Infinite minds. One memory.

Theagentsystemthatfinishesrealworkandcompoundsintoyoursecond brain.

Most agents forget your business, overrun the budget, and hand back drafts. agntmind runs across the apps you already use, sends each task to the model that should do it, and finishes the job to your standard — closing the month, redlining a contract, prepping a renewal. Every task it completes makes it sharper, in a private brain you own and could walk away with tomorrow.

  • Finishes the task
  • Right model, every task
  • 3,000+ verified skills
  • Your brain, your data

See it work on your workflows first — trust is earned, not asked for.

The reason you've been burned beforeagntmind is built for the day after the pilot
88%of AI agents never reach production2026 enterprise agent data73%of AI budgets came in over projectionFinOps Foundation, State of FinOps 202622%report negative ROI at the 12-month mark2026 enterprise agent data6%of organisations qualify as AI high performers2026 agent-memory research
88%of AI agents never reach production2026 enterprise agent data73%of AI budgets came in over projectionFinOps Foundation, State of FinOps 202622%report negative ROI at the 12-month mark2026 enterprise agent data6%of organisations qualify as AI high performers2026 agent-memory research
88%of AI agents never reach production2026 enterprise agent data73%of AI budgets came in over projectionFinOps Foundation, State of FinOps 202622%report negative ROI at the 12-month mark2026 enterprise agent data6%of organisations qualify as AI high performers2026 agent-memory research
88%of AI agents never reach production2026 enterprise agent data73%of AI budgets came in over projectionFinOps Foundation, State of FinOps 202622%report negative ROI at the 12-month mark2026 enterprise agent data6%of organisations qualify as AI high performers2026 agent-memory research

What to expect

Ask like you'd ask a person. Get work you don't have to check.

Nothing to configure, no prompt to engineer — you ask the way you'd ask a colleague who's been here for years, because it already holds the context: your accounts, your documents, your standards, the call you made last quarter. It moves through your apps in one motion, so nothing is dropped in the handoff between them. And before anything reaches you, it's checked against your standard and fixed if it's off. What lands on your desk is work you can use — not work you have to review.

  • Ask in plain words
  • Holds your full context
  • Seamless across your apps
  • Quality-gated before you see it
  • Your data stays yours
Book my free consultation

One place to ask

Your copilot lives in one app. Your work lives in thirty.

An in-app copilot only sees inside its own product — Salesforce's sees Salesforce, your inbox's sees email. Your actual work crosses all of them: pull the number from NetSuite, the thread from Gmail, the record from Salesforce, and act on it. agntmind signs in as you across the whole stack and runs the task end to end — the seam between apps that no single vendor's copilot will ever own.

Why the others can'tA copilot embedded in one product is confined to that product's data and API. Cross-app write-access is exactly what no vendor grants a competitor's assistant.
~291SaaS apps the average enterprise now runs — and workers lose ~9% of the workday just toggling between them.HBR / Cornell-Qatalog · Productiv
The differentiator

Your brain. Your organisation's brain.

The part your competitors can't copy.

Everything it learns — how you price, how you write, how you decide, what your last quarter actually taught you — flows into one private brain that belongs to you. Not a search index that looks things up and forgets, but memory that compounds: a little sharper this week, materially sharper this quarter. It is also the reason the work comes back right. An agent with your context does not guess at your business; it already knows it. Your competitors can run the exact same models. They cannot run the brain built from your work — it never leaks to a model maker, and if you ever leave, it exports with you.

Your brain

It learns how you work

Your standards, your tone, your last correction — so what comes back needs no rewriting.

Your organisation's brain

One memory across every team

Finance to legal to sales on one context — and a 360° read of how the business is actually running.

Why the others can'tA frontier lab serves identical model weights to every customer — that's its business model. It literally can't sell you a private, compounding asset; your usage improves its next model, for everyone.
6%Organisations that qualify as AI high performers — most of the gap traced to agents that don't retain what they learn. Agents that can't remember can't scale.Enterprise agent-memory research, 2026

Judged on finished work

It learns what 'done' looks like to you.

