Services // AI Engineering

AI Integration for Real Products.

CodeOpx integrates the OpenAI, Anthropic Claude, and Google Gemini APIs into existing Next.js and Node.js applications — chat assistants, retrieval over your own documents, classification and extraction pipelines, and multi-step agents. Contained features ship in one to two weeks, in your repository, with cost controls and evaluation from day one.

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What we build

forum

Chat assistants

Streaming chat grounded in your product data, with conversation history, tool calling, and a fallback path when the model is unavailable.

RAG over your documents

Ingestion, chunking, embeddings, and a vector store (pgvector, Pinecone, Qdrant) so answers cite your own content instead of guessing.

smart_toy

Agents and workflows

Multi-step automation that calls your APIs — triage, enrichment, drafting — with retries, timeouts, and human approval where it matters.

text_snippet

Extraction and classification

Turning invoices, tickets, CVs, or transcripts into structured JSON your database can actually use, validated against a schema.

mic

Speech and vision

Transcription, text-to-speech, OCR, and image understanding wired into existing upload and processing flows.

monitoring

Evaluation and cost control

A fixed question set to measure quality before and after changes, plus caching, model tiering, rate limits, and per-user usage logging.

Stack

Models: OpenAI GPT and embeddings, Anthropic Claude, Google Gemini, open-weight models via Ollama or a hosted endpoint.

Application: Next.js (App Router), React, Node.js, TypeScript or JavaScript, Python where the ML tooling calls for it.

Data: PostgreSQL with pgvector, Pinecone, Qdrant, Redis for caching and rate limits, S3-compatible object storage.

Delivery: your repository, your review process, Docker where useful, deployed to Vercel, Cloud Run, or your own VPS.

Remote-first

How remote collaboration works

Founders and product teams hire us remotely for Next.js builds, APIs, and launches—without losing velocity across time zones.

Book a call

publicFully remote—teams worldwide. English-first written updates and scheduled video calls when decisions need sync time.
scheduleOverlap for reviews and workshops is agreed in discovery (e.g. Americas / EU / UK windows) so feedback does not stall builds.
forumAsync between calls: recap notes, Loom-style walkthroughs when useful, and a single thread per milestone so context stays searchable.
hubWe meet you in your stack where possible—GitHub/GitLab, Figma, Linear, Jira, Notion—instead of forcing a foreign workflow.
paymentsMilestone billing with written scope, deliverables, and acceptance checks per phase so remote budgeting stays predictable.
verified_userNDA-friendly process; code and design assets live in repositories and workspaces you control unless we agree otherwise.

Who this is for

Teams with a working product that need an AI feature to actually ship — not a prototype that stalls at the demo, and not a rebuild of what already works.

Typical timeline

One to two weeks for a contained feature inside an existing codebase. RAG, evaluation harnesses, and agent workflows run longer — the work is in the data.

Related services

Building the product itself? Next.js SaaS development. Need it running somewhere? VPS deployment and maintenance.

Frequently asked questions

How much does it cost to integrate the OpenAI API into an existing app?: A single well-scoped feature — a chat assistant, a document summariser, a classification endpoint — typically lands in the low four figures. Cost is driven by how much of your data has to be prepared, not by the API call itself. You get a written estimate after a short technical review, before any code is written.
How long does an AI integration take?: A contained feature in an existing Next.js or Node.js codebase usually ships in one to two weeks. Retrieval over your own documents, evaluation harnesses, and multi-step agent workflows run longer because the work is in the data and the testing, not the wiring.
Which models and providers do you work with?: OpenAI (GPT and embeddings), Anthropic Claude, Google Gemini, and open-weight models served through Ollama or a hosted endpoint. Provider choice follows the task and your budget — we benchmark two options on your real inputs rather than defaulting to whichever model is loudest that month.
Can you build retrieval-augmented generation (RAG) over our own documents?: Yes. That means a document pipeline, chunking and embedding, a vector store (pgvector, Pinecone, or Qdrant), and retrieval tuned against real questions from your team. We measure answer quality on a fixed question set instead of shipping on vibes.
How do you keep API costs and token usage under control?: Caching for repeated prompts, a smaller model for the easy majority of requests, streaming so users see output sooner, hard per-user rate limits, and usage logging from day one. Most projects cut their initial token bill substantially once the traffic pattern is visible.
Is our data used to train models?: No. Requests go through provider endpoints that exclude data from training by default, keys stay server-side, and we can add PII redaction before anything leaves your infrastructure. If you need a self-hosted open-weight model instead, that is a supported option.
Can you work inside our existing codebase?: Yes. We work in your repository, your branch conventions, and your review process. The deliverable is reviewable pull requests with tests and documentation, not a black box dropped over the wall.

