What does an OpenAI development company actually build?

Production systems on GPT models: RAG over your documents, customer-facing chat and voice, agents that execute workflows, and the evals and monitoring that make all of it dependable.

How long have you worked with OpenAI models?

Since GPT-2, which powered the enterprise search we built for Meta. We have shipped our own AI products every year since 2017, so the API work rides on production habits, not experiments.

Our GPT prototype works. Why does production feel so far away?

Because demos skip evals, fallbacks, cost control, and security. That gap is the project. We close it with measurement first: define what correct means, test against it, then harden.

Can you cut our OpenAI API costs?

Often substantially. Model routing, caching, and prompt budgets are the levers; many workloads run fine on smaller models once someone measures. We size the saving after seeing your usage.

OpenAI, Claude, or an open model?

Workload first, vendor second. We build on OpenAI, Anthropic, Gemini, and LLaMA, and our Valkyrie gateway exists precisely so clients can switch. We will recommend against lock-in even when it costs us simplicity.

How do you handle our data in prompts?

As a security boundary. Data classification before integration, no secrets in prompts, injection defenses on anything user-facing, and we are SOC 2 certified ourselves.

Do you do fine-tuning?

When the evals justify it. Most teams need better retrieval and prompting before they need fine-tuning; when it is warranted, we run it as a managed engagement.

How fast can you start?

Days. Most OpenAI engagements open with a one-week review of your prototype or use case, then a build plan with the eval criteria attached.

Hire OpenAI Developers

OpenAI Development for Systems That Reach Production

We have built on OpenAI models since GPT-2: enterprise search for Meta, GPT automation for Angle Health, and our own voice agents. Demo to production is the part we do.

When to Hire

When Teams Bring in OpenAI Developers

Everyone integrates OpenAI now; few do it well. The gap between a chat demo and a production system with evals, fallbacks, and cost control is where projects stall.

We have shipped GPT-backed systems since GPT-2, for clients and in our own products. Our engineers build with these APIs daily. They also code with AI assistants every day, so the productivity case they build into your product is one they already live.

And you are hiring a development company: accountable for outcomes, not a profile renting hours.

Chat demo to product

Evals, fallbacks, and observability around GPT.

RAG over your data

Retrieval that answers correctly, with sources.

Voice and agents

Assistants that act, not just answer.

API costs out of control

Model routing and caching that cut spend.

Skills and Use Cases

The Skills Your OpenAI Project Requires

OpenAI is an artificial intelligence research laboratory and company that develops and promotes friendly AI for the benefit of humanity, producing state-of-the-art language models and other AI technologies.

Our OpenAI Developers always have

GPT integrations with evals and fallbacks

RAG pipelines with vector search

Assistants, function calling, and agents

Fine-tuning and model selection

Cost control: routing, caching, and monitoring

Where Teams Use OpenAI

Customer-facing chat and voice built on GPT

RAG over enterprise documents

Workflow automation with agents

Moving prototypes into supported production systems

Related Technologies:

Anthropic

LangChain

Python

Pinecone

Google Gemini

Add a OpenAI Development Company

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How We Hire

How We Vet OpenAI Developers

Everyone can call the API. We score for the engineering that makes model output dependable.

Production LLM work

Evals, fallbacks, and monitoring wrapped around every model call

Demo-grade prompt chains

RAG

Retrieval tuned for correctness, with sources surfaced

Embeddings dumped into a vector store and hoped over

Cost control

Model routing and caching that cut token spend

The largest model for every task

Security

Prompt injection and data boundaries treated as real threats

Secrets and customer data pasted into prompts

Judgment

Knows when a regex beats a model

An LLM for every problem

Our favorite filter: Tell me about a model output that burned you, and what you changed.

Our Experience

OpenAI Work We Have Shipped

We have built on OpenAI models since GPT-2, which powered the generative AI enterprise search we shipped for Meta. For Angle Health we automated RFP responses with GPT. Our own products run on these APIs daily, including Charli, the voice agent on this page, and Valkyrie, our gateway to any AI model. The case studies below show the range.

Used in the Case Studies

Angle Health

Charlibot

Meta

Discovery Channel

Case studies from our OpenAI engagements

Why Azumo for Your Software Development

Ship faster with engineers who build with and for AI. We have delivered production ready solutions since 2016.

JP Lorandi, Azumo's CTO wearing a black collared shirt against a white background.

"Our engineers build production AI every day for our clients and our own primitives. That's the difference between a team that's used AI and one that ships it.”

Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect

Build With AI

Engineers develop with AI daily, compressing delivery cycles without cutting corners.

Senior by Default

We hire for seniority and test for it before anyone joins your team.

Scale on Demand

Grow or shrink the team as your roadmap changes — no renegotiation drama.

Time-Zone Aligned

Real-time collaboration across your full working day, from Latin America.

Engagement That Fits

Dedicated team, staff augmentation, or full project build. You pick the model.

Frequently Asked Questions

Q:
What does an OpenAI development company actually build?
Production systems on GPT models: RAG over your documents, customer-facing chat and voice, agents that execute workflows, and the evals and monitoring that make all of it dependable.
Q:
How long have you worked with OpenAI models?
Since GPT-2, which powered the enterprise search we built for Meta. We have shipped our own AI products every year since 2017, so the API work rides on production habits, not experiments.
Q:
Our GPT prototype works. Why does production feel so far away?
Because demos skip evals, fallbacks, cost control, and security. That gap is the project. We close it with measurement first: define what correct means, test against it, then harden.
Q:
Can you cut our OpenAI API costs?
Often substantially. Model routing, caching, and prompt budgets are the levers; many workloads run fine on smaller models once someone measures. We size the saving after seeing your usage.
Q:
OpenAI, Claude, or an open model?
Workload first, vendor second. We build on OpenAI, Anthropic, Gemini, and LLaMA, and our Valkyrie gateway exists precisely so clients can switch. We will recommend against lock-in even when it costs us simplicity.
Q:
How do you handle our data in prompts?
As a security boundary. Data classification before integration, no secrets in prompts, injection defenses on anything user-facing, and we are SOC 2 certified ourselves.
Q:
Do you do fine-tuning?
When the evals justify it. Most teams need better retrieval and prompting before they need fine-tuning; when it is warranted, we run it as a managed engagement.
Q:
How fast can you start?
Days. Most OpenAI engagements open with a one-week review of your prototype or use case, then a build plan with the eval criteria attached.