◆ THE AGENT FABRICATOR
Native eXecution Runtime · run on your hardware

Own your
intelligence.

Describe what you want in one plain sentence — Avery fabricates a deterministic agent that's private, auditable, and yours. Not rented by the token.Local-first • Deterministic • No-code • Cheapest-model routing • Governable
Get Started FreeDownload for MacGet Started FreeDownload for WindowsGet Started FreeDownload for Linux
See it in action — watch the product demos →Need to deploy on premise? Schedule a call →

Agents

Make a new one
Describe what you want to automate, in your own words…
Plan itIt drafts a plan first — nothing is built until you confirm.
Your agents
Melanie
US Market News Digest Agent

Collects the latest US market news and writes a concise, readable summary…

Connect it
Soraya
Prospect Anchor Brief Builder

Turns a single prospect into a researched 3-page brief and outreach note…

Ready
KAIROS Equity
Independent Equity Analyst

Builds an auditable, multi-source world view of a US stock and values it…

Needs your help
Start from a template
Sam
Chief of Staff Agent
ScheduledGoogle sign-inApproval-gated
Mira
Client Reporting Agent
ScheduledPer-client loopWeb search
Vera
Records Analyst · on-device
On-deviceZero egressFile upload

Soraya

Prospect Anchor Brief Builder
▶ Run
Fast & thriftyBalancedBest quality
BuildConnectionsKnowledgeRunsAppsActivity
I want an agent that prompts me for the person name, role, LinkedIn and company URL — then researches the profile, the company news and priorities, and picks one specific problem we can uniquely solve…
Avery · Reading your description and drafting the plan…
Taking shapev3.0
Starts when you ask to build a briefstart
Shows a form: name, role, linksscreen
Searches the web for recent newsconnection
Picks the ONE deep problemdeep thinking
Writes the 3-page narrativedeep thinking
Lays it out as a 16:9 deckcalculation

Connections

The services your agents can use — sign in or add a key once, reuse everywhere.
Name a service it should work with (e.g. QuickBooks)…Find the best way
Connected 11
T
Tavily
Not used by an agent yet
● ConnectedTest
◷
A clean browser
No sign-ins
● Connected
31
Google Calendar
Not used yet
● Sign in needed
R
Email · Resend
Not used yet
● ConnectedTest
Q
Web Search · Tavily
Used by 6 agents
● ConnectedTest
P
PDF.co
Not used yet
● ConnectedTest
Available
⌹
Files & Folders
Read/write approved folders
Connect
M
Gmail
Read & send mail
Sign in
#
Slack
Post & read channels
Sign in

Settings · Local models

Ollama models
Larger / always-on models that run as a local server.
Llama 3.2 1B ~1.3 GB · Runs great here · classify, extract, summarizeDownload
Qwen 2.5 3B ~1.9 GB · Runs great here · classify, extract, draftDownload
Qwen 2.5 Coder 7B ~4.7 GB · Runs great here · classify, extract, codeDownload
DeepSeek R1 7B ~4.7 GB · Runs great here · summarize, draft, reasonDownload
Who does what
Route a kind of work to a model — or leave it on “Choose the best”.
Sorting things into typesChoose the best ⌄
Pulling details out of documentsChoose the best ⌄
SummarizingChoose the best ⌄
Writing draftsChoose the best ⌄

Spending

TodayThis monthAll time
Total
$264.53
building $242.75 · running $21.77
By engine
claude-opus-4-8$179.18
claude-fable-5$48.05
claude-sonnet-4-6$26.97
local: *$0.00
By build stage
repair105× · $117.08
graph100× · $85.34
run754× · $21.77
node-gen203× · $18.04
Budgets
Per run, each agent ($)
5
Everything, per month ($)
300
Each build session ($)
15
When a budget is reached, runs pause and ask before spending more.

Knowledge

What Avery has learned across all your agents and apps — applied to every future build.
Search what it learned…
What it learned while building
Fixes it worked out during builds — applied automatically so it never repeats them.