Ship the AI feature, not the demo.

Send the repository and the use case. You get a written scope, a fixed estimate, and a working feature in your branch.

Request a Quote Order on Fiverr

Fiverr gig: AI developer — Next.js, Node.js, OpenAI API integrations — buyer protection and escrow handled by Fiverr.

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AI Integration for Real Products.

What we build

forum

Chat assistants

Streaming chat grounded in your product data, with conversation history, tool calling, and a fallback path when the model is unavailable.

RAG over your documents

Ingestion, chunking, embeddings, and a vector store (pgvector, Pinecone, Qdrant) so answers cite your own content instead of guessing.

smart_toy

Agents and workflows

Multi-step automation that calls your APIs — triage, enrichment, drafting — with retries, timeouts, and human approval where it matters.

text_snippet

Extraction and classification

Turning invoices, tickets, CVs, or transcripts into structured JSON your database can actually use, validated against a schema.

mic

Speech and vision

Transcription, text-to-speech, OCR, and image understanding wired into existing upload and processing flows.

monitoring

Evaluation and cost control

A fixed question set to measure quality before and after changes, plus caching, model tiering, rate limits, and per-user usage logging.

Stack

Models: OpenAI GPT and embeddings, Anthropic Claude, Google Gemini, open-weight models via Ollama or a hosted endpoint.

Application: Next.js (App Router), React, Node.js, TypeScript or JavaScript, Python where the ML tooling calls for it.

Data: PostgreSQL with pgvector, Pinecone, Qdrant, Redis for caching and rate limits, S3-compatible object storage.

Delivery: your repository, your review process, Docker where useful, deployed to Vercel, Cloud Run, or your own VPS.

Frequently asked questions

How much does it cost to integrate the OpenAI API into an existing app?

A single well-scoped feature — a chat assistant, a document summariser, a classification endpoint — typically lands in the low four figures. Cost is driven by how much of your data has to be prepared, not by the API call itself. You get a written estimate after a short technical review, before any code is written.

How long does an AI integration take?

A contained feature in an existing Next.js or Node.js codebase usually ships in one to two weeks. Retrieval over your own documents, evaluation harnesses, and multi-step agent workflows run longer because the work is in the data and the testing, not the wiring.

Which models and providers do you work with?

OpenAI (GPT and embeddings), Anthropic Claude, Google Gemini, and open-weight models served through Ollama or a hosted endpoint. Provider choice follows the task and your budget — we benchmark two options on your real inputs rather than defaulting to whichever model is loudest that month.

Can you build retrieval-augmented generation (RAG) over our own documents?

Yes. That means a document pipeline, chunking and embedding, a vector store (pgvector, Pinecone, or Qdrant), and retrieval tuned against real questions from your team. We measure answer quality on a fixed question set instead of shipping on vibes.

How do you keep API costs and token usage under control?

Caching for repeated prompts, a smaller model for the easy majority of requests, streaming so users see output sooner, hard per-user rate limits, and usage logging from day one. Most projects cut their initial token bill substantially once the traffic pattern is visible.

Is our data used to train models?

No. Requests go through provider endpoints that exclude data from training by default, keys stay server-side, and we can add PII redaction before anything leaves your infrastructure. If you need a self-hosted open-weight model instead, that is a supported option.

Can you work inside our existing codebase?

Yes. We work in your repository, your branch conventions, and your review process. The deliverable is reviewable pull requests with tests and documentation, not a black box dropped over the wall.