Last updated: 6 October 2026
Quick Answer: OpenAI consulting now comes from OpenAI's own Deployment Company, its four Frontier Alliance consultancies and the 72-firm OpenAI Partner Network. The Frontier Alliance firms are BCG, McKinsey, Accenture and Capgemini. Partner Network firms progress through Select, Advanced and Elite tiers, while independent specialists stay model-agnostic.
OpenAI now sells deployment help itself, which changes who you should ask to build on its models.
Until early 2026, OpenAI consulting did not come from OpenAI: a systems integrator, an agency, or your own engineers with the API reference and a deadline. Two announcements changed that. In February OpenAI signed four large consultancies into Frontier Alliances, and in May it stood up a separate company whose stated job is deployment work.
So the buyer's question is no longer whether help exists. It is which of four routes fits the system you are building, and what each one costs you in portability rather than fees.
Does OpenAI offer consulting services?
Yes. OpenAI sells deployment work directly through the OpenAI Deployment Company, announced on 11 May 2026 as "a new company designed to help organizations build and deploy AI systems". Most OpenAI consulting services reach buyers indirectly, through four alliance consultancies and 72 listed partner firms, where most delivery capacity sits.
The direct route is staffed by forward deployed engineers: engineers who sit inside the customer's environment and build, rather than advisors who produce a target operating model. OpenAI had already put that team to work alongside outside consultancies. Its Frontier Alliance announcement of 23 February 2026 has BCG, McKinsey, Accenture and Capgemini signing multi-year partnerships to deploy AI coworkers on Frontier, OpenAI's platform for AI coworkers, "alongside OpenAI's Forward Deployed Engineering (FDE) team".
What OpenAI does not publish is the commercial shape of any of this. The launch post sets out no engagement terms and no minimum customer spend, so scope and commitment come out of a conversation rather than a published page. Treat valuations or guaranteed returns quoted to you from secondary coverage as unconfirmed until OpenAI publishes them.
What is OpenAI's new enterprise consulting arm?
OpenAI's new enterprise consulting arm is the OpenAI Deployment Company, and in OpenAI's own words it is "majority-owned and controlled by OpenAI". OpenAI calls it a committed partnership with 19 global investment firms, consultancies and systems integrators, led by TPG, with Advent, Bain Capital and Brookfield as co-leads. It is a separate company, not a service line inside OpenAI.
Three details matter when you assess this OpenAI consulting arm.
First, some of its investors also compete for your budget. Bain & Company, Capgemini and McKinsey are named among the backers, and all three sell OpenAI implementation work in their own right. That is not a problem, but the "independent recommendation" framing deserves a question.
Second, the capacity is bought rather than grown. OpenAI agreed to acquire Tomoro, roughly 150 forward deployed engineers and deployment specialists, and says the deal is subject to regulatory approval and expected to close in the coming months. The headcount you are quoted may not yet sit under one roof.
Third, the structure does not change the dependency you are taking on. A deployment built by OpenAI's own engineers is, by construction, a deployment built on OpenAI models. So OpenAI enterprise consulting now has an in-house supplier as well as a partner channel, and both arrive with the same single-provider assumption baked in.
Who are OpenAI's consulting partners?
OpenAI consulting partners fall into three documented groups: the four Frontier Alliance consultancies, the firms listed in the OpenAI Partner Network, and the Deployment Company's own engineers. The fourth route is an independent OpenAI consulting firm outside all three lists, which is usually the option that keeps the architecture portable.
| Route | Who it is | What they do | How you engage |
|---|---|---|---|
| OpenAI Deployment Company | A company majority-owned and controlled by OpenAI, created to help organizations build and deploy AI systems | Embeds forward deployed engineers to design, build and deploy production systems | A diagnostic, then a few priority workflows; no engagement terms or minimum are published |
| Frontier Alliances | BCG, McKinsey, Accenture and Capgemini | Multi-year partnerships to deploy AI coworkers on Frontier, working alongside OpenAI's Forward Deployed Engineering team | Through the consultancy, normally inside a wider transformation program |
| OpenAI Partner Network | 72 firms listed on OpenAI's partner page, progressing through Select, Advanced and Elite tiers | Implementation and integration work, co-selling with OpenAI | Through the partner firm, found via OpenAI's partner locator |
| Independent specialists | Engineering firms outside all of the above | Build the integration, keep the model layer configurable, hand over the running code | Direct, usually project-based or as an embedded team |
Routes and tiers as documented by each vendor on 6 October 2026; links in the text.
A Partner Network firm is the better choice when the work crosses several business units, needs delivery in multiple countries, or has to clear a procurement review that asks for a named partner tier. A two-person shop cannot satisfy those constraints.
What do the Select, Advanced and Elite partner tiers mean?
The OpenAI Partner Network, announced on 14 June 2026, lets firms progress through three tiers, Select, Advanced and Elite, and says partners will later be able to earn specializations in areas such as Codex, cybersecurity and agents. OpenAI also runs a pilot Forward Deployed Experts program. Its stated goal is to train and enable 300,000 certified consultants by the end of 2026, drawing on a list that runs from Accenture down to far smaller firms.
Read the tier for what it is. It measures how far a firm has gone through OpenAI's enablement track, not whether that firm has shipped the thing you need, in your stack, under your security constraints. Those are reference questions, and the tier is not a substitute for them.
