Our engineering decision rule

We do not force AI into every problem

Fixed rules use ordinary software. Narrow learned tasks may use a focused model. Open-ended language work may use an LLM. We choose only after defining accuracy, privacy, uptime, speed, cost, and maintenance requirements.

Choose the tool after defining the job

Four options, each with a clear reason to exist

The simplest solution that meets the requirement is usually the safest one to operate. A model is justified by measured performance, not by novelty.

Rules and ordinary code

Fixed steps, calculations, permissions, and decisions that must be repeatable.

Most predictable and easiest to test.

Search and retrieval

Finding the right policy, record, document, or approved answer.

Keeps answers tied to known source material.

A focused model

One narrow learned task such as classification, extraction, vision, or prediction.

Useful when measured examples outperform fixed rules.

A general language model

Open-ended language work that genuinely needs broad context or flexible reasoning.

Used only when the flexibility earns its cost and variability.

A worked example

Routing an incoming invoice does not need one model to do everything

A dependable workflow can combine several small, testable decisions instead of sending the entire job to a general model.

Rules confirm required fields and approval limits.

Search retrieves the account policy and matching records.

A focused model classifies varied document layouts when testing justifies it.

A person reviews low-confidence or policy-sensitive exceptions.

Sensitive information travels less

When the work runs inside your business or on the device, fewer private records need to be sent to an outside service.

Important features can work offline

Local features can keep working in a warehouse, on the road, or during an internet outage.

Frequent work can cost less

Using hardware you already control can avoid paying an outside provider for every request, especially at high volume.

We choose the setup that fits the job

Some work belongs on a device, some inside your company, and some in the cloud. We explain the tradeoffs instead of forcing one answer.

Bring the requirement, not a preferred model

We will help separate fixed logic, retrieval, learned tasks, and open-ended reasoning, then explain where each part should run and why.

Review the constraints contact@olib.ai +1 (864) 865-4224