How much does ChatGPT app development cost?

A focused ChatGPT integration or an internal assistant grounded in your documents typically costs $25,000-$60,000. A production LLM product with retrieval, evaluation, guardrails, and clean integrations into your systems typically runs $70,000-$180,000 or more. The biggest cost drivers are the retrieval layer and the evaluation harness, not the model calls. Model API usage is often the smallest line in the budget. Ask any vendor to break the quote into retrieval, evaluation, guardrails, and integration so you can see where the real work sits.

How long does it take to build a ChatGPT-powered product?

A grounded internal assistant or a scoped ChatGPT integration takes roughly 8-14 weeks from kickoff. A full LLM product with retrieval, evaluation, guardrails, and multi-system integration takes 16-28 weeks. Teams that lock the data sources, the evaluation set, and the privacy model before writing product code are consistently faster, because retrieval quality and evaluation are where late rework hides.

What is the difference between a custom GPT and a RAG system?

A custom GPT wraps a model with instructions, a persona, and sometimes a small set of uploaded files. It is quick to stand up and good for narrow, low-stakes tasks. A RAG (retrieval-augmented generation) system connects the model to your own knowledge base at query time, so answers are grounded in your current documents rather than the model's training data. RAG is what you need when answers must be accurate, current, and traceable to a source. Most business-grade ChatGPT products are RAG systems, not custom GPTs, because they have to cite where an answer came from and stay right as your data changes.

How do you keep our data private in a ChatGPT build?

A good answer names specifics: which model provider and under what data terms, whether prompts and outputs are retained or used for training, where the retrieval index lives, and how access is scoped per user. It should cover redaction of sensitive fields before data reaches the model, encryption in transit and at rest, and how the design meets GDPR or sector rules like HIPAA where they apply. A red-flag answer treats privacy as a setting added near launch rather than an architecture decision made first. If a vendor cannot tell you exactly what leaves your walls and under what contract, that is the answer.

How should a vendor prove a ChatGPT product actually works, and what is a red flag?

Ask to see a live LLM product, then ask it something it was not prepped for and watch how it handles the edge. A vendor with real experience will show an evaluation harness, a set of test questions with graded answers, and a story about a hallucination or wrong retrieval they caught and fixed. The red flag is a scripted demo that only answers planted questions, or a team that cannot explain how they measure accuracy. If the only proof is a slick chat window, you are looking at an API wrapper, not a product.

Should we use OpenAI models, or should the build be model-agnostic?

Prefer a model-agnostic build. The strongest teams design the retrieval, evaluation, and guardrail layers so the underlying model can be swapped, whether that is an OpenAI, Anthropic, or open-weight model. That protects you from one provider's pricing changes, rate limits, or roadmap. A firm that hard-wires everything to a single model and calls itself a partner is selling lock-in. None of the firms on this list is an official OpenAI partner, and that is not a mark against them. What matters is engineering that keeps your options open.

Who should own the model prompts, retrieval index, code, and data?

You should, from the first commit. Every repository, the prompt library, the retrieval index, the evaluation set, and the cloud and model-provider accounts belong in your name. The prompts and the evaluation set are intellectual property you are paying to create, and they are what make the product yours rather than the vendor's. A firm that hosts your index in accounts you cannot access, or that treats the prompt library as its own, is building a dependency you will pay to unwind. Confirm ownership and an exit plan in writing before you sign.

Should we hire a US, UK, or offshore ChatGPT development company?

Location is the wrong first filter. The right question is whether a firm has shipped a live LLM product with retrieval, evaluation, and privacy handling that matches your risk level. Firms outside the premium US tier, several on this list deliver from Poland, the UK, or Brazil, routinely ship the same quality at a lower rate. What matters is a working product you can inspect, verifiable reviews, and a documented process for evaluation and data handling, not the flag on the office.

Top ChatGPT development companies (August 2026 Update)

Riya ThambirajBuyer's GuideAug 21, 2026 · 14 min read

Short answer

Choosing a ChatGPT development company comes down to whether a firm has shipped a live LLM product with retrieval, evaluation, and guardrails, not just an API wrapper. RaftLabs meets that bar with conversational AI products built since 2015, work across OpenAI and Anthropic models, a 4.9/5 Clutch rating, and fixed-price engagements at $29-$49/hr.

