OpenAI AI development company · EonTech Poland

OpenAI AI development company,
production-first.

EonTech is a Poland-based OpenAI AI development company. We design, build, evaluate, and operate production applications on GPT-5, the o-series reasoning models, embeddings, the Realtime voice API, and the Assistants and Responses runtimes. Not demos, not notebooks — shipped systems your users depend on.

Start your project AI consulting
  • OpenAI + fallback providers
  • Senior EU teams · your time zone
  • GDPR · ISO 27001 · ZDR available
Senior only
Engineers who ship OpenAI in production, not demos
You own it
Prompts, evals, and orchestration code stay yours
EU nearshore
GDPR-native delivery from Poland, your time zone

Why this page exists

A working guide to building on OpenAI in production

Getting a working demo out of the OpenAI API takes an afternoon. Getting a system that survives real users, real load, real regressions, and a real invoice at end of month is a different discipline. It is the discipline of prompt versioning, retrieval quality, evals in CI, per-tenant cost dashboards, guardrails, and quiet cutovers between model tiers as prices and capabilities move. That gap — between demo and production — is where most AI features stall.

EonTech is a Poland-based full-cycle engineering agency: custom software development, web and mobile, cloud and DevOps, QA, and custom AI/ML model development. Our OpenAI practice sits inside that broader team, so the application, the retrieval layer, the evaluation harness, the cost observability, and the OpenAI integration are shipped by the same people, under one contract, with one accountable escalation path.

The rest of this page is a practical brief for CTOs, product leads, and non-technical founders comparing an open AI development company against a generic offshore vendor, a strategy consultancy, or a DIY-with-freelancers approach. Everything below is drawn from actual delivery — no fabricated case studies, no benchmark screenshots pulled from someone else's blog.

Key benefits

Why teams pick us as their open AI development company

Six practical reasons EU and UK product teams choose a specialised OpenAI partner over a generic AI shop or a strategy consultancy.

  • One partner for the whole stack

    Front end, back end, retrieval, evals, observability, and OpenAI integration under one contract. As an OpenAI AI development company we do not hand off model work to a second vendor and hope the seams hold.

  • Model-neutral discipline

    We default to OpenAI where it fits — GPT-5, o-series reasoning, embeddings, Realtime, image generation — and reach for Claude, Gemini, or open-weights when a task genuinely benefits. The point is production quality, not brand loyalty.

  • Cost-and-latency literate

    Prompt caching, structured outputs, batching, and model-tier routing are engineering decisions we make on your data — not defaults we cargo-copy. Bills stay predictable when traffic grows.

  • Real evals, not vibe-checks

    Golden sets, task-specific rubrics, and regression suites go into CI alongside the code. Model, prompt, and retrieval changes ship the same way features do — with numbers.

  • Nearshore EU delivery

    Poland-based senior engineers with full-day CET overlap for the UK, EU, and Nordics. Real-time reviews and pair sessions, not overnight email chains.

  • You own the artefacts

    Prompts, evals, retrieval indexes, orchestration code, and deployment configuration are yours from day one. No lock-in, no proprietary middleware between you and the OpenAI API.

Where we ship on OpenAI

The OpenAI surface, top to bottom

The API is not one thing. These are the eight surfaces our OpenAI AI development company work spans — routed intentionally, not "let's just use GPT-5 for everything".

  • GPT-5 & 4.1 family. Default reasoning and text generation across chat, agents, and structured extraction. We route to the right tier per call — not one model for everything.
  • o-series reasoning. Deep-reasoning tasks: hard extraction, planning, multi-step math, and code review. Explicit budget and fallback rules to keep spend sane.
  • Embeddings + retrieval. text-embedding-3 with hybrid search, reranking, and evaluated retrieval. As an open AI development company we ship RAG that gets measured, not guessed at.
  • Realtime API (voice). Low-latency spoken interfaces for support, sales, and in-app copilots — with barge-in, VAD, and function-calling wired into your product.
  • Assistants / Responses API. Thread persistence, file search, and tools without rebuilding state each turn. Useful when you want OpenAI's runtime instead of your own.
  • Structured outputs & tools. JSON Schema-strict outputs, typed tool definitions, and end-to-end type safety so downstream code trusts what the model returns.
  • Image, vision & DALL·E. Vision extraction from documents and images, plus generation where it belongs in a real product surface — never a decorative gimmick.
  • Fine-tuning & distillation. When prompting stops paying back, we fine-tune 4.1 or o-mini on your data with proper evals — usually to cut latency and cost, not to chase accuracy alone.