You define what finished means. It plans the steps, does the work across your tools, checks its own output against your acceptance criteria, and fixes what's off — then hands you something finished, not a draft to salvage. Because it remembers your last correction, it clears your bar sooner every time. That is the whole difference between a tool you supervise and work you can actually hand over: other AI stops at "almost right" and leaves the last, hardest mile to you.

PlanActCheckFinish

Why the others can't"Done" is org-specific — your review steps, your acceptance criteria, your last correction. A system with no persisted state can't encode it; a stateless assistant restarts from zero every task.
01 Plan. Reads the full picture across your apps, maps the steps.
02 Act. Does the work across email, docs, CRM, finance — whatever it takes.
03 Check. Reviews its own output against your standard, fixes what's off.
04 Finish. Hands you a completed result to approve — not a rough start.
66%Share of practitioners who now spend more time fixing "almost-right" AI output than they saved getting it. Finished beats fast.Stack Overflow Developer Survey, 2025

It arrives knowing the work — then learns yours

Demos are easy. Ours is built for production.

3,000+ enterprise skills ship verified — each one tested to behave the same on the thousandth run as the first, so it holds when the inputs are messy and the stakes are real. Closing the books, redlining an NDA, running a clinical audit, standing up a campaign: it already knows how, and it reaches for the right one without being told. Then it goes further. It watches how your team actually works and writes new skills around your methods — yours, owned by your company, sharper every day it runs. Not 3,000 integrations. 3,000 things it can reliably do, and a library that only ever grows more yours.

Why the others can'tReliability comes from constraint, not a better prompt. A generalist with no tested-path layer is nondeterministic wherever inputs get messy — which is exactly where production lives.
0+Verified skills
40%Share of agentic-AI projects Gartner expects to be scaled back or cancelled by 2027 — largely on unreliability. Verified skills keep you in the other 60%.Gartner, 2025

A step ahead

It answers the questions you didn't think to ask.

Because it holds a live picture of how your business runs, it notices what you'd have missed: the renewal lapsing in nine days, the margin slipping on one product line, the deal that's gone quiet. It brings each one to you with a plan already drafted. A tool you have to prompt can only answer what you already know to ask — which is no help at all for the thing about to go wrong.

Why the others can'tYou can't be proactive with no memory. Surfacing a deviation needs a persistent baseline of "normal" — which stateless tools discard between prompts.
The renewal lapsing in nine daysPlan already drafted

What it costs to run

Every task on the right model. Not the priciest one.

AI bills didn't explode because tokens got expensive — they got cheaper. They exploded because one agent workflow burns tens of times what a simple question costs, and most systems send every step to the most expensive model in the building. agntmind decides which model each step deserves: the fast, cheap one for reading and gathering, the frontier one for the judgement that actually carries risk. You get frontier-grade output where it matters and stop paying frontier prices where it never did — so the bill lands where you budgeted it, and finance stops treating your agent as an open tab.

Why the others can'tA model maker will never send you to a rival's cheaper model — that's its revenue you'd be spending elsewhere. Choosing the right model only saves you money when the company choosing doesn't sell one.
73%Organisations whose AI costs came in over projection — with agent workflows consuming 10–50× the tokens of a simple query.FinOps Foundation, State of FinOps 2026

One task, three decisions

  • Reading the thread, pulling the numbers

    Fast modelPennies

  • Drafting from your templates

    Mid modelLow

  • The pricing call, the legal language

    Frontier modelWorth it

You never pick a model. You just stop paying for the ones the work didn't need.

Never obsolete

Always the best model. Because we don't sell one.

When a stronger model ships — from any lab — agntmind is running it the next morning. No migration, no re-buy, no retraining your team. The labs can't offer this: their business is you running their model, so they'll never route you to a rival's when it's better or cheaper. You get the upside of a market that improves every month, and none of the lock-in.

Why the others can'tHonest model-agnosticism conflicts with a model-maker's economics. A lab routing you away from its own model when a rival is better is working against itself.
81%Enterprises that name vendor lock-in as a top AI concern — while the cost of a given level of capability fell ~280× in 18 months. Both are arguments against marrying one lab.Zapier · Stanford HAI AI Index

You're always in control

You keep the veto. It loses the busywork.