Key Takeaways

Most ChatGPT builds fail on retrieval and evaluation, not the model. A wrapper around an API is easy. Grounding answers in your data, and proving they are right, is the hard part that decides whether the product ships.
A demo that answers three planted questions tells you nothing. Ask to see a live LLM product handle a question it was not prepared for, and ask how the team measures accuracy over time.
The first decision is not the vendor, it is the pattern: a custom GPT app, a retrieval system over your own documents, or a fine-tuned model. Getting that wrong costs more than picking the wrong firm.
Your data leaves your walls the moment a prompt hits a model API. A firm that cannot explain data handling, retention, and provider terms in plain words is a privacy incident waiting to happen.
No firm on this list is an OpenAI partner, and that is fine. What matters is whether they build well across model providers, so you are not locked to one vendor's pricing or roadmap.

Every ChatGPT build starts with a demo that lands and a product that struggles. The demo answers three planted questions and everyone in the room nods. Then a real user asks a real question, and the model invents a policy that does not exist, cites a document that was never uploaded, or confidently gives last year's number. The chat window was the easy part. What decides whether a ChatGPT product ships is the layer nobody sees in the demo: how answers get grounded in your own data, how the team measures whether an answer is right, and what happens to your data the moment a prompt leaves your walls. An API wrapper takes a weekend. A product that stays accurate as your data changes, and can prove it, is a different job. The companies on this list have shipped LLM software where retrieval, evaluation, and privacy were designed first, not bolted on after the first wrong answer reached a customer.

The reason this category is hard to buy well is that every shortlist from a directory search looks the same. Every firm now claims GPT and generative AI, every profile shows a rating, and every sales call opens with the same chat demo. What separates a firm that will ship a working ChatGPT product from one that hands you a rework bill is invisible until you ask the right questions: how they ground answers, how they test for hallucination, who owns the prompts and the index, and what they got wrong on a past LLM build and how they caught it. This guide is organized around those questions, not around logos. We looked at production track record, depth in the areas LLM products actually depend on, pricing transparency, fit with the buyer reading this, and honest limitations, because the wrong-fit firm is more expensive than the more expensive firm.

The eight ChatGPT development companies on this list are phData, RaftLabs, Fusemachines, Kanda Software, Addepto, Xebia, Datatonic, and CI&T. RaftLabs is on this list. We wrote our own entry with the same directness we applied to everyone else.

By 2026, more than 80% of enterprises will have used generative AI APIs or models - Gartner

How we evaluated this list

A buyer's guide is only as honest as its criteria, so here are ours before the companies. We did not rank on rating alone. A high directory score tells you clients were happy, not that a firm has shipped an LLM product that stays accurate under real questions. We weighted evidence of a real, live LLM or GenAI product, discipline around retrieval and evaluation, transparency on data handling and pricing, fit with the reader's profile, and depth in the areas where ChatGPT projects quietly go over budget: grounding answers and proving they are right. Where a firm's rating could not be verified against a live profile during sourcing, we say so and hedge rather than repeat a number we could not confirm.

We evaluated companies on five criteria:

Criterion	What we looked for
Shipped LLM product	A live product using an LLM in production, not a demo or an API wrapper
Retrieval and evaluation depth	Grounding in the client's own data, plus a real way to measure accuracy and catch hallucination
Data privacy discipline	Clear handling of what leaves your walls, provider data terms, retention, and access control
Pricing transparency	A published rate band or a clear, layer-by-layer quoting process
Client profile fit	A track record with buyers who match the reader -- funded startups, growing companies, and enterprises

No company paid for placement on this list.

1. phData

phData is a data-engineering and enterprise-AI consultancy based in Minneapolis, Minnesota, with a focus on DataOps, MLOps, and AI built on modern data platforms. It is a Snowflake Elite partner and was named a Snowflake AI Partner of the Year, external recognition that signals real depth on that data stack. For a ChatGPT product, that data-platform pedigree is the right center of gravity. The model is a commodity call. The engineering that grounds it in clean, current data is the whole job, and phData starts from the data layer up rather than the chat window down.

phData reads as a fit for a buyer whose ChatGPT product depends on a serious data platform underneath it, especially one already on Snowflake or building toward it. Its MLOps discipline matters most after launch, when an LLM product has to stay accurate as data and models change.

