YarnAI — the AI-native method for shipping better digital products, faster.

YarnAI is how Loomery teams put senior product, design and engineering judgement in charge of AI agents, end to end, so clients get speed without losing control of what ships.

THE GAP

Everyone's using AI. Almost nobody's changed how they work.

Unstructured AI usage
AI gains stay stuck with whoever's using it, they never show up in delivery speed.
The plan, the review and the accountability still run on the old operating model.
Nobody senior trusts the output enough to skip the old checks anyway.
More AI tools, but not a faster or safer team.
With YarnAI
Warp and Heddle put the same context and skills in front of the whole team, so gains compound.
Senior product, design and engineering judgement directs every agent output, end to end.
Guardrails and evaluations earn trust before agents get more autonomy.
A team restructured around what agents are good at, with senior makers owning the outcome.
What it is

YarnAI is Loomery's proprietary method for harnessing the power of AI and agents

YarnAI is Loomery's method for running AI agents across a whole project, not just inside a code editor, putting senior judgement in charge of what agents draft so clients get speed without losing control of what ships. Six parts make up the stack; Warp, Heddle and Bobbin, named after parts of a loom, are the core of it.

Skills icon
Skills

Packages of instruction & code

A growing library inside Heddle covering discovery standards, delivery fundamentals, engineering patterns and more, ready to reuse or adapt to your organisational context.

Method icon
Methodology

Our AI-native processes & workflow

A set of rules and steps we've codified for how people and agents work together: always name an owner, always show the reasoning, never ship what nobody reviewed.

Accelerators icon
Accelerators

Re-usable artefacts

Prompt libraries, architecture scaffolds, test templates and decision frameworks, so no project starts from a blank page, plus the watchouts we've learned the hard way: where agents overreach and where review can't be skipped.

Principles

The principles behind YarnAI

01

Judgement directs, AI executes

AI accelerates the first 70% of the work. Craft and judgement own the last 30%, the part a model can't bring.

02

Guardrails before autonomy, every output earns trust

Tests, CI and static analysis come before agent freedom. We review the commits, not the summary, and bin mediocre work rather than ship it.

03

Codify context, don't just prompt

Standards and decisions get written down as machine-readable context, so agents and teammates act on them reliably instead of everyone re-explaining from scratch.

04

Evidence over vibes

We validate with real evals, not gut feel, treat agents as a genuine trust and security surface, and stay cost-aware about what's earning its keep.

Getting there

Our AI maturity framework

We use this framework across Loomery to make sure YarnAI stays embedded as capability grows, whatever stage a team is starting from. We help clients apply the same framework to their own teams.

Level 01
Co-pilot / chat
Give context each time
Use for Q&A
Discrete tasks
Copilot, Claude etc. · Gemini in Docs, Granola Chat
Level 02
Repeatable tasks
Shared, saved context
Largely Q&A or simple actions
Discrete tasks
Gems, Skills, Projects · prompt libraries · limited integrations, mostly out-of-the-box
Level 03
Smart workflows
Chain of tools with linked context
Takes actions, not just answers
Multi-step processes
Claude Co-work/Code, Gemini CLI, Codex · connectors: Slack, Jira, Figma
Level 04
Autonomous agents
Proactive workflows
Agents discover tools you didn't specify
Parallel agents, co-ordinating
Claude Code, Gemini CLI, Codex · OpenCode, Pi.dev, open models · inter-agent comms
Across the lifecycle

Where YarnAI fits in the SDLC

YarnAI runs through every stage of a project, not just when the code gets written. At each stage, agents produce the first draft and a Loomer owns the decision.

Discover stage icon
Discover
Agents draft:research synthesis, competitor scans, opportunity maps.
Loomer owns:which problem is actually worth solving.
Design stage icon
Design
Agents draft:architecture options, prototype variants.
Loomer owns:the trade-offs that decide direction.
Build stage icon
Build
Agents draft:code, first-pass documentation.
Loomer owns:what actually gets merged.
Test & review stage icon
Test & review
Agents draft:test suites, security and quality checks.
Loomer owns:sign-off and accountability.
Ship stage icon
Ship
Agents draft:release notes, deployment checks.
Loomer owns:the call to go, and the fallback.
Learn stage icon
Learn
Agents draft:usage analysis, what changed.
Loomer owns:what happens next, fed back into Warp.
A deepdive

Warp · the project brain

A closer look at how Warp turns scattered tools into one shared, working context, kept current automatically.

