Custom AI vs Off the Shelf AI Tools

The four questions Tepia asks before recommending custom AI

Most companies should buy first. Tepia builds custom AI and still starts every conversation by asking whether a subscription would do.

Four questions decide it.

1. Data sensitivity. Does the AI need to see PHI, financial records, customer PII or trade secrets? Off the shelf tools vary widely in retention and training terms. Custom lets Tepia control what is sent, where it is stored and which vendor signs a BAA.
2. Workflow fit. Does the AI need to act inside your system (create the work order, update the CRM, approve the claim) or just produce text a person copies somewhere? Off the shelf tools mostly do the latter.
3. Differentiation. Is this AI feature something your customers will pay for or choose you over a competitor for? If so, your competitors can buy the same off the shelf tool tomorrow.

If you answer no to all four, Tepia will tell you to buy. If you answer yes to two or more, custom usually wins.

What custom AI from Tepia actually looks like

Custom AI rarely means training a model. Tepia builds on OpenAI and Anthropic model APIs and adds the parts that make them useful for your business: retrieval augmented generation over your documents and database, a vector database for search, tool calls into your existing APIs, guardrails on what the model can do, and evaluation sets that measure accuracy before and after every change.

Typical Tepia builds include a support assistant grounded in your knowledge base that can also look up an order and issue a return, a field technician app that drafts the service report from photos and notes, an intake assistant that extracts structured data from documents into your CRM, and internal agents that reconcile records across systems. See AI integration services, custom AI agent development and AI chatbot development.

Because these run inside apps Tepia builds or maintains, the AI feature ships with the same retention instrumentation as any other feature, so you can see whether it is used and whether it changes outcomes.

How Tepia approaches custom AI vs off the shelf decisions

Tepia applies its six phase process with an AI specific Discovery that is designed to end in a buy recommendation when that is the right answer.

1. Discovery (typically 3 to 6 weeks for an AI feature). Use case scoring against the four questions above, a data readiness review (where the documents and records live, how clean they are, what is sensitive), and a third party review of the off the shelf tools you could buy instead. Deliverables: Investigation Summary, Interview Summary and User Stories, with a written build or buy recommendation and a pilot plan.
2. Design. Wireframes and sample designs for how the AI appears in your product, including failure states, confidence cues and human handoff. Tepia’s design questionnaire and style guide keep the feature consistent with your existing app.
3. Development and Testing. A 6 to 8 week pilot on real data, then Alpha and Beta schedules with functional, user acceptance and non functional testing. For AI that includes an evaluation set of real questions and expected answers, and red team testing for prompt injection and data leakage.
4. Training. Hands on sessions for the people who will monitor outputs, update knowledge sources and handle escalations.
5. Launch. Feature flagged rollout to a segment of users first, with Tepia support reps watching logs and feedback.

Tepia's honest summary

Buy an off the shelf AI tool when the task is generic, the data is not sensitive, you need it this week and you are still learning what AI can do for you. Many Tepia clients run ChatGPT or Copilot internally and a custom feature in their product at the same time, and that is the sensible pattern.

Build custom with Tepia when the AI must work on regulated or proprietary data, take actions inside your systems, serve thousands of interactions a day, or be something your customers choose you for. Tepia’s Discovery ends with a written recommendation either way. Read about AI strategy consulting, browse Tepia’s work and process, or contact Tepia with the use case you have in mind.

Frequently asked questions