The OpenAI agency.GPT live in your stack.
A demo in the Playground isn't a product your users can touch. We integrate the OpenAI API into your app and backend, build RAG assistants grounded in your content, ship agents that run your workflows through n8n and Make, and set the cost and governance controls.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
AttioAttio Implementation Partner
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude CodeClaude CoworkClaude Design
Clay
Clickup
Cursor
DeepSeek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Folk Implementation Partner
Freepik Spaces
Gamma
GeminiAn OpenAI agency ships GPT into production, not just a demo.
Anyone can paste a prompt in the Playground. Wiring the OpenAI API into your product, grounding it in your data, and keeping the bill under control is a different job. Here are the four things we own.
- API integration
GPT wired into your product and stack
A demo in the Playground isn't a product. We integrate the OpenAI API into your app and backend: the Responses API, function calling, structured outputs (JSON Schema), streaming and the tools your models actually need. We handle auth, rate limits, retries, fallbacks and token tracking, so GPT runs in production behind your UX instead of in a notebook nobody ships.
See a typical build - RAG & assistants
Assistants that answer on your own data
Generic GPT answers generic questions. We build RAG assistants grounded in your real content: embeddings, a vector store, retrieval, and prompts tuned to your domain, so answers cite your docs, your tickets, your product. Support copilots, internal Q&A, analytics assistants, content and email drafting. Each one scoped, evaluated, and built so it says 'I don't know' instead of inventing.
See the method - Agents & automation
Agents that run your real workflows
The leverage isn't a chatbot, it's GPT doing a task end to end. We build agents that call your tools and APIs, then wire them into your stack through n8n and Make so they run on triggers, not on someone remembering to paste a prompt. Lead enrichment, ticket triage, document extraction, content pipelines. Scoped, permissioned, with a human in the loop where the call matters.
See the integrations - Cost & governance
GPT under control, not a runaway bill
Token bills surprise teams that ship first and govern later. We pick the right model per task (the cheaper model where it's enough, the reasoning model where it earns it), cache and batch where it helps, set budgets and logging, and keep your data handling clean. We're model-agnostic: if Claude or another model fits your case better, we'll tell you, because we're an AI and automation agency first.
See AI enablement
We build with OpenAI like product engineers, not prompt jockeys.
Most OpenAI projects die the same way: a slick Playground demo, no evals, no error handling, a token bill nobody budgeted, and it never reaches a real user. So we treat it like product: integrated into your app, grounded in your data, measured on your own cases, and governed on cost and data from the first commit.
- Audit · map your product, your data, and where GPT actually moves a metric
- Build · API integration, RAG and agents, scoped and evaluated on your cases
- Integrate · wire it into your stack via n8n and Make so it runs on triggers
- Govern · model choice, cost controls, logging and data handling, by default
We build with the OpenAI API every day.
We don't sell a partner tier. We ship products on the OpenAI API, including this stack, so we set it up the way it actually works in production: structured outputs, evals, retries and fallbacks, and a model picked per task. That's exactly what's missing when an OpenAI project ends at a Playground screenshot.
- We build with the OpenAI API every day, so we set it up the way it ships, not the way a demo suggests: structured outputs, evals, retries and fallbacks.
- Model-agnostic and honest: if Claude or another model fits your case better on quality or cost, we say so instead of forcing GPT to win.
- Governance is not an afterthought: model choice, budgets, logging and clean data handling are wired from day one, not bolted on after the bill spikes.
- You leave autonomous: the integration lives in your codebase and your n8n flows, so your team owns it without a retainer to keep the lights on.
The OpenAI API at the core, your product stack around it.
We build the parts that turn a model into reliable product behaviour, then connect them to how your business already runs. Here's what a real integration covers.
- Setup
OpenAI API integration
We wire the OpenAI API into your product: the Responses API, chat completions, streaming, function calling and structured outputs, with auth, rate limits, retries and fallbacks handled so it survives real traffic.
- Setup
RAG & embeddings
We build retrieval-augmented generation on your data: embeddings, a vector store, chunking and retrieval tuned so GPT answers from your docs and cites sources, instead of guessing from its training data.
- Setup
Agents & tool use
We build agents that call your tools and APIs through function calling, scoped to a task with the permissions they need and a review step where the decision matters, so they act rather than just chat.
- Setup
n8n & Make automation
We connect GPT to your stack through n8n and Make so it runs on real triggers: a new lead, an inbound ticket, an uploaded file. The model does the work inside a flow you can see and audit.
- Setup
Structured outputs & evals
We use JSON Schema structured outputs so responses are typed and parseable, and we set up evals so you can measure output quality on your own cases before and after every prompt or model change.
- Setup
Cost, logging & governance
We pick the right model per task, cache and batch where it helps, set budgets, log every call, and keep data handling clean (including the DPA and data-retention questions) so the bill and the risk stay predictable.
We map where GPT pays off, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your product, your data and where the OpenAI API would actually move a metric. You leave with an honest read on what GPT fixes, what to build first, and which model fits. Zero pitch, just an engineer's take on your use case.
- An honest read on where the OpenAI API helps your product
- The first use case worth building, and the model for it
- The cost and data controls to wire in from the start
- A frank take on what it won't fix
How we run an OpenAI integration.
Five steps, in order. We don't ship before evals pass on your own cases, we don't skip cost and data governance, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Use-case audit
Map where GPT actually moves a metric
We sit down with you and look at the real opportunities: support volume, repetitive drafting, document extraction, internal search that nobody can do fast. We check your product, your data and your stack. Half the value is telling you where the OpenAI API helps and where a simpler tool or another model does the job, so you don't ship GPT against a problem it won't fix.