A knowledge node with no indexed source returns empty — re-mechanism to a real read

It was configured with topics over Avery source pages, but nothing was indexed, so the brief core claim was left ungrounded.

seen 1× · applied 0×

For a “make the PDF a beautiful presentation” request, render with a real PDF library, not raw bytes

Flat raw-PDF streams read as one wall of prose; a vector library gives the layout control a slide-deck look needs.

seen 1× · applied 0×

Frontier model nodes must leave the model blank for platform resolution, never the literal “auto”

A pinned config rejected with a 404; every other node left it blank so the platform picks a valid installed model.

seen 1× · applied 0×

Skills

Skills help Avery build better agents and apps — it suggests one only when a build truly needs it.
Search for a skill (e.g. PDF, spreadsheets, a tool name)…Search
Installed
Pdf curatedhelps buildtrusted

Read, create & review PDFs where rendering and layout matter.

Remove
Pptx curatedhelps build + recreatestrusted

Create & edit PowerPoint (.pptx) decks — slides, layouts, speaker notes.

Remove
Find-Skills curatedtrustedbuilt-in

Helps discover & install skills when a build needs new capability.

Remove
Recommended
Word Documents trusted

Create, read & edit Word (.docx) with formatting & tables.

Install
Spreadsheets trusted

Open, edit, compute & format Excel (.xlsx/.csv).

Install
MCP Builder trusted

Scaffold & reason about Model Context Protocol servers.

Install

Apps

↧ Import app
Compose a new app
Describe what the app should do and which agents it uses — then watch it build, screen by screen.
An IDP app for freight forwarders: upload shipping documents, track status, review extracted fields beside the document, edit to update the CargoWise XML, and download it…
Soraya
Build the app
Prospect Anchor Brief Builderauto

Turns a prospect into a researched 3-page infographic brief.

1 screenReadyOpen
Why now
The agent era's trust problem
The reckoning

Everyone is racing to deploy agents. Most of them will fail.

The work most worth automating — your research, your numbers, your customers, your judgment — is exactly the work you can't hand to a public model's API. So the highest-value automation stays on the table, while the agents that do ship are cloud-only, metered by the token, and non-deterministic. The category has a trust problem.

40%+
of agentic AI projects will be canceled by 2027
Killed by escalating cost, unclear value, and inadequate risk controls — not by a lack of ambition.
Gartner, 2025
57%
cite data privacy as the #1 inhibitor to AI adoption
The cloud is exactly where sensitive work can't go. 55% of enterprise inference now runs on-prem — up from 12% in 2023.
IBM · industry survey
8×
more to route every step to a frontier model
$18.40 vs $2.31 per million tokens. Blended AI cost fell 67% in a year — yet total spend still tripled. The meter never sleeps.
2025–26 enterprise cost data
The turn

Avery is built for the exact reasons agents fail: cost, reliability, privacy, and governance — by running on your hardware, compiling to a deterministic graph, and reaching for a frontier model only when a step truly needs one.

Zero learning curve
One sentence → a working agent
What Avery does

Describe it. Avery fabricates it.

You don't build the agent. You describe the outcome — and Avery researches it, specs it, compiles a deterministic workflow, wires the connections, and tests itself on your real data until it passes. Under 30 minutes. Zero concepts to learn.

1

You say it in plain language

Avery asks one or two clarifying questions — tone? escalation? — then nothing else.
"Every Monday, pull unpaid invoices, draft polite chasers personalized by client history, let me review, then send and log outcomes."
2

It drafts a readable spec — and an agent with a face

Objective, steps, what it needs access to, what it will learn. You tap Looks right.
IrisInvoice Chaser · QuickBooks · Gmail
READY
3

It builds the workflow and tests itself

Rules filter, code does the date math, an on-device model drafts, a review surface waits for you — and it runs build-test cycles on real samples until its own checks pass.
filter rules→on-device draft→you review→send→log
4

You review the real output — and ship it as an app

Drafted emails with one-line plain-language reasoning. Your edits become feedback the agent learns from. Share it with the team as a clean app — they never see a node, a prompt, or a token.
✔ 12 chasers drafted· on-device model · $0.00 · awaiting your approval
See it work
The studio build experience
Never a black box

You watch it build itself.

