What Services Do Gen AI Development Companies Offer?

If you have only heard the phrase "gen AI development company" in the context of chatbots, you are missing most of what these firms actually do. A full-service provider covers everything from picking the right foundation model to keeping it running safely months after launch, and the mix of services you need changes depending on where your project sits.

This matters because the market is moving fast. The global generative AI market is projected to reach $1.3 trillion by 2032, and vendors have expanded their offerings well beyond model access to match that demand. This guide breaks down every core service a gen AI development company typically provides, what each one solves, and how to match them to your project's stage. Let's get into it.

What Services Do Gen AI Development Companies Offer?

Gen AI development companies design, build, and maintain AI systems that generate text, code, images, or structured data using large language models and related technologies. The sections below cover each service in the order most projects typically need them, though not every engagement requires every service.

1. AI strategy consulting

AI strategy consulting identifies which use cases are worth funding before anyone writes a line of code. This process involves understanding business goals, running discovery workshops, and analysing requirements to map realistic AI applications against expected return.

This phase usually produces a prioritised roadmap, not a working system. The limitation is obvious: strategy alone builds nothing, so treat it as the first phase of a longer engagement, not the whole project. It matters most when leadership has several competing AI ideas and no clear way to rank them.

2. Data preparation and governance

Data preparation and governance cleans, labels, and secures the information your model will actually learn from or retrieve. This includes deduplicating records, redacting personal data, and setting access controls so sensitive information never reaches the model unfiltered.

Governance work also covers compliance frameworks aligned with recognised security and privacy standards, which enterprise buyers increasingly require before signing off. Skip this step and even a well-chosen model produces unreliable or non-compliant output. It applies to every project, but the depth of work scales with how sensitive or messy your source data already is.

3. Model selection

Model selection means choosing between proprietary APIs, such as GPT or Claude, and open-source alternatives based on cost, latency, and how much control you need over the underlying weights. This decision shapes everything downstream, including whether fine-tuning is even possible.

The trade-off is straightforward: proprietary models ship faster with less infrastructure work, while open-source models give you more control but demand more engineering effort to host and maintain.

4. Fine-tuning services

Fine-tuning adjusts a pretrained model's weights using your own examples, so its tone, format, or domain knowledge shifts in a lasting way. Techniques like parameter-efficient tuning let vendors do this without retraining an entire model from scratch, which keeps cost manageable.

This service works best when you need consistent behaviour on a narrow task, not when you need access to information that changes weekly. The limitation is cost and rigidity: retraining is expensive, and it does not automatically keep pace with new facts.

5. Prompt engineering

Prompt engineering designs and tests the instructions, examples, and formatting that shape a model's output without touching its underlying weights. This includes structuring system prompts, adding few-shot examples, and refining instructions until output quality and consistency improve.

It is usually the cheapest lever a vendor pulls, and often the first thing tried before committing to fine-tuning or RAG. The ceiling is real, though: prompting alone cannot fix a model's lack of domain knowledge or reliably stop factual errors, which is where the next two services come in.

6. RAG implementation services

RAG implementation connects a model to your external knowledge base through vector databases and semantic search, so it answers using current, verifiable data instead of only what it learned during training. This involves setting up embeddings, chunking documents sensibly, and building the retrieval pipeline that feeds relevant context into each query. RAG suits knowledge that changes often or needs source citations. It is a poor fit when the goal is changing how a model behaves rather than what it knows.

7. AI agent development

AI agent development builds systems that plan multi-step tasks, call external tools, and act with some autonomy, often using standards like the Model Context Protocol (MCP), which lets agents connect to tools and data sources in a consistent way. This service goes beyond a single question-and-answer exchange into workflows that chain several actions together.

Industries are already applying agents to distinct problems across customer support, operations, software development, research, and other workflows. The main limitation is compounding error: a mistake early in a multi-step agent chain can cascade, so strong guardrails and human checkpoints matter more here than in simpler chatbot builds.

8. Custom generative AI model development

Custom model development designs or substantially modifies a model architecture from the ground up for a proprietary use case, rather than adapting an existing one. This is the heaviest-lift service on the list and the rarest, because it demands large training datasets, significant compute budget, and specialist teams.

Rates for this kind of deep engineering work vary widely, running from under $25 an hour for offshore teams to $150 or more for specialised consultancies. Custom model development typically only makes financial sense at scale or where full IP ownership is a hard requirement.

