Frontier EngineeringBook a discovery call

AI-native engineering consultancy

Engineering built for the AI era. Proven in production.

Frontier Engineering embeds senior engineers inside your business to find where AI actually pays, build it the AI-native way, and test it until the numbers hold.

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WORKING WITH

[CLIENT LOGO][CLIENT LOGO][CLIENT LOGO][CLIENT LOGO][CLIENT LOGO]

Practices

Four disciplines. One standard: it works in production.

Each practice stands alone, but most engagements move through all four — from finding the opportunity to keeping it honest after launch.

01

AI-Native Engineering

Rebuild your software lifecycle around agents: agent-ready codebases, spec-driven development, and review pipelines that keep humans in charge.

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02

Forward-Deployed Engineers

Senior engineers who sit with your operators, find the workflows where AI moves a real metric, and ship working software in weeks — not decks.

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03

Model Testing & Evals

Evaluation harnesses built on your data and your tasks, so model choice, prompt changes and upgrades are decisions backed by evidence.

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04

AI-Native Playbook

The practices, conventions and guardrails we use ourselves — taught to your team through workshops, pairing and codified standards.

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How an engagement runs

From first conversation to production, with a gate at every step.

  1. Week 0 — Discover

    Map the work

    Engineers shadow the teams doing the work and inventory tasks by frequency, cost and risk.

  2. [Weeks] — Prototype

    Build the smallest real thing

    A working tool in the hands of real users, against real data — not a demo environment.

  3. [Weeks] — Prove

    Measure against a baseline

    Task-specific evals and business metrics agreed up front decide whether it ships.

  4. [Weeks] — Hand off

    Leave your team stronger

    Code, eval suites, runbooks and the habits to extend them — owned by your engineers.

What we believe

AI-native isn't a tool rollout. It's a different way of engineering.

  • P.01

    Start from the work, not the model.

    The best opportunities are found on the floor, not in a strategy offsite.

  • P.02

    If it isn't evaluated, it isn't done.

    Every prompt, model and agent ships with tests that say when it breaks.

  • P.03

    Context is the codebase now.

    Specs, conventions and docs are written for agents and humans alike.

  • P.04

    Humans own the judgment.

    Agents write more of the code; engineers own design, review and accountability.

Field notes

[CASE STUDY TITLE]

[One-paragraph summary: the client's situation, what the forward-deployed team discovered, what was built, and how it was evaluated.]

Read the case study →
OUTCOME
[METRIC]
TIME TO PRODUCTION
[WEEKS]
EVAL COVERAGE
[VALUE]
INDUSTRY
[SECTOR]

Find out where AI pays in your business.

Start with a fixed-scope discovery sprint. You leave with a ranked opportunity map and a working prototype of the top candidate.