How it works

From data audit to a production system

Five honest steps. The same arc runs whether the endpoint is a customer copilot, a document-intelligence pipeline, or a Realtime voice agent.

  1. 01

    Discovery & data audit

    Two to four weeks of workflow mapping, data-quality check, and cost/latency envelope. The output is a scoped pilot with a written accuracy bar — no vibes.

  2. 02

    Prompt, retrieve, and orchestrate

    Prompt design, retrieval index build, tool definitions, and orchestration wiring inside your codebase. Everything under version control, everything reviewable.

  3. 03

    Build evals before features

    Golden sets, task-specific rubrics, and regression suites go in first. We do not ship prompt changes past this point without a number attached.

  4. 04

    Ship, observe, and iterate

    Streaming UI, cancellation, guardrails, and per-turn logging. Real-user traces, cost dashboards, and drift alerts land on day one, not month three.

  5. 05

    Fine-tune or distil when it pays

    Once prompting plateaus, we fine-tune or distil onto smaller OpenAI models to cut latency and cost. Only when the eval numbers say it will actually pay back.

Main use cases

Where OpenAI actually earns its place

Six product shapes where an open AI development company can deliver measurable value inside a quarter — not indefinite POC theatre.

  • Customer support copilots

    Grounded chat over your knowledge base, ticket triage, and agent-assist that handle real conditions — deflection with citations, escalation without drama.

  • Sales & marketing intelligence

    Lead enrichment, ICP scoring, meeting notes, and outbound copy — with human-in-the-loop where the ROI does not clear the risk.

  • Document & contract intelligence

    Structured extraction from PDFs, contracts, invoices, and clinical notes. Evaluated per field, not per prompt.

  • Internal knowledge assistants

    Retrieval-augmented assistants over Confluence, SharePoint, Google Drive, or your data warehouse — with permissioned answers and audit trails.

  • Voice agents (Realtime API)

    Spoken support, appointment booking, and in-car copilots on OpenAI Realtime — sub-second latency and clean function-calling handoff.

  • Coding copilots & internal tools

    IDE-style assistants, code-review bots, and internal ops tools tied into your repos, tickets, and runbooks.

Features

Built in by default, not bolted on later

  • Prompt & version management. Prompts under source control with semver and blue/green rollout — no more editing prompts in an admin UI at 03:00.
  • Golden-set evals in CI. Every merge runs task-specific rubrics against a versioned eval set; regressions block the same way test failures do.
  • Streaming UX with cancellation. Server-sent events, token streaming, and real cancel semantics — so users never wait on a request they no longer want.
  • Cost & token dashboards. Per-tenant, per-feature, per-model dashboards from day one. No 'why is the bill this size' Slack threads at end of month.
  • Guardrails & moderation. Input classification, output validation, PII redaction, and jailbreak detection — layered defence, not a single filter.
  • Prompt caching & batch. OpenAI prompt caching for repeated context, and Batch API where latency allows — often 50–90% off list price.
  • Structured outputs first. JSON Schema-strict outputs, typed clients, and downstream code that treats model output like any other API contract.
  • Fallback & multi-provider. Graceful degradation to a cheaper model or a different provider when a call fails or the primary lane is saturated.

Industry relevance

Domains our OpenAI work already lands in

Domain fluency shortens every discovery. These are the buyer shapes our senior engineers already speak — with real compliance-and-audit context, not just a demo dataset.

  • Fintech & payments

    Support copilots over regulated knowledge bases, KYC document extraction, and internal ops assistants — inside GDPR and audit constraints.

  • SaaS & B2B platforms

    In-product copilots, onboarding assistants, and admin-facing intelligence that meaningfully cut support cost.

  • E-commerce & retail

    Merchant assistants, product-description generation, and customer support that reads the order history before it answers.

  • Healthtech

    Clinical documentation, patient-portal intake, and back-office coding — inside HIPAA/GDPR posture, human-in-the-loop by design.

  • Legal, insurance & compliance

    Contract review, policy-summary tools, and clause extraction where explainability and citations are non-optional.

  • Logistics & mobility

    Voice agents, dispatch copilots, and driver support that survive edge cases the office has never seen.

Trust & compliance

The signals procurement and CISO ask for

AI features live inside a real compliance envelope: GDPR for personal data, ISO 27001 for information-security posture, OWASP's LLM Top 10 for AI-specific attacks, and — for regulated buyers — sector-specific rules on top. We build to those from the first sprint, not the first audit.

