The OpenAI agency.GPT in production, not a demo.
The Playground gives you a demo, not something your users can touch. As an OpenAI agency, we build GPT into your app and backend with the OpenAI API, ground assistants in your own content with RAG, ship agents that fire through n8n and Make, and wire in the cost and governance controls. Remote, US-friendly, and used to shipping with US teams.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
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GeminiA good OpenAI agency puts GPT in production, not on a slide.
Anyone can drop a prompt into the Playground. Building the OpenAI API into your product, grounding it in your data, and keeping the bill in check is another job entirely. These are the four we take on.
- API integration
GPT wired into your product and stack
A tab open in the Playground is not something your users can click. We build the OpenAI API into your app and backend: the Responses API, function calling, structured outputs against a JSON Schema, streaming, and the tools your models genuinely need. Auth, rate limits, retries, fallbacks, token tracking. All handled. GPT ends up running in production behind your UX, not sitting in a notebook that never ships.
See a typical build - RAG & assistants
Assistants that answer on your own data
Vanilla GPT gives you vanilla answers. We build RAG on your real content instead: embeddings, a vector store, retrieval, and prompts shaped around your domain, so replies point back to your docs, your tickets, your product. Support copilots. Internal Q&A. Analytics helpers. Content and email drafting. Every one gets scoped and run through evals, and each is built to say it does not know rather than make something up.
See the method - Agents & automation
Agents that run your real workflows
A chatbot is not the win. GPT taking a task from start to finish is. We build agents that call your tools and your APIs, then drop them into your stack through n8n and Make so they fire on a trigger, not on someone remembering to paste a prompt. Lead enrichment, ticket triage, document extraction, content pipelines: scoped, permissioned, and with a person in the loop wherever the call actually carries weight.
See the integrations - Cost & governance
GPT under control, not a runaway bill
Teams that ship first and govern later get a surprise at the end of the month. We size the model to the task, reach for the cheaper one where it holds up and the reasoning one where it pays for itself, cache and batch when that helps, and set budgets, logging and clean data handling. We stay model-agnostic on purpose. If Claude or another model suits your case better, you will hear it, because Hack'celeration is an AI and automation agency before anything else.
See AI enablement
We build on OpenAI like product engineers, not prompt jockeys.
A lot of GPT projects fall over the same way. A slick Playground demo, no evals, no error handling, a token bill nobody planned for, and it never reaches a single real user. We treat the work as product instead: built into your app, grounded in your data, measured on your own cases, and governed on cost and data from the very first commit.
- Audit · read your product, your data, and the spot where GPT moves a real metric
- Build · API integration, RAG and agents, scoped and run through evals on your cases
- Integrate · drop it into your stack via n8n and Make so it fires on triggers
- Govern · model choice, cost controls, logging and clean data handling, by default
We ship on the OpenAI API every single day.
There's no partner tier for sale here. We ship real products on the OpenAI API, this very stack included, so we set it up the way production actually behaves: structured outputs, evals, retries and fallbacks, and a model chosen per task. That is precisely the part that goes missing when a GPT project stops at a Playground screenshot.
- We ship on the OpenAI API every day, so we set it up the way production wants it, not the way a demo hints at: structured outputs, evals, retries and fallbacks.
- Model-agnostic and blunt about it. If Claude or another model wins your case on quality or cost, you hear that instead of us bending GPT to fit.
- Governance comes first, not last. Model choice, budgets, logging and clean data handling go in on day one, well before any bill has a chance to spike.
- You end up self-sufficient. The integration sits in your codebase and your n8n flows, so your team runs it without a retainer keeping the lights on.
The OpenAI API at the center, your product stack wrapped around it.
We build the pieces that turn a model into dependable product behaviour, then hook them into the way your business already runs. Here is what a real integration actually covers.
- Setup
OpenAI API integration
We plug the OpenAI API into your product: the Responses API, chat completions, streaming, function calling and structured outputs. Auth, rate limits, retries and fallbacks are all set up so the thing holds up once real traffic hits it.