- Step 2 · Build the integration
Build it so it survives real traffic
We integrate the OpenAI API into your product: the Responses API, function calling, structured outputs and streaming, with auth, rate limits, retries and fallbacks handled. For knowledge work we build RAG on your data with embeddings and a vector store. Everything is scoped, typed and built to fail gracefully, not to look good in a one-off demo.
- Step 3 · Evaluate & tune
Measure output quality on your own cases
We don't ship on vibes. We set up evals on your real inputs so we can measure whether the assistant or agent actually answers correctly, then tune the prompts, the retrieval and the model choice against that. You see the before and after on your cases, not a benchmark score from a press release, before anything goes near production.
- Step 4 · Integrate & automate
Wire it into your stack so it runs on triggers
We connect GPT to where the work happens: your app, your CRM, your support tool, your data, through n8n and Make so it fires on a new lead, an inbound ticket or an uploaded file. The model does its part inside a flow you can see, log and audit. A human stays in the loop wherever the decision carries real weight.
- Step 5 · Govern & hand over
Keep cost and data under control, then hand it over
We pick the right model per task, set budgets and logging, and keep your data handling clean so the bill and the risk stay predictable. The integration lives in your codebase and your n8n flows, so your team owns it. If you want to go deeper, our training covers the OpenAI API and agents end to end. If you want us on call for what scales next, we talk about that separately.
We're judged on what ships and runs.
No partner badge to display, so we lead with what matters: feedback from the teams whose OpenAI integration we built, and whether GPT kept doing real work after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The integration lives in your codebase and n8n flows, owned by your team
- Cost and data controls wired before anything reaches a user
- Assistants and agents evaluated on your own cases, not a demo
- Trustpilot reviews come from the teams we built it for
The questions we get asked on repeat.
What does an OpenAI agency actually do?
An OpenAI agency builds GPT into your product and stack so it ships, instead of leaving you with a Playground demo nobody can put in front of users. We integrate the OpenAI API (Responses API, function calling, structured outputs), build RAG assistants grounded in your data, build agents that run your workflows, wire it into your stack through n8n and Make, and set the cost and governance controls. The point is GPT doing real work in production, not a chatbot a few people try once.Should we use the OpenAI API or just ChatGPT?
ChatGPT is a product your team uses in a browser. The OpenAI API is how you build GPT into your own product, automate workflows, and ground answers in your data. If you want a colleague-style assistant, ChatGPT (or ChatGPT Enterprise) is often enough and we'll say so. If you want GPT inside your app, your CRM or an automated flow, that's the API, and that's what we build: function calling, embeddings, structured outputs, the parts a chat window can't give you.How much does an OpenAI integration cost?
It depends on scope: an API integration is nothing like building a RAG assistant on your data and wiring agents into your stack. We don't throw out a flat package. We start with a free 60-minute audit to find where the OpenAI API actually moves a metric for you, then quote a fixed scope. The OpenAI usage itself you pay OpenAI directly, per token; we pick the right model per task and set budgets and logging so the bill stays predictable instead of surprising you.What can you build with the OpenAI API?
More than a chatbot. We build RAG assistants that answer on your docs and cite sources, agents that call your tools through function calling, document extraction with structured JSON outputs, support copilots, content and email drafting tuned to your domain, embeddings-based search, and Realtime voice where it fits. Each is scoped to a real use case, evaluated on your own inputs, and built to fail gracefully. The OpenAI API is the engine; the value is in wiring it to your actual workflow.Is our data safe with the OpenAI API?
It can be, if it's set up right, and that's part of the job. OpenAI's API does not train on your data by default, you can sign a DPA, and we keep data handling clean: minimise what you send, redact where needed, log calls, and set retention to fit your policy. We'll be honest about where data is processed and when a use case needs an extra control or a different deployment. If you have hard residency or compliance constraints, we factor that into the model and architecture choice up front.Why are you model-agnostic if you're an OpenAI agency?
Because picking the right model per task is the whole point of doing this well. GPT is excellent at many things, and for some cases Claude, an open model or a cheaper OpenAI model is the better call on quality, latency or cost. We build with the OpenAI API every day and we know it deeply, but we won't force GPT to win a case it shouldn't. We'll tell you honestly where it's the right fit and where it isn't, then build with what actually serves your product.Can you integrate GPT with n8n, Make and our tools?
Yes, that's where it earns its place. We connect the OpenAI API to your stack through n8n and Make so it runs on real triggers: a new lead, an inbound ticket, an uploaded file, instead of someone pasting a prompt by hand. We wire it to your CRM, your support tool, your database and your internal APIs through function calling. The model does its part inside a flow you can see, log and audit, with a human in the loop wherever the call matters.How long does an OpenAI integration take?
For a scoped build (one use case: a RAG assistant or an automated flow), count a few weeks: audit and architecture first, then build, evals and a production-ready integration. Building several agents and wiring them across your stack runs longer. We split into batches so you get one useful thing in production fast, evaluated on your own cases, rather than waiting on a big rollout before anyone sees GPT do real work.
Stop demoing in the Playground. Ship GPT for real.
A 60-minute audit, your best use case mapped, a build plan with the model choice and cost controls baked in. If your team can run it in-house after the build, we'll hand you the playbook. If we're the right fit, we handle it.