# AI integration for existing products — not a demo chat

I help companies integrate large language models and related AI capabilities into software they already run. The default is still deterministic code. A model enters only where the task is ambiguous enough that probability earns its latency, cost, and error rate. That usually means structured outputs, retrieval over the company's own data, tool use behind validation, and a human path when the happy path does not hold. I have shipped AI-native product surfaces and an enterprise LLM benchmarking platform — the work is about choosing the model against a use case, not decorating a landing page with 'GPT'. If you need a wrapper around a chat API, you do not need me.

## Who this is for
- SaaS teams adding AI to an existing product, not starting an 'AI company'.
- Internal platform teams who want retrieval or copilots on company data.
- CTOs who need a build-vs-buy and model-selection decision before procurement.
- Product companies that already have a prototype and need it to survive traffic and bad input.

## Seven factors before adding an LLM to an existing product
If you cannot answer these, you are not ready to put a model on the request path.
### 1. Task ambiguity
If a rules engine or a form can do it, do not pay for tokens. Models earn their keep on classification, extraction, drafting, and retrieval over messy text — not on calculating tax.

### 2. Cost of being wrong
A wrong summary is not the same as a wrong payment instruction. High-cost errors need validators, dual control, or a human in the loop. Do not 'prompt better' your way around that.

### 3. Data boundary
What may leave the VPC? Customer content, secrets, and regulated records often cannot go to a hosted API. That decision precedes model quality.

### 4. Grounding
If the answer must come from your corpus, you need retrieval and citations, not a larger base model. Fine-tuning is for style and task format, not for facts that change weekly.

### 5. Output contract
Production software consumes JSON, tool calls, and database writes. Free-form chat is a UI, not an interface. Schema, repair, and reject paths are part of the design.

### 6. Latency and cost envelope
A 8-second, $0.04 call in a background job is a different product from the same call on page load. Measure both before you pick a frontier model.

### 7. Operability
You need traces, eval sets, and an owner. A prompt in a dashboard with no tests is not an integration.


Canonical: https://ihar-ivaniuk.com/en/services/ai-integration