- Setup
RAG & embeddings
We stand up retrieval on your own data: embeddings, a vector store, chunking, and retrieval tuned so GPT answers from your docs and shows its sources rather than guessing from whatever it learned in training.
- Setup
Agents & tool use
We build agents that reach your tools and APIs through function calling. Each one is scoped to a single job, granted only the permissions it needs, and paused for review where the decision counts, so it acts instead of just chatting.
- Setup
n8n & Make automation
We link GPT to your stack with n8n and Make so it runs on real triggers: a new lead, an inbound ticket, a file someone just uploaded. The model does its part inside a flow you can watch and audit.
- Setup
Structured outputs & evals
We lean on JSON Schema structured outputs so responses come back typed and easy to parse, and we set up evals so you can measure output quality on your own cases before and after any prompt or model change.
- Setup
Cost, logging & governance
We match the model to the task, cache and batch where that helps, set budgets, log every call, and keep data handling tidy, DPA and retention questions included, so both the bill and the risk stay something you can predict.
We find where GPT pays off, you leave with a plan.
Before a single quote, we spend 60 minutes on your product, your data, and the spot where the OpenAI API would truly move a metric. You walk away with a straight read on what GPT fixes, what to build first, and which model fits. No pitch. Just an engineer's take on your use case.
- A straight 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 put in from the start
- An honest word on what it won't fix
How we run an OpenAI integration.
Five steps, in order. Nothing ships until evals pass on your own cases, cost and data governance never gets skipped, and your team owns the result at the end. Every step comes with a deliverable, and you sign off before we move to the next.
- Step 1 · Use-case audit
Find where GPT moves a real metric
We sit down with you and go through the openings that matter: support volume, drafting you repeat all day, document extraction, internal search nobody can run fast. Your product, your data, your stack, all on the table. Half the value here is naming where the OpenAI API helps and where a plainer tool or a different model already does the job, so you never point GPT at a problem it was never going to fix.
- Step 2 · Build the integration
Build it to hold up under real traffic
We build the OpenAI API into your product: the Responses API, function calling, structured outputs and streaming, with auth, rate limits, retries and fallbacks all taken care of. When the work is knowledge-heavy, we add RAG on your data with embeddings and a vector store. The whole thing stays scoped and typed, built to fail softly rather than to look sharp in a one-off demo.
- Step 3 · Evaluate & tune
Grade output quality on your own cases
Vibes are not a shipping criterion. We build evals on your real inputs so we can see whether the assistant or agent actually gets the answer right, then tune the prompts, the retrieval and the model choice against that signal. What you get is the before and after on your own cases, not a benchmark score lifted from a press release, and it happens well before anything gets near production.
- Step 4 · Integrate & automate
Drop it into your stack, running on triggers
We connect GPT to the places work actually happens: your app, your CRM, your support tool, your data. Through n8n and Make it fires on a new lead, an inbound ticket, an uploaded file. The model handles its piece inside a flow you can see, log and audit, and a person stays in the loop wherever the decision carries real weight.
- Step 5 · Govern & hand over
Lock down cost and data, then hand it over
We match the model to the task, set budgets and logging, and keep your data handling clean so both the bill and the risk stay predictable. The integration lives in your codebase and your n8n flows, which means your team owns it outright. Want to go further in-house? Our training walks the OpenAI API and agents end to end. Want us on hand for whatever scales next? We work that out on its own terms.
Judge us on what ships and keeps running.
There's no partner badge to wave around, so we lead with what counts: word from the teams whose OpenAI integration we built, and whether GPT kept pulling its weight after we walked away. 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 in place before anything reaches a user
- Assistants and agents run through evals on your own cases, not a demo
- Trustpilot reviews come from the teams we built it for
The questions teams keep bringing us.
What does an OpenAI agency actually do?