The reason an MLOps focus is worth paying attention to, rather than dismissing as jargon, is that the hard part of a ChatGPT product starts after launch. A model gets updated and answers drift. A new batch of documents lands and retrieval quality drops. A prompt that worked for months starts failing on a new question type. A firm that treats operations as a first-class concern builds the monitoring and evaluation to catch these before users do. A firm that treats launch as the finish line hands you a product that decays. Ask phData, or any firm here, how it detects when an LLM product starts getting worse, and listen for whether the answer names specific evaluation and monitoring practice.

Notable work -- phData is a documented Snowflake Elite partner and a named Snowflake AI Partner of the Year; specific ChatGPT client engagements are not verified here. Ask to see a live LLM product and a reference client in your space before signing.

Pricing signal -- $100-$149/hr per its Clutch profile, an upper band consistent with an enterprise data-and-AI consultancy. Request a quote broken into retrieval, evaluation, and integration.

What to watch -- phData is a data-platform and MLOps consultancy, so it fits buyers whose LLM product is a data problem first. A team that only needs a quick custom GPT for a narrow internal task, with no serious data layer, is paying for depth it will not use, and a buyer off the Snowflake stack should confirm the fit.

Best for: Companies building a ChatGPT product grounded in a modern data platform, especially on Snowflake, with MLOps rigor.
Specialization: Data engineering, DataOps and MLOps, enterprise AI
Pricing: $100-$149/hr per Clutch
Clutch: Profile lists no reviews; confirm rating before engaging

2. RaftLabs

RaftLabs is an AI-first tech studio that has built custom software for established businesses since 2015, including clients such as Vodafone and T-Mobile. Its ChatGPT development work centers on the parts of an LLM product that decide whether it survives contact with real users: grounding answers in the client's own data, an evaluation harness that measures accuracy, guardrails against wrong or unsafe output, and clean integrations into the systems a business already runs. Engagements start with a scoped discovery sprint that fixes the data sources, the evaluation set, and the privacy model before a line of product code gets written.

RaftLabs builds model-agnostic. It works across OpenAI and Anthropic models rather than hard-wiring a product to one provider, and it is an independent builder, not an OpenAI partner. That matters because it keeps your product portable. If a provider changes its pricing, rate limits, or terms, the retrieval and evaluation layers stay put and the model underneath can move. One shipped example is a conversational AI research platform for a marketing-tech client, built in a 12-week engagement, that runs LLM-driven interviews and turns them into structured insight.

The reason the discovery order matters is specific to LLM software. Retrieval quality and evaluation are where late rework hides, so RaftLabs treats them as the first architectural decisions rather than settings added near launch. The discovery sprint produces two artifacts before design starts: a data-and-retrieval map that lists every source the product must ground answers in and how it stays current, and an evaluation set of real questions with graded correct answers that the product is measured against on every change. Those two documents are where most of the real cost lives, and pinning them down early is what lets a fixed price hold. It is also what makes the difference on the day the product meets a question nobody scripted. RaftLabs runs discovery precisely so hallucination and stale-answer cases are named while they are cheap to handle, in the architecture, rather than discovered after launch when they mean a customer got a wrong answer.

Notable work -- RaftLabs has shipped 30+ products since 2015 for clients including Vodafone and T-Mobile, evidence of building at enterprise scale with the reliability an LLM product in production demands. Its shipped conversational AI work includes an LLM-driven research platform for a marketing-tech client. It has not published a standalone ChatGPT case study on this list, so ask to see relevant retrieval, evaluation, and conversational AI work directly during scoping.

Pricing signal -- $29-$49/hr with fixed-price engagements and milestone payments, scoped after the discovery sprint that defines data sources and the evaluation set. Fixed-price suits buyers who want a known number before retrieval and privacy complexity is priced in.

What to watch -- RaftLabs owns the full delivery stack, from discovery through architecture, engineering, and delivery, which fits businesses that want one team accountable end to end for a build. A company that only needs a single specialist to augment an internal AI team, or one that wants a throwaway custom GPT for a one-off task, is better served by a staffing engagement or a no-code tool.

