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OpenAI Development Services

We build directly against the OpenAI API, from tool calling and structured outputs to the data controls and evals a production system needs.

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How we work

Building on the OpenAI API is a distinct skill from adding an AI feature to existing software: it means designing around a specific provider's request shapes, tool-calling conventions and rate limits, and getting the parts that are easy to skip in a prototype, evals, cost controls, data retention settings, right before the feature carries real traffic. Our openai development services cover the Responses API for multi-turn and tool-using interactions, function and tool calling against your own systems, structured outputs constrained to a schema your code can parse without guesswork, and embeddings for search and retrieval.

We also build the parts that separate a demo from a production feature: batch processing for workloads that do not need a synchronous response, evals that catch a regression before a prompt or model change reaches users, and monitoring for cost and latency once real usage starts. Business use raises a specific set of questions around data: what OpenAI retains, for how long, and under what terms, and we walk through the account-level and API-level controls that govern that before a client's data starts flowing through the API.

We also build the abstraction layer between your application and OpenAI's specific API, so a future switch to another provider, or a mixed setup where different requests route to different models, is a configuration change rather than a rewrite. Locking an entire codebase to one vendor's exact request format is avoidable, and worth avoiding.

Fit

When OpenAI & LLM APIs is the right choice, and when it is not

Good fit

  • The product needs a specific OpenAI capability: the Responses API's built-in tool-use loop, structured outputs constrained to a JSON schema, or an embeddings model for search and retrieval over your own content
  • You are building an AI feature as its own product or system, not adding a capability to existing software you already run, which is a narrower integration job
  • The workload has volume or cost sensitivity that justifies batch processing, prompt caching and model-tier selection rather than calling the most capable model for every request by default
  • You need evals in place before shipping a prompt or model change, because the feature is customer-facing and a silent quality regression is expensive to discover after the fact

Consider something else

  • The requirement is adding an AI capability to a CRM, helpdesk or ERP you already run; that is an integration project across whichever systems are involved, not primarily an OpenAI API build, and the vendor choice is one part of a larger scope
  • The task needs open-weight models for cost, data residency or fine-tuning control that a hosted API does not offer; a self-hosted model changes the infrastructure and operational requirements significantly
  • The use case is simple enough that a no-code AI feature inside a tool you already use covers it; building directly against the API adds engineering overhead that a narrow, low-volume use case may not need
  • The team has no plan to monitor cost or quality after launch; an API-based AI feature without evals or usage monitoring tends to drift in cost and quality as usage patterns and available models change

What we build

What we build with OpenAI & LLM APIs

  • Responses API and tool-calling agents

    Multi-turn interactions built on the Responses API, with function and tool calling against your own systems and data, so the model can look up a record or take an action rather than only generating text.

  • Structured outputs

    Model responses constrained to a JSON schema your application can parse directly, removing the brittle parsing and retry logic that free-form text responses require.

  • Embeddings and retrieval

    Embedding models used for semantic search and retrieval-augmented generation over your own documents or records, paired with a vector store appropriate to your data volume and query patterns.

  • Batch processing

    The Batch API for large, non-interactive workloads, classification, extraction or summarization across many records, at lower cost and without holding a synchronous connection open per request.

  • Evals and quality monitoring

    Evaluation suites that run against a fixed set of test cases before a prompt or model change ships, catching regressions that manual spot-checking misses once a feature is in production.

  • Cost and latency control

    Model tier selection, prompt caching and request batching chosen against your actual latency requirements and volume, rather than defaulting to the most capable model for every call.

  • Data retention and business controls

    Configuration of account-level data controls and API retention settings appropriate to business use, reviewed against what your data actually is before any integration goes live.

