, WhatsApp and app, on GPT, Claude, Gemini or Llama, with handover to your team.
- LLM DevelopmentCustom LLM applications on open or commercial models, including self-hosted options when data cannot leave your network.
- RAG vs fine-tuningWhen to ground a model in your documents and when to train it on your examples, and what each costs to run.
- AI & ML DevelopmentEnd-to-end AI and machine learning development, from a feasibility check and proof of concept to models running inside your ERP, CRM or apps.
- Generative AI DevelopmentGenerative AI applications that draft, summarise, search and answer from your own content, built on the model that fits and tested before rollout.
- AI Agent DevelopmentAI agents that read, decide and act across your systems, completing multi-step tasks with human approval where it matters and a log of every step.
- Model Training & Fine-TuningWe prepare your data and fine-tune or train AI models so they follow your terminology, formats and rules, with evaluation before and after every change.
- Machine Learning DevelopmentCustom machine learning models for forecasting, risk, recommendations and inspection, trained on your own data and evaluated before they reach production.
- Computer VisionCustom computer vision models that detect, count, read and inspect what your cameras see, trained on your own images and run in the cloud or on edge devices.
Further reading
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Tell us which feature or workflow you have in mind and we will show you how it would work on your own data, with an estimate of build and running costs.