OpenAI integration services: add GPT models to the product you have

Ryz Labs provides OpenAI integration services through senior engineers and AI pods who add OpenAI models to products you already run, using the OpenAI API directly or Azure OpenAI in your own tenancy. The work covers the API layer, structured outputs, tool calling, rate limits, cost control and the evals that tell you a feature is ready. Our engineers are the top 1% of the tens of thousands we have interviewed, and they work on US business hours.

What we build

Integrating OpenAI into an existing product is different from building a new AI app: the feature has to fit your data model, your auth, your latency budget and your release process. Typical work:

• In-product AI features. Summaries, drafting, smart search and classification added to existing screens and APIs, behind feature flags.
• Structured Outputs pipelines. Responses constrained to a JSON Schema with strict mode, so extracted data maps straight to your database types.
• Function calling into your APIs. Tool definitions for your existing endpoints, with argument validation and the user's own permissions applied.
• Responses API migrations. Moving older Chat Completions or Assistants API code to the Responses API, which OpenAI positions as the path forward, without changing user-facing behavior.
• Embeddings and semantic search. OpenAI embedding models indexed in pgvector, OpenSearch or Pinecone, with re-embedding plans for when models change.
• Batch processing. Large backfills and nightly jobs through the Batch API, which trades turnaround time for lower cost.
• Realtime voice features. Speech-in, speech-out features on the Realtime API, connected to telephony or your web and mobile apps.

How an engagement works

1. Talk. We review the product, the feature you want, your data rules and whether you need the OpenAI API, Azure OpenAI or both.
2. Match. We propose engineers who know your application stack (for example TypeScript and Node.js, or Python and Django) plus OpenAI experience, with names and a price.
3. Join. The engineers work in your repos and CI, follow your code review and release process, and demo the feature behind a flag each week.
4. Grow. Extend to more features on the same integration layer, or hand it over with tests and runbooks.

In week 1, the team sets up keys or Azure deployments per environment, writes the integration layer and collects 50 to 100 real examples for an eval set. By month 1, the first feature usually works behind a flag for internal users, with cost per request and eval scores tracked. By month 3, typical work is gradual rollout, rate-limit tuning, caching and a second feature. The real timeline follows scope and onboarding.

The stack our teams work in

How we keep OpenAI features reliable and affordable

OpenAI integrations break in predictable ways. The problems a senior team handles up front:

• Rate limits at launch. Limits apply per organization and model, in requests and tokens per minute, and depend on your usage tier. We estimate peak load, request increases early, queue non-urgent work and back off on 429s.
• Parsing free text. Regex over model prose breaks. Structured Outputs with a strict schema and server-side validation keep data clean.
• Surprise bills. We log tokens per feature, keep stable instructions at the start of prompts so prompt caching applies, use smaller models where evals allow and move bulk jobs to Batch.
• Model updates. Aliases can move to new snapshots. We pin dated model versions and rerun evals before switching.
• Prompt injection through user content. User text and retrieved documents are treated as data, tool calls are checked against the user's permissions, and sensitive actions need confirmation.
• Data handling questions from security. We document what goes to which endpoint, apply PII redaction where needed and help your team review OpenAI's or Azure's data retention terms and options for your account.
• Latency in the UI. We stream tokens to the screen, show partial results and set timeouts with a usable fallback.

Our pods have shipped production AI systems such as an AI driver-support agent that covers about 218,000 driver calls a year in three languages. See the case studies.

Team shapes and cost

Ryz engineers typically cost $7,000 to $15,000 per engineer per month: mid-level (comparable to Amazon L5) $7,000 to $10,000, senior (comparable to Amazon L6) $10,000 to $15,000, leads $15,000+.

• Single feature: 1 to 2 senior engineers on your team. 1 to 2 × $10,000 to $15,000 = about $10,000 to $30,000 per month.
• Integration pod: a tech lead plus 2 senior engineers for several features and a shared integration layer. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
• Production pod: about 7 senior engineers, including a tech lead, an ML engineer and backend engineers, when OpenAI features span several products. About $75,000 to $105,000+ per month.

Total cost is team size × duration × monthly rate; OpenAI or Azure usage is separate, on your own account. You get a scoped plan, a price and the names of the people before you start.

Dedicated team or staff augmentation?

For one or two features in an application your team owns, staff augmentation is usually enough: hire OpenAI developers or Azure OpenAI engineers who work on your team and ship through your process. When OpenAI features touch several products and need shared infrastructure, an AI pod team can own the integration layer, evals and rollout.

When Ryz isn't the right fit

If you only need ChatGPT Enterprise rolled out to employees, that is a procurement and enablement task, not an engineering one. If you want an off-the-shelf AI platform to license, or engineers in European or Asian time zones, other providers fit better.

Related

• OpenAI vs Anthropic for enterprise
• AWS Bedrock vs Azure OpenAI
• AI integration services
• AI chatbot development
• AI development services

FAQ

Should we use the OpenAI API or Azure OpenAI?

Use Azure OpenAI if your organization standardizes on Azure and needs private networking, Entra ID access control or specific regions. Use the OpenAI API directly if you want new models and features as soon as OpenAI releases them. Some teams use both. Model availability differs between the two, so we check before committing.

Does OpenAI train on data we send through the API?

OpenAI states that it does not train on business API data by default, and Azure OpenAI has its own data terms. Your legal and security team should review the current terms for your account; we document exactly what data the integration sends.

How much does OpenAI integration cost?

Ryz engineers typically cost $7,000 to $15,000 per engineer per month, with leads from $15,000. One senior engineer is $10,000 to $15,000 per month; a three-person pod is about $35,000 to $45,000+. Total cost is team size × duration × monthly rate, plus your own API usage.

How quickly can an engineer start on our integration?

After the scoping call we propose engineers by name. Most of the timeline depends on scope and on how quickly your team grants repo access and API keys or Azure deployments.

Can you make our integration provider-agnostic?

Yes. We keep OpenAI calls behind a thin interface so you can test Anthropic models or Bedrock-hosted models on the same eval set and switch per feature if the numbers favor it.

Questions we didn't answer? Email info@ryzlabs.com.