Hire OpenAI Developer
Build on GPT-4, Agents, and the OpenAI Platform
Ship production AI features with senior engineers who know the OpenAI stack end-to-end — function calling, Assistants API, embeddings, Whisper, DALL·E, and Realtime.
What We Build with OpenAI
GPT-4 / GPT-4o Applications
Chat, copilots, and structured-output workflows using function calling, JSON mode, and long-context windows.
Agents & Assistants API
Multi-step agents with tools, file search, and persistent threads via the Assistants API and agent SDKs.
RAG with Embeddings
Retrieval pipelines built on text-embedding-3, vector databases, and hybrid search over your private corpus.
Fine-Tuning & Evals
Supervised fine-tuning on curated datasets, DPO, and structured eval harnesses to measure real-world quality.
Whisper Voice & Realtime
Transcription, voice-first interfaces, and Realtime API integrations for low-latency audio applications.
DALL·E & Image Models
Image generation, editing, and brand-safe creative pipelines for marketing and product experiences.
OpenAI Development Services
OpenAI's API suite — GPT-4o, o1/o3 reasoning models, Whisper, DALL·E, and the Assistants API with tool use and retrieval — has become the fastest path from AI idea to production product. But calling an API is not the same as building a reliable, cost-efficient AI system. Prompt engineering, context management, token budgeting, fallback routing, latency optimization, and safe output handling are engineering disciplines, not configuration options.
ABM Tech has been integrating OpenAI APIs into commercial products since GPT-3 was in closed beta. Our engineers have shipped production systems built on GPT-4o for document analysis, contract review, customer support automation, and AI-assisted workflows — with structured output validation, retrieval-augmented grounding, and cost telemetry so clients know what they're spending per user action. We treat OpenAI as a powerful primitive, not a magic button.
The engagements that deliver real ROI combine the right model selection (not every use case needs the most expensive model), a well-designed retrieval layer to keep prompts grounded in your data, and guardrails that prevent hallucination from reaching end users. That's the work we scope, design, and build.
The challenge
Most early-stage OpenAI integrations are held together with string concatenation and hope. They fail in production because prompts drift as models update, token limits get hit unexpectedly, outputs are inconsistent JSON that breaks downstream logic, and costs spiral once real users start hitting the system. The engineering work required to go from 'it works in the notebook' to 'it works reliably at scale' is almost always underestimated.
Our approach
We build OpenAI integrations as proper software systems: typed output schemas enforced via function calling or structured outputs, prompt versioning with A/B test harnesses, embedding-based retrieval layers that keep context grounded in proprietary data, and cost-per-query telemetry from day one. Model selection is deliberate — GPT-4o mini for high-volume classification, o1 for complex reasoning chains — so the unit economics work at your target scale.
The outcome
Clients ship AI features that are observable, testable, and cost-predictable. A typical document processing pipeline runs at $0.003–$0.02 per document with GPT-4o mini and returns structured, validated data — actual spend depends on document length and output schema complexity. Customer support automations with a well-tuned retrieval layer commonly handle a meaningful share of routine tickets without human intervention; the right benchmark is your specific ticket taxonomy, which we evaluate during scoping. You get a system you can monitor, improve, and explain — not a black box.
Free 30-minute technical call — bring your use case and we'll spec the architecture.
Case Studies
Built, launched, and still running.
Selected engagements
Browse all cases→
Healthcare · Sweden
Consent Management System Integration
Digital patient consent platform for Sweden's healthcare sector — BankID-integrated, cutting consent processing time by 86%.
Online Consultation Platform Integration
Healthcare · Telehealth
HIPAA-compliant telehealth platform pairing people with qualified therapists — booking, secure video sessions and progress tracking.
Shopping Platform Creation
E-commerce
Deal and coupon aggregation platform with algorithmic coupon stacking across leading U.S. retailers.
Sentiment Analysis & Trend Prediction
AI & Data Science
Multilingual NLP pipeline reading sentiment, sarcasm and emerging trends across global social channels.
