ORACIS
Start a project

AI product engineering

Sheet
A-050
Set
Practice
Status
Issued

Build AI into the product.

LLM features, AI‑native products and multi‑model systems, engineered to run in production with evaluation, cost control and human review designed in. We build them the way we build our own AI engineering system.

Discuss your project

A-050PracticeIssued

An AI engineer marking up printed model answers with a red pen beside a monitor of evaluation scores.

What we build

01

LLM features in your product

Assistants, drafting, extraction, search and summarization, built into your existing app with real latency, cost and failure budgets, not bolted on as a demo.

02

AI‑native products

New products where the model is the core: architecture, data pipelines, prompt and tool design, and the production systems around them.

03

Multi‑model systems

Several models in defined roles that check each other, with deterministic rules where a model's judgment isn't enough. We run this pattern ourselves every day.

04

Evaluation

Test sets and graders that measure your AI feature against the job it has to do, so a model or prompt change is a measured decision, not a hunch.

05

Human‑in‑the‑loop design

Review, attestation and escalation designed into the workflow, so people stay accountable exactly where the stakes are.

06

AI in regulated workflows

Clinical, legal and financial contexts, where the founding team has shipped AI that a licensed professional reviews before anything is relied on.

How an AI feature reaches production

Checked before it answers, with a person where it matters.

How an AI feature reaches production A user request goes to a model call, several models at a capped cost, then to an evaluation that scores it before it ships, then to a human review where the stakes need it, and only then to a response. A call that fails the check takes a dashed path to a safe fallback. User request Model call several models, capped cost Evaluation scored before it ships Human review where the stakes need it Response fails the check Safe fallback How an AI feature reaches production A user request goes to a model call, several models at a capped cost, then to an evaluation that scores it before it ships, then to a human review where the stakes need it, and only then to a response. A call that fails the check takes a dashed path to a safe fallback. User request Model call several models, capped cost Evaluation scored before it ships Human review where the stakes need it Response fails the check Safe fallback
Fig. D-1An AI feature is evaluated before it answers, and a person decides where it matters

Shipped by the founding team

Multi‑model review, with a deterministic safety backstop.

Clinical AI output is checked by several independent models in different roles. Disagreement goes to a clinician, and a rules engine, not a model, decides what reaches a patient. In production today.

Multi‑model review with a deterministic backstop A lab result goes to three independent models in different roles. If they disagree it escalates to a clinician. If they agree, a deterministic rules engine decides whether anything is released to the patient, and can block it. Lab result Interpret Challenge Cross-check agree? disagree → clinician Rules engine deterministic release block Multi‑model review with a deterministic backstop A lab result goes to three independent models in different roles. If they disagree it escalates to a clinician. If they agree, a deterministic rules engine decides whether anything is released to the patient, and can block it. Lab result Interpret Challenge Cross-check agree? disagree agree Clinician human review Rules engine deterministic block release
Fig. W-03No single model output reaches a patient unchecked
Section-by-section attestation AI drafts four sections of a clinical note. A provider attests each one. Three are attested and one is not, so signing stays locked at three of four. AI draft History Assessment Plan Orders Sign note locked · 3 / 4 Provider attests each section · the server enforces the lock Section-by-section attestation AI drafts four sections of a clinical note. A provider attests each one. Three are attested and one is not, so signing stays locked at three of four. AI draft History Assessment Plan Orders Sign note locked · 3 / 4 Provider attests each section · the server enforces the lock
Fig. W-02No blanket approval; each section is attested

Shipped by the founding team

AI drafts. A professional attests every section.

AI drafts clinical notes, and signing stays locked until a provider has attested each section. That's human‑in‑the‑loop as a product feature, enforced by the server.

How we build it

With the same system that built this site.

Your AI feature is engineered through our own multi‑model system: isolated tasks, provider‑enforced budgets, independent review on every change, and an engineer who owns the merge. See how it works.

Close-up of hands typing on a keyboard, code blurred on the monitor behind.

What should your product be able to do?

Tell us the feature or product you want AI to power. We'll tell you how we'd build, evaluate and ship it.

Discuss your project
Next sheet · A‑100 Engineering