In production today
The productivity floor
just moved.
Memory remembers. Anneal directs.
No new app to open. Anneal works inside the AI tools you already use — Claude Code, Codex, Gemini, VS Code. Context that travels with you, workflows that compound, and an intelligence layer that steers every agent — not just recalls context for it.
A full agency engagement,
run in one session.
Research, brand voice, positioning, five pages of copy, a real build, an art-direction pass, a four-buyer executive study, and multiple full-site design directions — for a named client, Covington Place Partners. End to end. The same deliverable set an agency scopes across most of a year.
Human agency — effort-hours
~1,070 – 2,660 hrs est.
$160k – $400k · 7–9 months
Anneal — one working session
~2 hrs agent wall-clock
3–5 hr human window · same deliverable set
4 AI personas reacted to actual rendered screenshots — not wireframes. Unanimous consensus. (Simulated study — methodology disclosed in the receipt.)
3 full-site designs built simultaneously, not sequentially. A palette-swap misfire was caught and escalated before it shipped.
Copy grounded in 4 Big-4 analyst reports + 6 research scans — all ingested and indexed in session.
Anneal directed every turn — pre-classifying intent, enforcing standards, steering how the work got done. The intelligence layer decided how. The model executed.
13 phases · agency benchmarks cited · methodology shown honestly
What happens before
the model sees your prompt.
Anneal doesn't hand your prompt to a model and hope. Every turn is pre-classified — Anneal reads what the work actually needs, reverse-engineers the intent, sets the quality bar the output must clear, and tells the model how to do it. Then the model executes.
classification: { effort_level: comprehensive, intent_type: build, confidence: 0.90 } hard_gates: [ observe_before_act, quality_gates_before_build, honest_verbs_only ] required_actions:[ read reverse_engineering @ THINK, read quality_gates @ PLAN ] reverse_engineering: "The real job is to earn trust with a skeptical C-suite — design and copy must read as evidence, not marketing."
A memory layer hands the model a pile of notes and hopes. Anneal directs the model — which is why the output cleared the full bar on the first pass, and why brute force couldn't. Not compression, not recall. Direction. That's the floor moving.
The models are the engine.
Anneal is what you put in front of them.
Anneal isn't another model — it's the intelligence layer that steers whichever frontier model you use. Claude, GPT-4, Gemini, or any foundation model underneath. Switch mid-workflow. Your context, directives, and quality standards stay consistent regardless of what's underneath.
Switch mid-workflow. The model changes. The intelligence layer doesn't.
Three things no memory layer can do.
Context delivery
One context layer for every AI surface you work in — Claude Code, Codex, Gemini, VS Code. Same memory, across sessions, across teammates. No starting from scratch.
Direction
Pre-classification, quality gates, directive enforcement. Anneal tells the model how to do the work — not just what the work is about. A memory layer can't direct how work gets done. This does.
Compounding intelligence
Every session is classified, steered, and written back. Anneal builds a directed model of how you work, what you enforce, and what good looks like. Day 90 is not Day 1.
How Anneal compounds.
Five stages. One continuous loop. The longer you run it, the faster it spins — and the further ahead you get.
Start with a task. Anneal surfaces relevant context automatically.
Anneal pre-classifies what the task actually needs — not just what it asked for — before a model sees a word. Prior work and context follow.
AI output that's grounded in your actual work, directed to your standard.
Iterate in place. Every edit teaches Anneal what good looks like for you.
Output goes out. Context stays in — ready to make the next task faster.
Individual leveling
Anneal builds a personal productivity layer — your prompts, your patterns, your output style — that gets better the more you use it.
Team leveling
What one person learns, everyone benefits from. Shared workflows, shared context, shared intelligence — without the coordination overhead.
Compounding returns
Day 30 isn't the same as Day 1. Anneal accumulates context the way a great analyst does — and applies it automatically.
Reliability you don't think about.
Anneal runs on grāmatr — the AI infrastructure platform built for teams that take reliability seriously. Sub-100ms context delivery. 99.9% SLA. Fail-open by design, so your work never stops.
It stays out of your way and never goes down. Anneal is what you actually use.
Learn about grāmatr →The reasoning layer your AI estate is missing.
Want Anneal for your whole organization without building an AI layer from scratch? Anneal is in production today — license it and deploy in weeks, not quarters. Your people get the workspace that pre-classifies every task, directs every agent, and enforces your standards on every session — from day one.
A design-partner media agency is already running Anneal in production today — alongside a bespoke app built on the same grāmatr platform.
Default deployment runs in Azure East US; private cloud options support localized data residency when regional requirements apply.
Book an architecture review →The floor moved.
Get on the right side of it.
Anneal is in production today. Early access is limited — we're onboarding teams who want to shape what the AI workspace looks like.
Or email us directly at [email protected]. No sales process. Just a conversation.