Best for: Established businesses building a production ChatGPT or LLM product end-to-end without hiring an internal AI team.
Specialization: Retrieval and evaluation, conversational AI, guardrails, model-agnostic LLM integration
Pricing: $29-$49/hr, fixed-price engagements
Clutch: 4.9/5

3. Fusemachines

Fusemachines is an enterprise-AI products and services firm headquartered in New York and publicly traded on the Nasdaq under the ticker FUSE. It was founded by Dr. Sameer Maskey, whose background is in AI research, and its work spans generative AI extraction, predictive models, and risk and compliance systems. For a large organization, a public company with a research pedigree is a different risk profile from a boutique, and for some buyers that stability is worth a lot.

Fusemachines reads as a fit for an enterprise that wants a ChatGPT product built inside a broader AI program, with the scale and process a public company brings. Its strength in extraction and structured output is directly relevant to the most common enterprise LLM use case: pulling reliable, structured data out of unstructured documents and conversations.

The distinction worth understanding here is between a chat product and an extraction product, because buyers conflate them. A chat product answers questions in a conversation. An extraction product reads documents and returns structured fields you can trust in a downstream system. Many "ChatGPT projects" are really extraction projects wearing a chat interface, and the engineering is different: extraction lives or dies on accuracy measurement and edge-case handling, not conversational polish. A firm with a real extraction practice will push you to define what "correct" means field by field, and will build the evaluation to prove it. Ask Fusemachines how it measures extraction accuracy and handles the documents that break the pattern.

Notable work -- Specific ChatGPT engagements are not verified here. Fusemachines is a documented public enterprise-AI company (Nasdaq: FUSE) founded by Dr. Sameer Maskey; ask for references in your industry and a live LLM product before signing.

Pricing signal -- Pricing is not publicly listed; work is enterprise and program-based. Expect enterprise AI-consulting economics rather than a startup rate band, and confirm scope directly.

What to watch -- Fusemachines is built for enterprise AI programs, not quick single-feature builds. A startup or growing business that needs one focused ChatGPT product on a tight budget may find the enterprise engagement model heavier and pricier than the job requires.

Best for: Enterprises building ChatGPT or extraction products inside a broader AI program, wanting a public-company partner.
Specialization: Enterprise AI, GenAI extraction, predictive and risk models
Pricing: Not publicly listed; enterprise and program-based
Clutch: Public company (Nasdaq: FUSE); directory rating not confirmed -- verify via direct reference

4. Kanda Software

Kanda Software is an engineering firm based in Boston, Massachusetts, with a track record in regulated software, including HIPAA-compliant health and life-sciences builds, and a growing agentic-AI practice. That regulated pedigree matters for ChatGPT products more than it first appears. A team that has shipped software where a wrong record carries legal weight brings the right instincts to an LLM product, where a confident wrong answer can do real harm if it reaches the wrong user.

Kanda reads as a fit for a buyer who wants an engineering firm rather than an AI-only shop, especially one building a ChatGPT product on top of, or connected to, existing regulated systems. Its verified review base gives a buyer a real pattern to read on how it handles delivery.

The transfer from regulated engineering to LLM work is more direct than it looks. In health and life sciences, a team learns that some data is more sensitive than others, that access is contextual, and that every output has to be defensible. Those instincts map straight onto a ChatGPT product, where a prompt might carry protected information, where retrieval has to respect who is allowed to see what, and where an answer may need to cite its source. A team that has already built for that scrutiny will design redaction, access-scoped retrieval, and audit logging as defaults rather than afterthoughts. Ask Kanda how it kept sensitive data out of model prompts on a past build, and judge how cleanly that maps to your risk.

Notable work -- No specific ChatGPT engagement is verified here. Kanda's verified strengths are regulated engineering and HIPAA-compliant builds, with a stated agentic-AI practice; ask how that discipline maps to your LLM product and request a live example.

Pricing signal -- $50-$99/hr per its Clutch profile, a mid-to-upper band consistent with a regulated-engineering firm.

What to watch -- Kanda's strength is engineering with a compliance backbone. A buyer whose ChatGPT product is low-stakes and has no regulatory or integration weight may not need that depth and could move faster with a lighter studio.