Integrating APIs with the ServiceNow Platform
Enterprise Integration
REST API integration into ServiceNow that replaced error-prone manual workflows with automated ones.
The metrics that follow from shipping with senior engineers
4.9 / 5
Average client rating across platforms
93%
Net Promoter Score
Long-run
Client retention rate
Secure
Type II certified
Pick the engagement that fits
Four ways to work with us — from surgical staff augmentation to fully managed delivery. All models share the same senior-first talent bench.
Dedicated Teams
Full-time engineers embedded in your team for long-running engagements.
Explore Dedicated Teams↗Staff Augmentation
Add senior specialists to an existing team — vetted, onboarded, and up to speed in weeks.
Explore Staff Augmentation↗Project Delivery
Managed fixed-scope projects with a committed timeline and deliverables.
Explore Project Delivery↗Why ABM Tech
What keeps clients past the first delivery.
What clients tell us made the difference, usually somewhere around the second sprint.
Model Selection & Cost Optimization
We match the right OpenAI model to each task — using cheaper, faster models for classification and routing while reserving reasoning-heavy models for complex generation — so your per-query costs are defensible at production scale.
Structured Output Engineering
Function calling, JSON mode, and Zod/Pydantic validation schemas ensure your AI outputs are machine-readable, parseable, and safe to pass downstream — no more brittle string parsing of free-form completions.
Retrieval-Augmented Generation (RAG)
We build vector search layers (pgvector, Pinecone, or Weaviate) that ground GPT completions in your proprietary documents, knowledge bases, and structured data — dramatically reducing hallucination and expanding what the model can answer.
Prompt Engineering & Versioning
Prompts are first-class code artifacts in our engagements — version-controlled, reviewed, tested against regression suites, and evaluated with automated LLM-as-judge scoring so you know when a model update breaks your use case.
Safety, Guardrails & Compliance
Output filtering, PII redaction, content policy alignment, and audit logging are built in — particularly important for healthcare, fintech, and legal applications where uncontrolled AI output creates liability.
Assistants API & Tool Use
We build multi-step AI agents using OpenAI's Assistants API with tool calling — connecting GPT to your databases, APIs, and internal systems so the AI can retrieve live data, take actions, and return results grounded in real state.
Why Teams Choose Us
Security built in, not bolted on
Encryption, access control and compliance designed into the architecture from the first sprint — the way our Swedish healthcare consent platform was built.
Working hours that overlap
Singapore-based engineers who keep to your business day, so standups, reviews and decisions happen live rather than overnight.
Top Rated
Near-perfect satisfaction scores across Clutch, DesignRush, and Manifest.
How we work
Scoping call to production release.
Every engagement is staffed with named people whose only assignment is your build. No shared allocation, no roster of contractors matched to a brief.
Week zero
We start by arguing with the brief.
A working session on the problem, not a requirements hand-off.
The first conversation is technical. We go through the system you have, the constraints you are stuck with, and what would count as this having worked — and we push back where the brief and the goal disagree. The people in the room are the ones who would build it, because nobody else can tell you the architecture will not hold.
A walk through your existing stack, data and integration constraints
Run by the engineers who would staff the build, not an account manager
You leave with a scope, a team shape and the risks named out loud
FAQ
The questions that come up before you start.
Engagement models, pricing, security, and how we staff a project — answered straight, with the detail you would ask for on a first call anyway.
Scoped OpenAI integrations through ABM Tech typically range from $25,000 for a focused single-feature build (e.g., AI-powered search or a document summarizer) to $150,000+ for a full AI product layer with RAG, multi-turn conversation, tool use, and an admin dashboard for monitoring. The biggest cost variable is the retrieval architecture — building and tuning a vector search layer over proprietary data is often 40% of the engineering work. Ongoing OpenAI API spend is separate and depends on usage volume; we model this for you during scoping so there are no surprises.
Keep exploring