An OpenAI agency puts GPT to work inside your product and stack, rather than handing you a Playground demo you can't show a single user. Concretely, we build the OpenAI API in (Responses API, function calling, structured outputs), stand up RAG assistants grounded in your data, ship agents that run your workflows, connect the whole thing through n8n and Make, and set the cost and governance controls around it. The aim is GPT doing real work in production. Not a chatbot a handful of people poke at once and forget.OpenAI API vs just ChatGPT: which do we need?
Depends on the job. ChatGPT is a product your team opens in a browser; the OpenAI API is how you build GPT into your own product, automate a workflow, and ground answers in your data. Want a colleague-style assistant to chat with? ChatGPT, or ChatGPT Enterprise, is often plenty, and we'll say so. Want GPT living inside your app, your CRM or an automated flow? That's the API, and that's what an openai api agency actually builds: function calling, embeddings, structured outputs, the parts a chat window simply can't hand you.How much does an OpenAI integration cost?
It depends on scope, and we won't pretend otherwise. A straight API integration is a different animal from building RAG on your data and wiring agents across your stack, so there's no flat package to quote blind. We start with a free 60-min audit to find where the OpenAI API genuinely moves a metric for you, then quote a fixed scope on that. The OpenAI usage itself you pay OpenAI directly, per token. We match the model to the task and set budgets and logging so the bill stays something you can see coming, not a jolt at month end.What can you build with the OpenAI API?
Far more than a chatbot. RAG assistants that answer on your docs and cite the source, agents that reach your tools through function calling, document extraction with structured JSON outputs, support copilots, content and email drafting shaped to your domain, embeddings-based search, and Realtime voice where it earns a place. Each one is scoped to a real use case, run through evals on your own inputs, and built to fail softly. Think of the OpenAI API as the engine; the value shows up in how it's wired to the way you actually work.Is our data safe with the OpenAI API?
It can be, when it's set up properly, and that setup is part of the job. The OpenAI API does not train on your data by default, you can sign a DPA, and we keep handling clean: send the minimum, redact where it's warranted, log the calls, and set retention to match your policy. We'll be straight about where data gets processed and when a use case needs an extra control or a different deployment. Hard residency or compliance limits? We factor those into the model and the architecture from the start, not after the fact.Why stay model-agnostic if you're an OpenAI agency?
Because matching the right model to each task is what doing this well actually means. GPT is superb at a lot, and on some cases Claude, an open model, or a cheaper OpenAI model is honestly the better call on quality, latency or cost. We ship on the OpenAI API every day and know it inside out, but we won't twist GPT into winning a case it shouldn't. You get a straight read on where it fits and where it doesn't, and then we build with whatever genuinely serves your product.Can you integrate GPT with n8n, Make and our tools?
Yes, and that's exactly where it earns its keep. We connect the OpenAI API to your stack through n8n and Make so it fires on real triggers: a new lead, an inbound ticket, an uploaded file, instead of someone pasting a prompt by hand. Through function calling it reaches your CRM, your support tool, your database and your internal APIs. The model does its part inside a flow you can see, log and audit, with a person kept in the loop wherever the call actually matters.How long does an OpenAI integration take?
For a scoped build, one use case like a RAG assistant or a single automated flow, count a few weeks: audit and architecture first, then the build, the evals, and a production-ready integration. Stringing several agents across your stack takes longer, so we split the work into batches. That way one useful thing lands in production fast, evaluated on your own cases, instead of everyone waiting on a big rollout before they ever watch GPT do real work.Do you work remotely with US teams?
Yes. Hack'celeration works remote and is used to shipping with US teams, so time zone is not a blocker. The audit runs over a call, the build lands straight in your codebase and your n8n flows through your own repos and tools, and we sync on whatever cadence suits you. Because the OpenAI API and n8n both run wherever your stack does, none of this depends on being in the same room. You get the same scoping, the same evals on your cases, and the same cost and data controls, delivered remotely.
Quit demoing in the Playground. Ship GPT for real.
A 60-minute audit, your strongest use case pinned down, a build plan with the model choice and cost controls already baked in. If your team can run it in-house afterward, you get the playbook. If we're the right fit, we take it on.