Best for: Companies building a ChatGPT product on or alongside regulated systems, wanting an engineering firm with a compliance backbone.
Specialization: Regulated software engineering, HIPAA-compliant builds, agentic AI
Pricing: $50-$99/hr per Clutch
Clutch: 4.9/5 (17 reviews)

5. Addepto

Addepto is an AI and data-science consultancy based in Warsaw, Poland, with a delivery presence in Los Angeles and stated practices in AI for finance and logistics. Its center of gravity is data science and applied AI, which means it comes at a ChatGPT product from the data side first. For a company whose LLM product depends on messy internal data, a firm that leads with data engineering is a shorter path than a front-end studio learning the data layer on your budget.

Addepto reads as a fit for a buyer whose ChatGPT product is really a data problem in disguise: answers that must be grounded in a large, changing body of internal documents or records. Its verified review base gives a buyer evidence the consultancy ships.

The useful test here is whether a firm treats retrieval as an engineering discipline or a checkbox. A weak retrieval layer dumps a pile of loosely related text into the prompt and hopes the model sorts it out. A strong one chunks and indexes documents so the right passage surfaces, keeps the index current as data changes, and measures whether retrieval actually improved the answer. That difference is the gap between a ChatGPT product that feels sharp and one that feels vague and occasionally wrong. A data-led firm is more likely to build the strong version. Ask Addepto to walk through how it built and measured a retrieval layer on a past project, and whether it can show the accuracy difference.

Notable work -- No specific ChatGPT engagement is verified here. Addepto's verified positioning is AI and data-science consulting with finance and logistics practices; ask for an LLM or retrieval reference and a live product before signing.

Pricing signal -- $50-$99/hr per its Clutch profile, a mid-market consulting band.

What to watch -- Addepto is a consultancy that leads with data and AI strategy. A buyer who just wants a small custom GPT built fast, with no real data grounding, will find a data-science consultancy more involved than the task needs.

Best for: Companies whose ChatGPT product depends on grounding answers in large, changing internal data.
Specialization: AI and data-science consulting, applied AI for finance and logistics
Pricing: $50-$99/hr per Clutch
Clutch: 4.9/5 (18 reviews)

6. Xebia

Xebia is an AI-first consulting, software, and training firm with roots in the Netherlands and a US presence in Atlanta, in operation since 2001. It carries a large data practice and delivers generative AI alongside cloud and data modernization. For an enterprise that needs its ChatGPT product to sit on modern data infrastructure, a firm that does the modernization and the AI together removes a handoff.

Xebia reads as a fit for a larger organization building a ChatGPT product as part of a wider data and cloud program, rather than a standalone feature. Its training arm is a genuine differentiator for a buyer who wants their own team upskilled to run the product after handover, not left dependent on the vendor forever.

The point about training is worth dwelling on, because handover is where many AI engagements quietly fail. A ChatGPT product is not done at launch. Someone has to tune prompts, refresh the retrieval index, watch the evaluation scores, and respond when a model update shifts behavior. If the only people who understand the system are at the vendor, you are locked into a support contract for the life of the product. A firm that treats knowledge transfer as part of the job leaves your team able to operate and improve what was built. Ask Xebia what its handover looks like in practice, and whether your team ends up able to run the product alone.

Notable work -- No specific ChatGPT engagement is verified here. Xebia is a documented AI-first consulting and software firm with a large data practice and a training business; ask for a live GenAI product and references in your sector before signing.

Pricing signal -- Pricing is not publicly listed; work is consulting and program-based. Confirm scope and rate directly.

What to watch -- Xebia's strength is enterprise consulting, software, and training at scale. A startup or small team that needs one focused ChatGPT build, fast and cheap, will likely find a large consulting firm heavier than the job warrants.

Best for: Enterprises building a ChatGPT product inside a wider data and cloud program, wanting their own team trained to run it.
Specialization: AI-first consulting, generative AI, cloud and data modernization, training
Pricing: Not publicly listed; consulting and program-based
Clutch: Profile listed; confirm rating before engaging

7. Datatonic

Datatonic is a cloud data and AI/ML consultancy based in London, with a presence in Stockholm, focused on MLOps, generative AI, and LLM operations. It is a repeat Google Cloud Partner of the Year, an external recognition that signals real depth on that cloud's data and AI stack. For a company standardized on Google Cloud, that depth is hard to match with a generalist.