As Avery fabricates your agent you see every message and every step take shape — in plain language. Change your mind? Just type. One sentence steers the build, live.

Iris · Invoice Chaser● Building · cycle 1
YOU
Every Monday, pull unpaid invoices, draft polite chasers personalized by client history, let me review, then send and log.
AVERY
On it — two quick questions: what tone for the chasers, and escalate after how many unpaid weeks?
YOU
Friendly but firm. Escalate after 3.
AVERY
Here's the plan — I'll build it and test on your real data.
Objective Chase unpaid invoices every Monday
Needs access QuickBooks · Gmail
Learns which wording gets clients to pay
Taking shapev1 · 6 steps
Every Monday 9am
trigger
Find unpaid invoices
rules
Days overdue
code
Draft chaser
on-device model · setting up…
You review
your approval
Send & log
action
The whitespace
Where every other platform stops
How Avery is different

No-code. Local. Deterministic. The others make you choose.

Developer frameworks are powerful but demand engineers. Cloud no-code agents are easy but shallow, metered, and locked in someone else's cloud. Research labs prove what's possible but ship nothing you can run. Avery is the only platform that's all four at once.

CapabilityDev frameworksLangChain · CrewAICloud no-codeCopilot · Lindy · ZapierResearch labsSakana et al.Avery
No code — plain language✕✔✕✔
Runs on your hardware · private~✕✕✔
Deterministic & auditable✕✕✕✔
Cheapest-model routing (on-device first)✕✕~✔
Ensemble cross-checking on demand✕✕✔✔
Self-heals · tests on your real data~~✕✔
You own it — no per-token meter~✕✕✔
✔ full~ partial / DIY✕ not offeredCategory posture, not a feature-by-feature audit of any single product.
The one-liner

Avery gives the people closest to the work agentic automation they can build, trust, run privately, and afford — without an engineering team or a cloud bill that scales with success.

The wow list
Everything that's only Avery
What makes it feel like the future

Unlike anything you've used.

Twelve things Avery does that other agent platforms don't — roughly in the order people say "wait, it does that?"

01

It builds, tests & ships itself

Describe it in a sentence. Avery researches it, writes a deterministic workflow, tests on your real data, and self-repairs until it passes.

The Fabricator
02

It runs on your hardware

Private by default, air-gapped ready. Your data and your models never leave your machine unless you grant it.

Local-first
03

The right brain for every step

Cloud, Ollama, or in-browser WebLLM — routed to the cheapest model proven reliable, cross-checked when it must be right.

Wisdom of the models
04

Deterministic where it counts

Not everything needs an LLM. Avery writes plain code and rules for exact logic — even whole connectors.

05

Learns & heals itself

From its own errors, your feedback, and which model wins at which task — no prompt-wrangling.

06

Auditable & repeatable

A plain-English trail of every step and cost. Same input, same output.

07

Apps, not just agents

Orchestrate many agents — direct or via a conductor — behind a beautiful UI.

08

Shareable as a file

Export any agent as a signed .avery — someone else imports, installs, runs.

09

Approval-gated by default

Every external access needs your explicit yes on the first run. Revocable, logged.

10

Budgets to the dollar

Spend caps per build, per run, and per agent. The meter never surprises you.

11

Skills → deterministic

Hand it a skill; it compiles it into a repeatable, deterministic workflow.

12

Teach it your world

Review its work and add knowledge; it absorbs your corrections and improves.

Wisdom of the ensemble
The right brain for every step
The secret sauce

Orchestration is the new way to scale AI. We made it legible.

Instead of waiting for one bigger model, combine several specialised ones — route each step to the right model to match the best frontier model at a fraction of the cost, and bring several together to beat any single one. Avery builds that wisdom of the ensemble in — where every decision stays cheap, explainable, and reproducible.

Route from measured capability

Avery learns which model is best at each task from your own runs and routes every step to the cheapest one proven reliable — preferring free on-device models.

Verify, then escalate

Each step is double-checked. Avery steps up to a stronger model only when a check actually fails — cheap-first, made safe.

Cross-check what matters

High-stakes steps are run across several models and reconciled — reserved for the steps that must be right.