Ecosystem

Libraries and tools we pair it with

  • OpenAI Responses API
  • OpenAI Batch API
  • Structured outputs (JSON schema)
  • Embeddings API
  • Evals frameworks (OpenAI Evals and custom test harnesses)
  • Vector stores (pgvector, Pinecone, Qdrant)

Data controls and retention for business use

OpenAI's API-level data handling differs from ChatGPT's consumer product, and the terms and retention settings that apply depend on the account type and configuration in place, not on the model being called. Before a client's data reaches the API, we review what data actually needs to be sent, whether retention or training-use settings need to be configured at the account level, and whether redaction of sensitive fields belongs in the pipeline before the request goes out at all.

This review happens during scoping, not after a feature is built, because the answer changes what gets sent to the API in the first place. A field that should never leave your systems gets handled differently from one that is safe to send once retention is configured appropriately.

Model-provider abstraction, so you are not locked in

We build an abstraction layer between your application code and OpenAI's specific request and response format, so the business logic that calls a model does not need to know which provider is answering. That makes a future move to another provider, or a setup that routes different request types to different models based on cost or capability, a configuration change rather than a rewrite of the feature itself.

This matters more as model pricing and capability shift over time than it does on day one. A feature built directly against one vendor's exact API shape works fine until that vendor's pricing changes or a competitor's model becomes the better fit for a specific request type, at which point the abstraction is the difference between a config change and a rebuild.

  • Application code calls an internal interface, not OpenAI's API directly
  • Switching or mixing model providers becomes a configuration change
  • The abstraction costs some setup time upfront and pays that back the first time a model or vendor changes

Evals before every prompt or model change

A prompt that works well on the cases a developer tested by hand can regress on cases nobody thought to check, especially after a model version upgrade changes behavior subtly. We build a fixed set of eval cases early, representative inputs with a known-good or scored expected output, and run it before any prompt or model change ships, so a regression shows up as a failing eval rather than a support ticket from a customer.

This is a small addition to the build early on and becomes the difference between confidently shipping a prompt change and guessing that it is probably fine once a feature has real usage and a history of edge cases.

Services

Services that use OpenAI & LLM APIs

Industries

Where we use it

FAQ

OpenAI & LLM APIs FAQs

What is the difference between the OpenAI Responses API and the older Chat Completions API?

The Responses API is OpenAI's newer interface, built with native support for tool-calling loops, structured outputs and multi-turn state handled server-side, reducing the amount of orchestration code an application has to manage itself. Chat Completions still works and is well-documented, but we default to the Responses API for new builds where its built-in capabilities fit the use case.

Does using the OpenAI API mean our data is used to train their models?

It depends on the account type and configuration, not on which model is called. Business and enterprise API usage generally has different default terms than consumer products, and specific retention and training-use settings can be configured at the account level. We review the applicable terms and configure them appropriately before any client data reaches the API.

Can you build our feature so we are not locked into OpenAI specifically?

Yes, by building an abstraction layer between your application's business logic and the specific provider's API format. That makes switching providers, or routing different requests to different models, a configuration change rather than a rewrite, though it adds some setup time upfront compared to calling one API directly.

What are evals and do we actually need them for a small AI feature?

Evals are a fixed set of test cases run against a prompt or model before it ships, catching regressions a manual spot-check would miss. For a low-volume, low-stakes feature, a lighter manual review may be enough. For anything customer-facing or handling real business decisions, we recommend evals from early on, since a silent quality regression after a model update is expensive to discover from a support ticket instead of a test run.

How do you control OpenAI API costs at scale?

Through model tier selection matched to what each request actually needs, prompt caching for repeated context, and the Batch API for large non-interactive workloads that do not need a synchronous response. We set up usage monitoring from launch so cost trends are visible before they become a surprise on a monthly bill.

Can OpenAI's embeddings power search over documents we already have?

Yes. We embed your existing documents or records into a vector store and use similarity search to retrieve relevant context for a query or a generation step, which is the standard pattern for retrieval-augmented generation. The right vector store and indexing approach depend on your document volume and how often the underlying content changes.

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