Datatonic reads as a fit for a buyer whose ChatGPT product will run on Google Cloud and needs the data and MLOps layer built to a high bar. Its LLMOps focus lines up with the reality that an LLM product's cost and risk live in operations, not the initial build.

This is the entry to read carefully against your own cloud. A firm with deep partnership on one cloud brings knowledge of that platform's LLM services, data tooling, and cost controls that a cloud-agnostic team would take months to match. That head start is real and valuable if your future is on that cloud. The honest caveat is the mirror image: that depth is tied to one ecosystem. If you are on a different cloud, or deliberately staying cloud-agnostic, an ecosystem specialist's biggest advantage does not apply to you, and you should weigh it accordingly. Recognition on one platform is a strength for the buyers on that platform and a neutral fact for everyone else.

Notable work -- Datatonic is a documented repeat Google Cloud Partner of the Year; specific ChatGPT client engagements are not verified here. Ask for a live LLM product and references on your cloud and in your industry before signing.

Pricing signal -- Pricing is not publicly listed; work is consulting and project-based. Expect a consulting economics model and confirm scope directly.

What to watch -- Datatonic is a cloud data and AI specialist with deep Google Cloud ties. A buyer on a different cloud, or one who wants a full product studio rather than a data and MLOps consultancy, should confirm the fit before committing.

Best for: Companies building a ChatGPT product on Google Cloud that need a high bar on data and MLOps.
Specialization: Cloud data and AI/ML, MLOps, generative AI and LLM operations
Pricing: Not publicly listed; consulting and project-based
Clutch: Profile not verified during sourcing; confirm rating before engaging

8. CI&T

CI&T is a global digital and AI delivery firm headquartered in Campinas, Brazil, that combines design, engineering, and AI for enterprise clients. Where several firms on this list lead with data science or consulting, CI&T pairs product design with engineering, which matters for a ChatGPT product that real people have to want to use. An LLM product that is technically sound but confusing gets abandoned, and adoption is the metric these products are quietly judged on.

CI&T reads as a fit for an enterprise that wants a ChatGPT product designed as a product, with the user experience treated as seriously as the retrieval layer. Its global scale suits a buyer who needs delivery across regions and time zones.

The design point is more than polish. The interface of a ChatGPT product shapes how much users trust it and how they recover when it gets something wrong. A well-designed LLM product shows its sources, makes it easy to correct a bad answer, and sets honest expectations about what it can and cannot do. A poorly designed one hides its reasoning and leaves users unsure whether to believe it, which is how a promising product loses the room in its first week. A firm that puts design and engineering in the same team is more likely to get this right. The caveat is symmetrical to the others: a design-and-delivery firm should be pressed on its retrieval and evaluation depth if your product depends on hard accuracy, not just a good experience.

Notable work -- No specific ChatGPT engagement is verified here. CI&T is a documented global digital and AI delivery firm serving enterprises; ask for a live LLM product and references in your industry before signing.

Pricing signal -- Pricing is not publicly listed; work is enterprise and project-based. Confirm scope and rate directly.

What to watch -- CI&T is built for enterprise-scale delivery. A startup or small team needing one lean ChatGPT build will likely find a global delivery firm heavier and costlier than the job requires; confirm the retrieval and evaluation depth matches your accuracy needs.

Best for: Enterprises building a ChatGPT product where user experience and adoption matter as much as the model.
Specialization: Digital product design, engineering, enterprise AI delivery
Pricing: Not publicly listed; enterprise and project-based
Clutch: Profile listed; confirm rating before engaging

Side-by-side comparison

Company	Primary strength	Typical engagement	Pricing
phData	Data-platform and MLOps depth for grounding LLM products	Data-and-AI consulting build	$100-$149/hr per Clutch
RaftLabs	Retrieval, evaluation, and guardrails built in from sprint one	End-to-end custom LLM product	$29-$49/hr, fixed-price
Fusemachines	Enterprise AI and GenAI extraction, public-company scale	Enterprise AI program	Not publicly listed; enterprise
Kanda Software	Regulated engineering discipline applied to LLM products	Engineering-led build	$50-$99/hr per Clutch
Addepto	Data-science depth for grounding answers in your data	Data-led AI consulting build	$50-$99/hr per Clutch
Xebia	AI-first consulting plus training for team handover	Program build with upskilling	Not publicly listed; consulting
Datatonic	Cloud data and MLOps depth on Google Cloud	Data and LLMOps consulting	Not publicly listed; consulting
CI&T	Design plus engineering for high-adoption LLM products	Enterprise product delivery	Not publicly listed; enterprise

