# AI integrations

_One well-placed LLM feature_

- Typical scope: Search, extraction, copilots
- Guardrail: Evals from day one

One Claude feature inside your existing product: search, extraction, an assistant. Pointer answers from your database and never computes a number itself.

Most products don't need an agent. They need one feature that uses a model well: search that understands meaning, fields pulled out of documents, an assistant inside a screen people already use.

## What you're aiming for

- A feature users can rely on, with the model behind a boundary you control.
- Answers that are right, and labelled when they are not certain.
- A bill that stays flat when usage grows.
- Freedom to swap the model when a better one lands.

## What we integrate

- Semantic search over your data.
- Document extraction and classification.
- Assistants and copilots inside existing flows.
- Data questions answered from your database, read-only.

## What we've shipped this way

Pointer connects read-only to your database, documents and URLs; the model picks the metric and never computes the number, and exploratory answers are labelled as such with full query lineage. Linear invoices reads closed tickets and renders the invoice. Both are Claude integrations inside ordinary web services.

## How to start

From you: the feature, the data it needs, and who judges whether an answer is right.

From us: the integration, evals from day one, cost controls, and a handover.


---

Hire us: hello@quantflow.studio · https://quantflow.studio/#contact