Building a Generative AI Product: What to Know First

Intro

Generative AI is easy to try and considerably harder to turn into a product people can rely on. A team can connect a language model to a chat interface in a few days. Getting that interface to handle real customer requests, respect access permissions, work with company data, and recover from bad answers is another matter entirely.

For businesses considering generative AI for business applications, the first decision is not which model to use. It is whether the problem calls for a new product at all. Off-the-shelf AI tools might cover most of the need, while a custom implementation makes more sense when existing software cannot accommodate the required workflow. Companies evaluating the latter can consult SysGears’ generative AI engineers to explore the technical requirements of a product built around their particular use case.

A generative AI product involves considerably more than putting a model behind a company-branded interface. The model might handle the language, but the rest of the application determines what information it can draw on, whether it can interact with other software, and what happens when its answer misses the mark. Those decisions affect the product's usefulness just as much as the model choice itself.

An internal assistant can mislead employees if it retrieves an outdated policy document. Give it access to customer records, and access permissions become just as important as answer quality. If the application can also change records, a convincing but incorrect response could cause real damage.

These aren't details to patch up after launch. They influence the architecture, the engineering effort involved, and AI development costs, including what it takes to keep the product running. A business should work through them before committing to development, while there's still room to narrow the scope or decide that an existing tool would do the job.

Start With a Problem That Needs Generative AI

A convincing demo can obscure a fairly ordinary business problem. If employees spend too much time searching internal documentation, for instance, a semantic search system may be enough. If they need to interpret long documents, extract specific details, and draft responses using information from several sources, a language model may add something more substantial.

The distinction matters from the outset. Generative AI earns its keep when software has to make sense of messy, unstructured material or draft something that depends on context. For a task governed by fixed rules, though, an ordinary script is often cheaper to run and easier to trust.

Take a distributor dealing with a steady stream of product inquiries. Customers describe the same item in all sorts of ways, so a generative AI application could make sense of their requests, pull up the relevant specifications, and draft a reply. Stock levels are another matter. Those have to come from live inventory records, while a quoted price must follow the company's pricing rules, not whatever figure the model happens to produce.

That division of labor is worth keeping clear: let the model work with language and leave predictable operations to conventional software wherever possible.

Before any development starts, pin down what should actually improve. Sales staff might need fewer minutes to put together a quote; customers might get an answer without waiting for a support agent. Either way, record how the process works now. Otherwise, a team can end up celebrating adoption of its new AI interface without knowing whether it has made the work any easier.

Build vs. Buy: How Much Control Do You Actually Need?

The build vs buy decision is often presented as a choice between complete ownership and convenience. In practice, there are several options in between.

Off-the-shelf AI tools are a sensible starting point when a business needs familiar capabilities such as writing assistance, meeting summaries, document analysis, or general-purpose research. These products usually require less engineering work, and their vendors handle much of the underlying infrastructure. A company can test whether employees find a capability useful without first funding a dedicated development project.

The trouble starts when a generic tool has to fit a process it wasn't designed for. It might not talk to a legacy database, for instance, or apply the right access permissions when employees have different roles. Its output may also need reshaping before another business system can use it. APIs and configuration options can help bridge these gaps, but they won't necessarily cover the quirks of an established workflow.

A custom AI product becomes more defensible when the workflow itself is a competitive advantage, when existing products cannot meet a material requirement, or when the application needs tight integration with proprietary data and internal systems.

A third option is to build your own application around a model hosted by an external provider. The company develops the interface and the logic behind it, hooks the system into its existing software, and sets up its own way to test the results. The model itself is accessed through an API, so there's no need to train a foundation model from scratch. This gives the business room to shape the product around its workflow, though it still depends on the provider's pricing, model availability, and service limits.

That last distinction deserves attention. Developing a custom AI application and developing a new AI model are different undertakings. Training a foundation model requires substantial computing resources, specialized expertise, suitable data, and ongoing experimentation. Most businesses do not need to take on that burden. An existing model, supplemented with retrieval from company documents or carefully designed tool access, may be sufficient.

Choose the Architecture Around the Work

Once the business case is clear, the next challenge is deciding how the application will operate. The model is only one component, and it may not be the component that consumes the most engineering time.

For a document assistant, retrieval-augmented generation (RAG) is often worth considering. Instead of relying exclusively on what a model learned during training, the application retrieves relevant passages from an approved information source and includes them in the prompt. The model then formulates an answer using that material.

Answers grounded in internal policies or current product information can be more relevant, provided retrieval does its job. That is no sure thing. The search might pull an obsolete document, miss a crucial caveat, or hand the model a passage that it then reads incorrectly.

Retrieval needs testing in its own right. How documents are split into chunks can affect what the system finds, and even a good search result is useless if it comes from a source the user shouldn't see. An employee's access rights must carry through to the retrieval process, rather than being overridden because a search happened to surface restricted material. The application itself needs to enforce authorization. A model shouldn't be trusted to make that call.

Fine-tuning serves a different purpose. It can help adapt a model’s behavior, response format, or handling of recurring task patterns when suitable training examples are available. It is not a straightforward substitute for retrieval when the main problem is access to changing facts. A model fine-tuned on last year’s product catalog will not automatically know what changed this morning.

A prompt and a model API may be all a narrowly scoped application needs, at least to begin with. That depends on how predictable the inputs are and what the system is expected to do with its output. If every response needs to be checked against a large body of internal documentation, retrieval may become necessary; for a simpler task, it could just add overhead.

Keep the first version lean and try it on real requests, not just polished demo inputs. See how it handles a vague question or missing information. If it bluffs rather than admitting a gap, that's something to fix before adding more moving parts.