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94001, USA

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Marcus Lien

LEAD ARCHITECT

AI-native

We build AI-native platforms, autonomous agents, intelligent search, and conversational systems designed for the next generation of software.

THE THESIS

For thirty years, software treated humans as inputs and dashboards as outputs. AI-native products invert that. They do the work, and ask humans only when judgment is required.

We build the products that come out the other side of that inversion: research assistants for engineers, autonomous agents for operations, and generative platforms for creative teams. Each is shipped with retrieval, evals, guardrails and a clear unit economics model.

Core features

A complete AI runtime, not just
a wrapper around a model.

Each engagement composes a subset of these blocks. We commit to ship one of them every week.

Retrieval engine

Continuous evaluation, regression gating, golden datasets, cost & latency dashboards.

14M+ docs tested

Agent runtime

Durable, observable, restart-safe agents with human-in-the-loop escalation built in.

Temporal · LangGraph

Guardrails

Prompt injection defense, PII redaction, jailbreak hardening, content policy enforcement.

Red-team tested

Evals & telemetry

Continuous evaluation, regression gating, golden datasets, cost & latency dashboards.

CI-integrated

Model mesh

Route across Claude, GPT, Llama and custom fine-tunes by cost, latency and capability.

Vendor-agnostic

Fine-tuning ops

Continuous learning loop: corrections, preference data, RLHF, distillation, offline eval.

Closed-loop

In-product UX

A library of AI-native interaction patterns: streaming, citations, undo, drafts, branches.

Open kit

BENEFITS & OUTCOMES

AI that earns its place in
the P&L.

11×

Faster expert workflows

Median time-savings across five clinician and engineer co-pilots, measured at 90 days.

6×

Cheaper inference

Average cost reduction after applying our model-mesh routing and prompt-cache discipline.

14d

First production loop

Median time-to-first-user from project kickoff. Real workflow, real eval, real users.

1%

Hallucination rate

On in-domain queries with retrieval and citations, evaluated against expert-graded golden sets.

$0

Vendor lock-in cost

Model-agnostic architecture from day one. Foundation-model swap takes weeks, not quarters.

Process · 14 – 28 weeks · 5 phases

An end-to-end operating model.

A real operating model, not a giant chart. Every phase ends with a working artifact — not a deliverable on a slide.

1

Discovery & eval design

Week 1 — 3

Map the workflow, define what good means, build the golden dataset. Without evals, the rest is theatre.

Eval harness v1Golden datasetGolden dataset
2

Retrieval foundation

Week 3 — 6

Ingest, chunk, embed, index. Tune hybrid retrieval against the golden dataset. Cite everything.

Retrieval APICitation UXQuality dashboard
3

First production loop

Week 7 — 14

One workflow live in production with real users. Streaming, citations, corrections, telemetry.

Live workflowTelemetryFeedback UX
4

Widen & harden

Week 15 — 22

Expand to 3–6 workflows. Pen test the prompts. Bound cost. Switch to multi-model routing.

Multi-workflowCost governanceRed-team report
5

Continuous learning

Week 23 —

Optional managed operations: prompt iteration, eval growth, fine-tune cycles, model migrations..

Managed evalsQuarterly model reviewDistilled models