The question that separates AI consultancies from product-build teams

Most buyers compare ChatGPT vendors on rate or review score and get the model wrong before they get the vendor wrong. The real fork on this list is whether you need a product-build team that ships and owns a working LLM product end to end, or an AI and data consultancy that brings deep specialist strength to a program your own team helps run. Picking a firm before you have answered that question is how companies end up with a beautiful strategy deck and no shipped product, or a shipped product with no one internal able to keep it accurate.

Product-build teams, RaftLabs most clearly, and to a degree engineering firms like Kanda Software and CI&T, take responsibility for the result. If retrieval is weak or the product hallucinates, that is their problem to fix, and they hand you a working product with the code, prompts, and evaluation set in your name. That is the right shape when you want a specific ChatGPT product live, owned, and accountable to one team, and you do not have a deep internal AI function to run the program yourself.

AI and data consultancies, phData, Fusemachines, Addepto, Xebia, and Datatonic to varying degrees, bring specialist depth in data engineering, extraction, MLOps, or a specific cloud, often inside a wider AI program. That is the right shape when your ChatGPT product is one part of a larger data or AI effort, when you have internal capability to run alongside the consultancy, or when the hard part is the data and infrastructure rather than the product itself. The strongest of these firms will still ship, but the engagement assumes a more involved buyer.

There is a practical test for which side of the fork you are on. Ask who will keep this ChatGPT product accurate six months after launch. If the honest answer is "the vendor, because we do not have the people," you want a product-build team that ships something maintainable and trains you to run it, or you want a support contract you have priced in. If the answer is "our own AI or data team, we just need specialist help to get it right," a consultancy is the efficient choice. Most companies lean one way clearly once they ask the question. A firm that insists you need a full program when you need one product, or one that promises a product without asking who will maintain it, is selling its own shape rather than solving your problem.

Getting the model wrong is more expensive than getting the vendor wrong. A consulting program with no shipped product is wasted budget; a shipped product no one internal can maintain decays into wrong answers within months. Spend the first conversations on the model and on who will own the product after launch, and the vendor choice gets much easier.

A data point worth pricing in

Andrej Karpathy, the AI researcher and OpenAI founding member, captured why this category moved so fast when he wrote in early 2023 that "the hottest new programming language is English." The line is a joke with a serious edge: natural language became a way to instruct software, and every business suddenly wanted to build with it. The rush produced a wave of demos and a much thinner layer of durable products.

The scale of that rush is worth pricing in. Gartner projected that by 2026, more than 80% of enterprises will have used generative AI APIs or models, or deployed generative AI applications, up from less than 5% in 2023. For a ChatGPT product, though, adoption of the technology is not the same as a product that works. The failure mode is rarely a missing feature. It is an answer that was confidently wrong, a retrieval layer that surfaced the wrong document, or a privacy gap that let sensitive data reach a model under terms no one read. Those are architecture decisions, and architecture decisions compound. When you compare quotes, the cheapest number is often the one that quietly assumes the simplest answer to retrieval, evaluation, and privacy, and the gap only appears once a real user asks a question the demo never covered. The firms that put those three decisions in the discovery phase are the ones that ship a ChatGPT product that stays right, and the quotes that look expensive up front are frequently the ones that have actually priced the hard parts.

The verdict

phData for a ChatGPT product grounded in a modern data platform, especially on Snowflake, with MLOps rigor. RaftLabs for established businesses building a custom ChatGPT or LLM product end-to-end, with retrieval, evaluation, and guardrails designed in from the first sprint and full ownership at the end. Fusemachines for enterprises building extraction or chat products inside a broader AI program with a public-company partner. Kanda Software for a ChatGPT product on or alongside regulated systems that needs an engineering firm with a compliance backbone. Addepto for products whose answers must be grounded in large, changing internal data. Xebia for enterprises who want a ChatGPT product built inside a wider data and cloud program and their own team trained to run it. Datatonic for a product on Google Cloud that needs a high bar on data and MLOps. CI&T for enterprises where user experience and adoption matter as much as the model.