Every important action waits for your yes — the contract, the spend, the send — with one line on what it wants to do and why. The rote approvals you always wave through, it learns and streamlines; the ones that matter always come to you, and you can tighten the rules any time — or stop the whole thing, mid-task, with one switch. You never trade away control. You just stop repeating yourself.

Why the others can'tThe rote approvals only get cheaper if the system remembers them — a memoryless assistant makes you re-confirm the same steps forever. The decisions that matter always stay with you.
35%Executives who admit they could not immediately pull the plug on a rogue agent — and 36% have no formal plan for supervising one at all.Enterprise AI governance research, 2026
agntmindReady for your yes

Send renewal quote to Northwind Ltd?

Contract renews in 12 days. Draft matches your standard terms; 4% uplift applied per policy.

WhatWhy$48,200 / yr

Security, answered simply

Your data is isolated, encrypted, and trains no one's model.

Your data is isolated, encrypted in transit and at rest, and never used to train anyone else's model.

There's no shared model to leak into — your data lives in its own isolated space, and never enters a training set. Role-based access, a full audit trail, and human approval on sensitive moves are built in. Certifications are underway — ask us for current status and our DPA.

Access

Granular role-based permissions

Every action scoped to what that person may touch — nothing more.

Audit

A clean record, always

A complete, reviewable trail of what ran, when, and who approved it.

Governance

One system you can govern

Replace scattered shadow AI with something your security team is glad to sign off on.

Why the others can'tIf a product's model improves by training on all customers, "we never train on your data" contradicts how it gets better. No shared model, no shared training set — the guarantee is structural, not just contractual.
67%Executives who believe their company has already leaked data through AI tools nobody approved. The shadow AI already running in your org is the risk — not the system you can govern.Enterprise AI governance research, 2026

Getting started

A win this week. Not a project next year.

It runs on the stack you already have, so there's nothing to rip out and no six-month rebuild before anything works. Week one, it's finishing real tasks; week three, it's noticeably better at them. Bring us the one workflow that hurts most, and we'll show you the agent doing it — before you commit to anything wider.

  1. STEP 01

    Talk to us

    A friendly consultation to map your stack and find your highest-leverage first win.

    Free · no rip-and-replace

  2. STEP 02

    We connect it & stand up your Brain

    Your apps, knowledge and permissions, configured to your org. We handle the hard parts.

    Days, not quarters

  3. STEP 03

    You delegate. It grows.

    Real work, your approval, and an operator that gets sharper every week.

    A win in your first week

Why the others can'tA system that demands workflow-redesign-first is architecturally slow. One that runs on your existing stack starts on day one — you can't be both a transformational rebuild and fast.
How much more often externally-sourced AI reaches deployment than internal builds (~67% vs 33% success). Buying a working system beats a six-month rebuild.MIT Project NANDA, 2025

What it costs

Proof first. Then you scale.

Start with one workflow, a deliverable you define, and a number you'll judge it on. You see it pay off before you widen the scope — no per-seat surprise, no six-month spend ahead of the payoff. If the pilot doesn't earn its keep, you stop, and you're out a conversation.

The outcome contract

  1. 01You pick the workflow and the numberThe one that hurts most, and the metric you'd genuinely change your mind over — hours, cycle time, error rate, margin.
  2. 02We measure your baseline in week oneBefore anything changes, so the comparison is yours and not ours.
  3. 03You judge it on the deltaSame workflow, same metric, measured again. If it hasn't earned its keep, you stop — and you're out a conversation.

We publish no efficiency number here, because the only one that should decide this is measured on your work, against your baseline.

See it pay off on one workflow. Then grow.

One workflow, a defined deliverable, and a success metric you set. If it doesn't pay for itself in the pilot, you simply stop — you've spent nothing but a conversation.

  • Fixed-scope pilot
  • No rip-and-replace
  • See value first
Get my pilot scoped
42%Companies that abandoned most of their AI initiatives in 2025 — usually after spending before they saw value. A scoped pilot flips that: proof first.S&P Global Market Intelligence, 2025

Start today

See what it could learn about your business.

Book a free consultation, or start with a 15-minute Q&A. No rip-and-replace, no commitment — just a straight look at the first work agntmind would take off your plate, and what it'd grow into from there.

No commitmentNo rip-and-replaceWe map your first win on the call