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Take OpenAI from pilot to production
FullStack builds OpenAI-powered systems for retrieval, agents, voice, and multimodal workflows—and runs five in our own business. We bring the experience to turn OpenAI capabilities into reliable tools your teams can use every day.
Built for real-world AI
We deliver retrieval-augmented systems, agent workflows, voice interfaces, multimodal applications, and Codex adoption for engineering teams.
We use OpenAI in our own products before bringing it to client environments. When it is the right fit, we build it into the systems that move your work forward. When it isn’t, we’ll tell you.
How FullStack works with OpenAI
We use OpenAI in our own business
Our teams use OpenAI across software development and internal operations to build, deliver, and run more effectively. Five production systems support retrieval, prospecting, voice, client-facing search, and talent matching.
We bring OpenAI to our clients
We recommend and integrate OpenAI when it is the right fit for your business, technical, and transformation goals—and we’ll say so when it isn’t. We work within the OpenAI API or Azure OpenAI Service, based on the environment your organization already approves.
We build expertise through delivery
Our engineers continuously build OpenAI expertise through hands-on delivery. They bring practical experience with evaluation harnesses, cost and latency optimization, and the patterns we have tested, refined, and scaled in our own products.
We invest for the long term
We see this as a long-term relationship built around shared capabilities, market opportunities, and go-to-market collaboration—not a transactional license arrangement.
Five OpenAI systems, running in production
Aria
Aria is FullStack’s voice agent platform, built on OpenAI’s real-time voice models. It handles structured interviews for candidate pre-screening, client qualification, and follow-up calls—adapting the conversation to capture the information it needs. Instead of a raw transcript, Aria delivers a structured outcome summary and CRM-ready record, helping teams scale voice outreach without scaling headcount.
SalesRag
SalesRAG gives revenue teams a natural-language way to find answers across sales collateral, case studies, pricing, and scope documents. It combines OpenAI embeddings, a vector database, and live data from Connect—FullStack’s internal platform—to reduce answer time during active deal cycles and keep global pricing and messaging consistent.
Ask AI
Ask AI turns conversational client requests—such as “a senior React Native developer who has scaled fintech payments”—into precise searches across structured developer profiles. Built with OpenAI embeddings and the Connect database, it also recommends relevant service offerings and team structures, turning talent discovery from days of back-and-forth into a self-serve experience.
SalesNav
A desktop application that pairs OpenAI multimodal models with live web research to profile target accounts — funding rounds, key hires, product launches — and draft outreach grounded in what it found, replacing generic cold outreach with contextual messaging at scale.
Talent AI-Matching
A daily agentic workflow compares candidate profiles with open client requisitions across skills, verified experience, certifications, time zones, and availability—then generates qualified candidate pitches. It helps teams identify strong matches before roles are formally posted.
Built and running in production
Prove the impact before you build
Every OpenAI engagement starts with a fixed-fee assessment or a paid pilot, run on your systems and your data. You'll see the result before you scope a build.
A working pilot in two to four weeks*
One workflow, instrumented, with an eval set built on your data—not a demo on ours.
Measured from a Week 0 baseline
We establish the workflow’s time and cost baseline before OpenAI is introduced, so the results are measurable and easy to report.
Evals before scale, not after
Golden-dataset testing, prompt-injection defenses, PII safeguards, and tracing are built in from the start—because retrofitting them later is how pilots stall.
Built for your team to run
Prompts, evals, configurations and runbooks are handed over and documented. We treat capability transfer as a deliverable, not a favor.
Where OpenAI isn't the right fit
Where the workflow matters more
Most OpenAI projects stall because of data access, evaluation, ownership, or adoption—not the model. When no one owns the workflow or data is fragmented across systems without clear permissions, we’ll help you address those gaps first. It may take more upfront work than a pilot, but it gives your AI initiative a stronger path to adoption and scale.
Where another stack fits better
For long-running agentic coding workflows, another vendor’s tooling may be the better fit—and we’ll say so. For high-volume, non-interactive workloads, a smaller task-specific model can be faster and more cost-effective than a frontier model. In Microsoft-centric environments, Azure OpenAI Service may be the most practical path, even when the direct API is technically cleaner.
Where we draw the line
We don’t resell licenses or promise headcount reduction. Instead, we build automation that absorbs volume and removes repetitive work. Additionally, while we don’t run your security review, we deliver assessment outputs designed to support it.
Explore FullStack's OpenAI implementation services
From a focused retrieval pilot to a governed AI agent ecosystem, FullStack designs, builds, and operates OpenAI-powered systems on the infrastructure your organization already trusts. Each service delivers value independently—and together, they create a unified delivery model built to scale.
- Enterprise rollout and adoption programs for ChatGPT Enterprise and OpenAI-powered internal tools.
- Agent development on the OpenAI Agents SDK, with bounded autonomy and human checkpoints.
- Multimodal and computer-vision workflows for document, image and screen understanding.
- Voice agents and telephony workflows on the Realtime API.
- Retrieval-augmented systems on OpenAI embeddings and vector search.
- Fine-tuning, distillation and model selection across the OpenAI model family.
- Codex adoption across engineering teams—configuration, CI integration, enablement.
- Deployment on the OpenAI API or Azure OpenAI Service to match your compliance posture.
- Evaluation harnesses, golden-dataset regression suites, tracing and cost-per-outcome instrumentation.
Move the numbers that matter with OpenAI
OpenAI inside a governed estate
Production OpenAI without a platform team
Common questions about working with OpenAI
Is FullStack an OpenAI partner?
FullStack builds on OpenAI across our own products and client systems, and invests continuously in OpenAI expertise. We are not currently claiming a formal partner tier.
What does an OpenAI implementation actually involve?
Picking a workflow narrow enough to finish, measuring what it costs today, building it with an eval set on your data, wiring in permissions and tracing, and handing over the runbook. The model call is a small fraction of the work. Integration, evaluation and adoption are the rest.
Can we use OpenAI without our data leaving our cloud?
Azure OpenAI Service runs inside your existing Azure agreements and data-residency boundaries, which is how most regulated clients deploy. The direct OpenAI API is often cleaner technically; the deciding factor is usually your compliance posture, not the engineering.
How do you build a voice agent that doesn't frustrate people?
Bound the goal, script the escalation, and measure task completion rather than call minutes. Aria—our own voice platform on the Realtime API—adjusts its path to collect required data points, then hands off to a human at defined checkpoints. Latency and interruption handling matter more than model choice.
How do you stop a RAG system from confidently making things up?
Retrieval quality and evaluation, not prompt wording. That means permission-aware retrieval over real sources, citations back to the source document, a golden-dataset regression suite that runs on every change, and tracing so a wrong answer can be properly diagnosed.
How long before we see something working?
A scoped pilot with a working eval set takes two to four weeks on average. The most common reason that might slip are data access approvals, rather than engineering.
Who owns the system when the engagement ends?
You do—you'll own all the code, prompts, evals, configurations and runbooks. Capability transfer is written into our engagement, because a partner who leaves you more dependent has sold you the wrong thing.
Do you also work with Anthropic?
Yes. FullStack builds on both stacks, which is why we can tell you which one fits a given workload rather than which one we're incentivized to sell. See our Claude implementation partner page.
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