The first filter is the model: do you need a product-build team that ships and owns the result, or a specialist consultancy inside a program your team helps run. The second filter is the specific depth your ChatGPT product needs -- retrieval over messy data, extraction, regulated-data handling, a particular cloud, or design and adoption. Match those two questions to the right firm on this list, and confirm the retrieval, evaluation, and privacy story with a live walkthrough before you sign.

RaftLabs builds LLM and conversational AI products -- retrieval, evaluation, guardrails, and clean integrations across OpenAI and Anthropic models -- with one team accountable from discovery to delivery. No handoff gap. 4.9/5 on Clutch. Talk to a founder about your ChatGPT project.

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Frequently asked questions

: A focused ChatGPT integration or an internal assistant grounded in your documents typically costs $25,000-$60,000. A production LLM product with retrieval, evaluation, guardrails, and clean integrations into your systems typically runs $70,000-$180,000 or more. The biggest cost drivers are the retrieval layer and the evaluation harness, not the model calls. Model API usage is often the smallest line in the budget. Ask any vendor to break the quote into retrieval, evaluation, guardrails, and integration so you can see where the real work sits.
: A grounded internal assistant or a scoped ChatGPT integration takes roughly 8-14 weeks from kickoff. A full LLM product with retrieval, evaluation, guardrails, and multi-system integration takes 16-28 weeks. Teams that lock the data sources, the evaluation set, and the privacy model before writing product code are consistently faster, because retrieval quality and evaluation are where late rework hides.
: A custom GPT wraps a model with instructions, a persona, and sometimes a small set of uploaded files. It is quick to stand up and good for narrow, low-stakes tasks. A RAG (retrieval-augmented generation) system connects the model to your own knowledge base at query time, so answers are grounded in your current documents rather than the model's training data. RAG is what you need when answers must be accurate, current, and traceable to a source. Most business-grade ChatGPT products are RAG systems, not custom GPTs, because they have to cite where an answer came from and stay right as your data changes.
: A good answer names specifics: which model provider and under what data terms, whether prompts and outputs are retained or used for training, where the retrieval index lives, and how access is scoped per user. It should cover redaction of sensitive fields before data reaches the model, encryption in transit and at rest, and how the design meets GDPR or sector rules like HIPAA where they apply. A red-flag answer treats privacy as a setting added near launch rather than an architecture decision made first. If a vendor cannot tell you exactly what leaves your walls and under what contract, that is the answer.
: Ask to see a live LLM product, then ask it something it was not prepped for and watch how it handles the edge. A vendor with real experience will show an evaluation harness, a set of test questions with graded answers, and a story about a hallucination or wrong retrieval they caught and fixed. The red flag is a scripted demo that only answers planted questions, or a team that cannot explain how they measure accuracy. If the only proof is a slick chat window, you are looking at an API wrapper, not a product.
: Prefer a model-agnostic build. The strongest teams design the retrieval, evaluation, and guardrail layers so the underlying model can be swapped, whether that is an OpenAI, Anthropic, or open-weight model. That protects you from one provider's pricing changes, rate limits, or roadmap. A firm that hard-wires everything to a single model and calls itself a partner is selling lock-in. None of the firms on this list is an official OpenAI partner, and that is not a mark against them. What matters is engineering that keeps your options open.
: You should, from the first commit. Every repository, the prompt library, the retrieval index, the evaluation set, and the cloud and model-provider accounts belong in your name. The prompts and the evaluation set are intellectual property you are paying to create, and they are what make the product yours rather than the vendor's. A firm that hosts your index in accounts you cannot access, or that treats the prompt library as its own, is building a dependency you will pay to unwind. Confirm ownership and an exit plan in writing before you sign.
: Location is the wrong first filter. The right question is whether a firm has shipped a live LLM product with retrieval, evaluation, and privacy handling that matches your risk level. Firms outside the premium US tier, several on this list deliver from Poland, the UK, or Brazil, routinely ship the same quality at a lower rate. What matters is a working product you can inspect, verifiable reviews, and a documented process for evaluation and data handling, not the flag on the office.