Technology · AI
Adds AI features such as chat assistants, document summaries and smart search to your product, built on OpenAI models with human review.
How we use it
We integrate OpenAI's APIs into products and internal tools, with evaluation, cost controls and a fallback plan built into every integration.
Official site: platform.openai.com (opens in new tab)
OpenAI offers its AI models as paid online services (APIs) that other software can call. We connect your product to them so it can answer questions, read documents, search by meaning or turn speech into text. For you, that means useful AI features without training a model from scratch, paid for by usage.
OpenAI's models are capable general-purpose tools for language, images and speech, offered through a well-documented API. That makes them a practical starting point for many AI features, but not a finished product. We ground answers in your data, validate outputs against schemas and keep a person in the loop for decisions that matter. Where data location or contract terms require it, the same models are available through Microsoft Azure.
Use cases
Answers from your help articles with citations and handover to staff.
Invoices, forms and purchase orders turned into data for review.
Embeddings that find products, documents or tickets by meaning.
Transcription for field teams and call summaries.
Structured outputs and function calling return data that matches a schema and can call your own APIs, so AI results plug into your systems.
One API handles typed questions, photos of documents and voice notes.
Embeddings power search and recommendations that understand what people mean, not just the words they type.
Privacy & security
Only the fields a task needs are sent, with identifiers masked where possible.
We check current API data-use and retention terms against your obligations.
Prompts and outputs are logged for review, with restricted access.
Plain-English glossary
Services
AI features and AI-first apps built on model APIs, retrieval over your own data, workflow automation or custom ML, with evaluation and human review.
Multi-tenant SaaS products with subscription billing, team roles, onboarding and usage analytics, from first MVP to paying customers.
Browser-based products, customer portals, dashboards and internal tools, built on clean data models with secure roles and integrations.
Solutions
Support and sales chatbots for web, app and WhatsApp that answer from your own content and hand off to your team.
Assistants that book, schedule, look up records and raise requests by voice or text, with confirmation before every action.
Features that generate catalogue copy, reports, summaries, translations and images, with brand controls and a review step before anything is published.
Data extraction from invoices, KYC documents, statements and forms into your systems, with validation rules and human review for uncertain fields.
Search and question-answering over your documents, tickets and product data, with hybrid retrieval, citations and permission-aware results.
Work
Illustrative projects that show how we plan and build products that use OpenAI APIs. They are samples, not client work.
AI solutions
An illustrative AI assistant that answers order, return and product questions from a D2C brand's own policies and order data, with sources and a human hand-off.
Runs on
Built with
Industries
Industries whose typical builds with us include OpenAI APIs.
FAQ
According to OpenAI's published policies, API data is not used to train models by default, and business customers have options around retention. Policies change, so we review the current terms at the start of each project and limit exposure by design, sending only the fields a task needs and masking identifiers.
Errors can't be eliminated, but design choices reduce them. The assistant answers from retrieved passages of your own content and cites them, is instructed to say when it doesn't know, and hands unclear or sensitive cases to a person. We build an evaluation set of real questions, measure answer quality before launch and re-run it whenever prompts or models change.
It depends on the model, how much text goes in and out of each request, and your volume. We estimate usage from realistic scenarios during discovery, then control cost with smaller models for simple steps, caching of repeated prompts, trimmed context and batch processing for non-urgent jobs. Usage dashboards and spending limits help avoid surprises after launch.
Next step
Describe the task and the data involved. We'll tell you honestly whether a language model is the right tool.