As an EU-based open AI development company we sign standard DPAs and SCCs, layer PII redaction pre- and post-model, and can request Zero Data Retention terms with OpenAI on your account. For particularly sensitive workflows, Azure OpenAI in an EU region is often the right destination — same models, different compliance shape.

Why EonTech

An OpenAI partner you can trust to own it

  • One partner

    Front end, back end, retrieval, evals, and OpenAI integration under one accountable team.

  • Senior only

    Engineers who have shipped GPT-5 / o-series to production, not prompt-copying juniors.

  • Your time zone

    Poland-based team overlapping your working day and meeting EU data rules.

  • You own it

    Prompts, evals, retrieval, and orchestration code are yours to run and extend.

Common questions

What teams ask an OpenAI AI development company

  • What does 'OpenAI AI development company' actually mean at EonTech?

    It means we design, build, evaluate, and operate production applications on top of the OpenAI API — GPT-5, o-series reasoning, embeddings, Realtime, Assistants and Responses, tools, and structured outputs. As an OpenAI AI development company we cover the full loop from data audit to eval-in-CI to per-tenant cost dashboards, not just prompt engineering.

  • How is an open AI development company different from a generic AI consultancy?

    An open AI development company writes and owns the production code. Consultancies typically hand you a deck and a proof of concept. We hand you a running system in your repo, versioned prompts, an eval suite, guardrails, dashboards, and a support arrangement.

  • Do you build only on OpenAI, or across providers?

    We default to OpenAI where it fits, and we are candid when Anthropic Claude, Google Gemini, or an open-weights model (Llama, Qwen) fits better. The right architecture almost always keeps a fallback lane to a second provider — one API being down should not take your product down.

  • How do you keep OpenAI costs predictable at scale?

    Model-tier routing (cheap for easy, expensive for hard), OpenAI prompt caching for stable context, the Batch API where latency allows, JSON-schema-strict outputs to avoid retries, and per-tenant caps. Cost dashboards land on day one, not after the first surprise invoice.

  • Can you build with the Realtime API for voice?

    Yes. We ship low-latency voice copilots on the Realtime API with barge-in, VAD, function-calling, and clean handoff to human agents. Common use cases: support, appointment scheduling, in-car and in-warehouse copilots.

  • How do you evaluate LLM features?

    Task-specific rubrics against versioned golden sets, run in CI. Retrieval quality (recall@k, MRR), extraction accuracy per field, and grounded-answer correctness with citation checks. Prompt and model changes ship the same way features do — with numbers attached.

  • How do you handle prompt injection and jailbreaks?

    Layered defence: input classification, retrieval sanitisation, tool-call whitelisting, structured-output validation, output classification, and per-turn logging. We map to OWASP's LLM Top 10 and design assuming the model is adversarial, not that it will behave.

  • Do you fine-tune OpenAI models?

    When it pays. We usually start with prompting plus retrieval. Once the eval numbers plateau or costs drift up, we distil onto 4.1-mini, 4.1-nano, or a fine-tuned base model on your data. The goal is lower latency and cost, not chasing accuracy alone.

  • How do you protect PII and stay GDPR-compliant?

    As an EU-based delivery team we sign DPAs and SCCs, layer PII redaction pre- and post-model, and can request Zero Data Retention terms with OpenAI on your account. Sensitive workflows can run through Azure OpenAI in an EU region where the compliance shape fits better.

  • What team shapes can you field?

    Full-project delivery, dedicated AI pods (engineers, ML, retrieval, evals, DevOps), or staff augmentation with senior engineers plugged into your team. Monthly rolling contracts, no long lock-in.

  • How quickly can we see a working system?

    A scoped pilot on your own data typically lands in six to eight weeks, with real evals and a production-shape architecture — not a Notebook demo. Simple copilots on a clean knowledge base can be shorter.

  • What if OpenAI changes pricing or models mid-project?

    Model tiers and pricing move quarterly. Our architectures abstract the model choice behind a small routing layer so we can re-tier without touching product code. New models are benchmarked against your golden set before we promote them.

  • Who owns the prompts, evals, and code you build?

    You do, entirely — every prompt file, eval set, retrieval index configuration, orchestration line of code, and deployment manifest is yours from the first commit. There is no proprietary middleware between you and the OpenAI API.

Related practices

Nearby capabilities at EonTech

Ship an OpenAI feature that survives contact with users

Tell us the workflow and the compliance envelope. We will scope a pilot in days, then start building a production-shape system on OpenAI — evals in CI, cost dashboards on day one, and you owning every line of code.

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