Which firms build OpenAI integrations for enterprise software?
The firms OpenAI itself lists for this work include Accenture, IBM, PwC, KPMG, Ernst & Young, Cognizant, Infosys, TCS, HCLTech, Capgemini, Bain & Company, BCG, McKinsey, Booz Allen Hamilton and ZS, with AWS, Databricks and Snowflake listed on the platform side. The authoritative list is OpenAI's partner page, which held 72 firms when we read it.
Two things about that list are easy to get wrong. It is shorter than the roster of firms that market OpenAI work, so a familiar name you expect may not be on it. And it changes, which makes a third-party ranking a poor substitute for reading the page on the day you shortlist.
Beneath the listed firms sits a larger category no directory covers well: independent engineering firms that build model-backed features into existing software. They do not appear on partner pages because they are not reselling anything. You find them through references and a technical conversation. For a product team adding retrieval, agents or document workflows to software it owns, this is usually where the work lands.
Azure OpenAI consulting is a different shortlist again. Those deployments run inside Microsoft Azure, with Microsoft's contracts, identity model and regional footprint, so check Microsoft's own partner listings alongside OpenAI's.
How do you hire a company to connect ChatGPT to internal tools?
Write the scope before you talk to anyone. Teams that open with "connect ChatGPT to our internal tools" get proposals that cannot be compared, because the ambiguity is in the brief rather than the bids. Six items turn a vague brief into a comparable one, and our guide to companies that integrate ChatGPT into internal tools shows a worked version.
- Systems and actions. Name each system, and say whether the assistant reads from it or writes to it. Write access is a different security review.
- Identity. SSO, group mapping and provisioning, plus who is allowed to see which documents. Document-level scoping is the requirement most proposals skip.
- Data access and retention. Which stores are in scope, what gets embedded, where the index lives, and how long anything is kept.
- Plugin governance. OpenAI's help documentation states that the ChatGPT plugin directory is available across ChatGPT plans, and that workspace administrators control which plugins users can enable. Decide that policy before rollout, not after.
- Evaluation. A fixed set of real questions with known good answers, scored before and after each change. Without it you cannot tell a regression from a bad day.
- Operations after launch. Who holds the pager, who updates prompts and connectors, and what the handover artifacts are.
What should you ask before hiring an OpenAI consulting partner?
Six questions separate firms that have shipped this from firms that have read about it. Ask for the partner tier and specializations and verify them on OpenAI's page. Ask for two production references in your industry, running today, with names you can call. Ask how the model provider is configured, and what a swap would involve. Ask where data goes during inference and what is retained. Ask who owns the code, the prompts, the evaluation set and the connectors. Ask who operates the system in month four.
On commercials, expect engagement models rather than a published rate: fixed-price phases, milestone billing, time and materials, or an embedded team. Ranges and the drivers behind them are set out separately in our breakdown of AI consulting cost.
What mistakes should you avoid when choosing an OpenAI consulting partner?
Four recur. Buying a strategy phase when the blocker is engineering, which yields a deck and no running system. Treating a tier as a reference, when it measures enablement. Running the pilot on a sandbox tenant, so the production security review starts from zero. And accepting an architecture where prompts, retrieval and model calls are scattered through application code, which makes every later change a migration.
When should you avoid locking into a single model provider?
Lock in deliberately when the model is the product's differentiator and switching would mean rebuilding it anyway. Keep the provider configurable in the commoner case: the model is one component inside software you already own, and quality, latency or terms may change under you.
In practice the decision is architectural, not contractual. If every model call goes through one internal interface, with prompts, routing and evaluation on your side of it, changing providers is a configuration change plus a round of evaluation. If calls are scattered across services, each with its own prompt strings and response parsing, changing providers is a project. Portability is paid for once, early, and cheaply compared with retrofitting it.
Two decisions sit underneath this. The first is which providers you would actually route to, which our review of OpenAI API alternatives covers. The second is whether to build the layer or hire a firm that already has one, the build-versus-partner question we work through with generative AI development companies.
How Origins AI builds OpenAI and ChatGPT integrations
Origins AI (originshq.com) is a US-based AI-augmented engineering company, and OpenAI and ChatGPT integration work is one of the six blocks on its AI services page, alongside AI consulting, solution architecting, automation, generative AI and prompt engineering, and AI product and model development.
The shape of the work, as the page describes it, is integration into systems that already exist: modern cloud platforms and legacy estates alike, using APIs, middleware and custom connectors. The stack it names includes LangChain, PyTorch, TensorFlow, MLOps pipelines and Kubernetes, which is a build-and-operate list rather than an advisory one. On security it lists controls rather than badges: encryption at rest and in transit, secure authentication, continuous security monitoring, and least-privilege data handling.
Two points matter for comparison against the routes above. Origins AI is not on OpenAI's partner page, so none of the tier signalling applies, and the model layer stays the customer's choice rather than a fixed dependency. Its Domain-Specific LLMs product is the behind-the-firewall end of that choice, trained on a customer's own data rather than called as a hosted API. Origins AI does not publish a rate card; the page sets out engagement models instead, including dedicated teams, project-based contracts, time and materials, and build-operate-transfer.
Talk to an engineer
If you are weighing an OpenAI integration against a model-agnostic build, book a call and walk an engineer through the systems involved.