One simple dial

Every agent gets a plain Fast · Balanced · Best choice. No model names. Anyone can set it.

FastOn-device
BalancedFrontier quality · on-device cost
BestCross-checked

Frontier-quality at on-device cost on Balanced. Better-than-frontier on demand at Best. Every decision explained, reproducible, and learned from your own runs.

How it works
The two-plane architecture
The central idea

The intelligence is compiled out.

Frontier models design the agent at build time. The artifact that runs every day is a fixed, typed, auditable graph that prefers rules, code, and local models — reaching across your network boundary only with your explicit approval. That's what makes runs fast, cheap, private, and the same every time.

Inside your network air-gapped ready · zero egress by default
Build Plane · build-time only
Plain language → a deterministic graph
Researches · specs · generates · tests · self-repairs. Agentic (Claude Agent SDK) at build time only — the agentic intelligence never runs in production.
compiledout
Run Plane · the executor
Rules→Code→On-device→Frontier
The cheapest correct tool first — a frontier model only when a step truly needs one. Deterministic · offline-capable.
Wisdom of the models — route · verify → escalate · cross-check, dialed Fast / Balanced / Best. Grant-checked connectors · append-only audit ledger — every step logged in plain English.
Air-gapped by default

Runs fully offline. No egress to "turn off" — there is none unless you grant it.

Deterministic, not a prompt

Every agent is an inspectable, versioned graph. Same input, same output.

The right model per step

Rules, code, on-device, or frontier — chosen by measured capability and cost.

Every step is logged

An append-only ledger records each step in plain English — auditable end to end.

Pricing
Solo on your machine, or shared on-prem
Plans

Describe it. Avery builds it. It runs on your machine.

Avery turns a sentence into a working agent — it plans the steps, wires your connections, picks the right model for each job, and runs deterministically on your own computer. No cloud middleman, no data exhaust. Free and Pro run solo; Enterprise deploys the on-premise NXR Service across your org.

Free · solo
Free
$0forever

Everything you need to fall in love with Avery, on your own machine — the same engine, no toy mode.

  • Build real agents from plain language
  • 5 agents · 1 composed app
  • On-device AI + 1 frontier provider (your key)
  • 3 connections · 3 scheduled triggers
  • Pre-built template library
  • Encrypted local vault — private by design
Download for free
50 runs / day30-day history
Pro · solo
Pro
$29/ user / month

For builders who live in Avery. The features that turn it from a tool into a workshop.

Everything in Free, plus
  • 50 agents · 10 apps · 20 connections
  • 1,000 runs / day · 4 concurrent
  • Webhook triggers — wire Avery into anything
  • Export, import & share signed templates
  • Third-party Skills (Claude Code ecosystem)
  • Publish agents as MCP tools (Claude Desktop, Cursor…)
  • 3 frontier providers · 180-day history
Upgrade to Pro
Billed monthlyAnnual available
Enterprise · shared on-prem
Enterprise
Contact us

Run Avery as shared, on-premise infrastructure — the NXR Service inside your network, with full governance.

Everything in Pro, plus
  • On-prem NXR Service, shared across your org
  • Unlimited agents, apps, connections & runs
  • Team workspaces & roles (RBAC)
  • SSO / SAML · org policies & guardrails
  • Audit log + SIEM export · org-managed credentials
  • Org-wide shared learning — agents learn together
Talk to our team
64 concurrentUnlimited history

Your data never leaves your machine on Free and Pro — with on-device models, an entire agent can run without a single byte leaving your computer. Switch tiers anytime; your agents, runs, and data stay yours.

You're in good company
Teams already ahead
You're in good company

Join the companies already ahead.

The teams that adopted AI the right way — private, deterministic, and owned — are already pulling ahead of their peers. They build on Avery.

FIFisher Investments
KCKaspar Companies
NCNewborn Caulk Guns
SESummitEdge
CCCulture Collective
+and more
<30mto a working agent
70%of steps run for $0
~60%lower cost / run
95%runs hands-off
100%access audited
Stop renting intelligence by the token

Own your intelligence.

Get started freeDownload for MacGet started freeDownload for WindowsGet started freeDownload for Linux
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