See Warp in action >
Our tools · via MCP
Granola
GitHub
Google Drive
Figma
Miro
Linear
Slack
Ingest
Shared context repo

Distilled context, in Markdown

the project's knowledge, written down

A map of where knowledge lives

doc IDs, repo names, the right sources

Shared & version-controlled (Git)

clone it, get the full project history

Claude works inside the repo
Output
What you get

Answers

cross-referenced across every source

Code & architecture

decisions grounded in real context

Delivery planning

story maps & estimates from Linear

Write-backs to your tools

always draft-first — you approve

On repeat ↻

Every session, Claude checks Granola, Slack and Linear for what's new and proposes updates, you approve.

Warp stays current as a side-effect of working, not a doc someone has to remember to maintain.

THE TEAM SHAPE

From cross-functional team to product pair

YarnAI changes the shape of a delivery team, not just its tools.

Product counsel · on call
Specialist craft leaders, supporting and guiding judgement calls.
supporting ↓
Product pair · full time
Product Engineer + Strategic Designer
Collaborating with you to deliver value at pace.
directing ↓
Agent fleet · 24/7
Multiplying impact, supported by an intelligent shared context layer.

Close the strategy-execution gap

Get to making, fast.

Deep craft, without the big team

Specialist judgement on call.

Context that's built and kept

For the product and the org.

Controlled, predictable investment

One fee, not a headcount.

Working with us

How clients engage with YarnAI

Three ways in, depending on what you need.

AI-native product pair

A product engineer and a strategic designer join your existing team as a full-time pair, directing AI agents inside your codebase and your organisation.

Directs the agents drafting inside your codebase, day to day
Brings senior product and design judgement into your existing team
Ships alongside your team, inside your systems and your sprints
BEST WHENYou've already got the wider team in place and want the method and the judgement, not extra headcount.
Talk to us about a pair →

YarnAI enablement

Loomery sets up YarnAI, the tool stack and the methodology, inside your organisation, and trains your team to run it themselves.

Stand up the tool stack, workflows and guardrails inside your organisation
Train your team to run YarnAI's method themselves
Hand over a repeatable way of working, not a one-off project
BEST WHENThe goal is building the capability in-house, not renting it.
Talk to us about enablement →

Agentic pod

A full Loomery team, product counsel, multiple product pairs, and a 24/7 agent fleet, runs YarnAI end to end: strategy, design, build, and launch.

End-to-end ownership, strategy through launch, not just a slice of it
Multiple product pairs plus a 24/7 agent fleet at your disposal
Product counsel on call, guiding the judgement calls throughout
BEST WHENYou need the whole thing built at pace and don't have the team in place yet.
Talk to us about a pod →
Evidence

How YarnAI helps our clients move faster

10 days
to ship something and start learning
100 days
to a working Alpha in users' hands
Vorboss channel-partner portal, rebuilt with Loomery
Vorboss · Utilities
3x faster

Kick-off to live in a week

Rebuilt the channel-partner portal with AI-native delivery: a working demo in a day, live product in a week, roughly three times the pace of a traditional build.

View the case study →
University of Exeter campus
University of Exeter · Higher education
60 days saved

Months of build, down to days of setup

A university-wide agent platform on AWS with reusable templates and single sign-on, cutting the time to launch a new agent from months of development to days of configuration.

Bite Engineering AI orchestration platform
Bite Engineering · Construction
Team 50% smaller

Alpha to revenue-ready in twelve weeks

Rebuilt an AI orchestration platform for structural engineers, taking a rough-and-ready alpha to revenue-ready software in twelve weeks.

200+
AI-native specialists
Certified Developers
Consultative Solutions Practitioner
Certified Gen AI Developer