OpenAI API development company
Krapton integrates the OpenAI API into products that need dependable output: schema-checked responses, tool calls into your systems, retrieval over your documents and voice through the Realtime API, with evals, cost controls and fallbacks built in.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
OpenAI API development services for production apps
Krapton is an OpenAI API development company that turns model capabilities into product features with predictable behavior, cost and security. We build most features on the Responses API, which combines text, image input, function calling and built-in tools such as file search and web search behind one interface, with Structured Outputs enforcing a JSON schema on every reply. Around that core we add the engineering a prototype lacks: streaming to the interface over server-sent events, retries with exponential backoff for rate limits, prompt caching through stable prompt prefixes, the Batch API for bulk offline jobs, and the Moderation endpoint where user content needs screening. Every feature ships with an evaluation set so a model upgrade becomes a measured decision instead of a surprise. We also cover the policy side: data retention settings, regional data residency where your account supports it, and when Azure OpenAI is the better route for enterprise procurement.
OpenAI API development services
Responses API integration
Responses API features with streaming, conversation state, image inputs and built-in tools, choosing reasoning effort and model tier per task rather than one default for every request.
Function calling and agents
Your APIs become strictly typed function tools, and multi-step agents built with the OpenAI Agents SDK handle handoffs, guardrails and tracing, with confirmation steps before any write action.
Retrieval over your documents
We use OpenAI vector stores with file search for simple cases, or embeddings in pgvector or another store when you need custom chunking, metadata filters and hybrid ranking.
Voice with the Realtime API
Speech-to-speech assistants over WebRTC, WebSocket or SIP phone connections, with interruption handling, function calls mid-conversation, and transcripts logged for quality review and compliance.
Assistants API migration
Assistants API projects move to the Responses API and Conversations API, with threads, files and tool definitions mapped across and behavior checked against an evaluation set before cutover.
Cost and latency engineering
We trim prompts, structure them for prompt caching, route easy requests to smaller models, push offline work to the Batch API and track token spend per feature in your dashboards.
Why teams choose Krapton for OpenAI API
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Evals gate every model change
OpenAI releases new models often. We keep a scored test set per feature, so switching models or prompts is approved on measured results rather than on a demo.
Schemas over string parsing
Structured Outputs and strict function schemas replace regex parsing of free text, which removes a whole class of production errors when responses feed databases or downstream APIs.
Prompt injection taken seriously
We treat user and document content as untrusted input, keep secrets out of prompts, scope tool permissions narrowly and test for prompt injection before release.
No lock-in by design
Model calls sit behind a thin internal interface, so you can add Azure OpenAI, another provider or a self-hosted model later without rewriting product code.
How a OpenAI API project runs with Krapton
01
Use-case framing
We pin down the task, inputs, required output format, acceptable error rate and data sensitivity, which decides model tier, retention settings and whether retrieval is needed.
02
Eval set and prototype
A scored evaluation set comes first; prompts, schemas and tools are then iterated against it in the Playground and in code until results meet the bar.
03
Production build
We add streaming, retries, rate-limit handling, logging with PII redaction, moderation where needed and per-feature cost tracking, then load test against your account’s rate limits.
04
Monitor and improve
Live outputs are sampled and scored, failures become new eval cases, and model upgrades are trialed on a slice of traffic before full rollout.
The OpenAI API stack we build with
OpenAI API development: questions answered
What does an OpenAI API development company do beyond writing prompts?
It builds the software around the model. An OpenAI API development company designs output schemas, tool integrations, retrieval, streaming interfaces, error handling for rate limits, logging, cost tracking and evaluation suites. Krapton also configures data retention and access controls on your OpenAI organization, sets up separate projects and keys per environment, and documents how to upgrade models safely.
Should we use the Responses API or Chat Completions?
Use the Responses API for new builds. It supports built-in tools such as file search, web search and code interpreter, keeps conversation state server-side when you want it, and works better with reasoning models. Chat Completions remains supported and is fine for simple stateless calls or code that must stay compatible with other OpenAI-style APIs. We migrate existing Chat Completions code only when a feature needs what Responses adds.
Is our data safe when building on the OpenAI API?
It can be, with the right settings and design. By default, OpenAI does not train on API data, and eligible organizations can request zero data retention or choose regional data residency for supported regions. On our side, we redact personal data before it reaches prompts where possible, keep keys in a secrets manager, separate projects per environment, and log only what your compliance team approves.
How do you keep OpenAI API costs and latency under control?
By matching each request to the smallest model that passes its evals. We also keep long system prompts stable so prompt caching applies, cap output tokens, stream responses so users see text quickly, and move non-urgent work such as classification backfills to the Batch API. Per-feature token dashboards and budget alerts show cost drift early, before it reaches the monthly invoice.
What decides the cost of OpenAI API development services?
The number and complexity of AI features decide it, not the API itself. Key drivers are how many tools and internal systems the model must call, whether retrieval needs document cleaning and custom indexing, voice or real-time requirements, evaluation depth, compliance work such as PII handling or audit logging, and whether an existing Assistants API build must be migrated. API usage is billed by OpenAI to your account.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.