9. Generative AI model replication

Generative AI model replication recreates capabilities similar to well-known systems like ChatGPT or DALL-E, tailored to a client's own technology stack. Companies choose this route for data sovereignty, to control costs at very high query volumes, or to avoid depending on a third party's API pricing and uptime. It demands substantial engineering investment and rarely matches the breadth of a frontier vendor model, so it suits organisations with specific, high-volume needs rather than general-purpose use. This service sits close to custom model development but starts from proven techniques instead of an entirely blank slate.

10. AI model integration and deployment

AI model integration and deployment wires the selected or trained model into your product through APIs or containerised services, then pushes it to production infrastructure. This stage covers cloud platform setup, latency tuning, and techniques like model quantisation to keep response times fast under real user load. It happens after model selection, fine-tuning, or RAG has already been validated in testing. Skipping proper deployment planning is a common cause of pilots that never reach production, since a model that works in a demo does not automatically perform reliably at scale.

11. Enterprise system integration

Enterprise system integration connects the gen AI layer to the business systems you already run, such as CRM, ERP, ticketing, or document management platforms. This is what turns a standalone AI feature into something that actually executes work, letting an agent update a record or trigger a workflow instead of just producing text in a chat window. Some vendors build agentic frameworks that orchestrate multi-step workflows across ERP and CRM systems directly, moving well past simple chatbot functionality. Without this step, even a well-built model stays isolated from the processes it is meant to improve.

12. MLOps and LLMOps support

MLOps (machine learning operations) and LLMOps (large language model operations) cover the pipelines that monitor, version, and retrain models after launch. This includes tracking prompt versions, detecting output drift, and monitoring token costs so spending does not creep upward unnoticed. Some enterprise platforms package this alongside data lakehouse infrastructure and model tooling as a combined offering. Models degrade in accuracy and cost efficiency over time without this ongoing discipline, so treat it as a continuous service rather than a one-off deliverable at launch.

13. Ongoing support, maintenance, and post-deployment care

Ongoing support keeps a gen AI system reliable long after go-live, through bug fixes, scheduled retraining, and continuous performance monitoring. This typically includes service level agreements, security patching, and updating prompts or guardrails as new failure modes and attack patterns emerge. Budget for this from the start of a project rather than treating it as an afterthought, because gen AI systems are not "set and forget" software. They need active maintenance in the same way any production system handling live user data does.

How to match services to your project stage

Match the services above to where your project genuinely sits, not to whichever technique is trending. Early-stage teams exploring feasibility need AI strategy consulting and a data audit before anything else. Teams building a first pilot typically need prompt engineering plus either fine-tuning or RAG, alongside basic model integration. A focused pilot may take roughly 8 to 12 weeks, while a full enterprise rollout can take 4 to 9 months.

Teams scaling to production need enterprise integration, MLOps and LLMOps, and a support contract in place before go-live. For teams that prefer to work with a single partner across these stages, F22 Labs provides generative AI development services covering areas such as RAG, model integration, deployment, and ongoing optimisation.

Conclusion

Gen AI development companies offer a far wider service catalogue than most buyers expect, spanning strategy, data work, model choice, fine-tuning, RAG, agents, integration, and long-term operational support. The right combination depends entirely on your project's stage: early exploration calls for strategy and data groundwork, while scaling calls for integration, MLOps, and ongoing care.

There is no single "correct" stack of services that fits every organisation. A pilot chasing a narrow internal use case needs a fraction of what a full enterprise rollout requires. Before signing with any provider, map your current stage against the services listed here and ask which ones they actually deliver in-house versus subcontract.

Use this breakdown as a checklist during vendor conversations, and confirm each proposed service ties back to a specific outcome you need, not just a capability the vendor happens to sell.

Frequently Asked Questions

What is the difference between fine-tuning and RAG services?

Fine-tuning retrains a model's weights to change its behaviour permanently, while RAG connects a model to external data at query time. Most projects only need one of the two.

Do all gen AI development companies offer AI agent development?

No. Agent development, especially using standards like MCP, is a more specialised service that not every generalist AI vendor provides in-house.

How much do gen AI development company services cost per hour?

Rates range from under $25 an hour for offshore teams to $150 or more for specialised consultancies, depending on region and expertise level.

How long does a typical gen AI project take from strategy to launch?

A focused pilot usually takes 8 to 12 weeks, while a broader enterprise implementation typically takes 4 to 9 months to reach production.

Do gen AI development companies offer support after deployment?

Yes. Reputable providers include ongoing maintenance, monitoring, and retraining as a standard service, since gen AI systems need continuous upkeep after launch.

What is generative AI model replication and when is it needed?

It means recreating capabilities similar to models like ChatGPT within your own stack, usually for data control, cost management at scale, or independence from third-party APIs.