The Company Brain Ramp-Up Curve: How Long Until AI Actually Knows Your Business

28 September 202621 min read

Co-founder at Superkind

A rising row of components illustrating the ramp-up curve of a Company Brain learning a business over time

It is the question that decides most Company Brain purchases, and almost no vendor answers it honestly: how long before the AI actually knows my business? Not the internet. Not a generic model that can write a decent email. My customers, my exceptions, the reason we never ship on the last Friday of the quarter, the way our best Sachbearbeiter handles a disputed invoice without being told.

The honest answer has a shape. It is a curve, not a switch. A human hire takes around eight months to reach full productivity, and only three to four with strong onboarding¹². A Company Brain follows a similar arc, compressed into weeks, with one difference that changes the economics entirely: it never leaves, and every correction it receives makes it permanently better.

This article walks the ramp-up curve stage by stage - day one, week one, month one, month three and beyond. What feeds it. What speeds it up and what stalls it. Why it is not the same as a bigger context window, a wiki, or a smarter model. And why the daily feedback loop is the part that compounds into something a competitor cannot copy.

TL;DR

Useful on day one, specific to you in 8 to 12 weeks. A Company Brain does capable general work immediately and learns your judgment over the same arc a good hire does - just faster.

Four inputs feed it: documents and data, processes, people-knowledge, and daily feedback. The fourth is the one that compounds.

Month three is the inflection. Enough corrections have accumulated that output looks like your own team produced it.

It is not a context window or a wiki. A context window forgets after every session; a wiki stores what someone had time to write. A Company Brain is persistent, shared, permission-aware, and it accumulates.

The ramp-up you pay for once stays. Unlike a human hire, the knowledge survives turnover - so you never repay it.

Why This Is the Highest-Intent Question a Buyer Asks

When a Geschaeftsfuehrer or head of operations evaluates a Company Brain, they rarely ask about model benchmarks. They ask how long until it is worth it. That question is really four questions stacked together, and separating them is the first step to a real answer.

When does it stop being generic? - The base model knows the internet on day one. The buyer wants to know when it knows their pricing exceptions, their approval chain, their customers by name.
When can I trust it unsupervised? - Trust follows accuracy. Buyers want to know when the human-in-the-loop check can widen from every task to spot checks.
When does it pay back? - Time-to-value on enterprise agent deployments is running at a median of around 5 months across 2026 surveys, with simpler use cases paying back in about 3 and complex ones nearer 9¹⁶.
Does the knowledge stay? - This is the question that separates a Company Brain from a hire. A person’s ramp-up is rented; a Company Brain’s is owned.

The market context makes the question urgent rather than academic. Adoption is nearly universal but value is not: 78 percent of organisations report using AI, yet only around 6 percent have scaled it to material financial impact¹⁷. The gap is not the model. It is whether the system ever learned the specific business it was pointed at.

The Real Question Behind the Question

“How long until AI knows my company” is a proxy for “how long until this is a durable asset rather than a clever demo.” The demo works on day one. The asset is built over the weeks that follow - and unlike a demo, it keeps appreciating.

What the buyer says	What they mean	Honest answer
“How long to set up?”	When can we connect it?	Days to connect systems and documents
“When is it useful?”	When does it save real time?	Week one for factual and drafting work
“When does it know us?”	When does it apply our judgment?	Month one to month three, via feedback
“When can we trust it alone?”	When can we reduce oversight?	As correction rate falls, typically after 60 to 90 days¹⁶
“Will it stick?”	Do we keep the knowledge?	Yes - it survives turnover, unlike a hire

With the question separated into its parts, the curve itself becomes easy to read. Here is what each stage actually looks like.

The Ramp-Up Curve, Stage by Stage

Think of a Company Brain the way you would think of an exceptionally fast new colleague who has already read every textbook but none of your files. The stages below describe how that colleague goes from capable-but-generic to genuinely one of yours.

Day 1: Capable, but generic

What it can do - Draft, summarise, translate, search, and answer general questions at the level of a strong graduate. The base model is fully formed on arrival.
What it just gained - Read access to the documents and systems you connect: email, Teams, SharePoint, CRM, ERP. It can now retrieve your facts even before it understands them.
What it does not have - Your judgment. It does not yet know which price list is current, which customer gets 45-day terms, or why a particular supplier is never chased on a Monday.
The right expectation - Treat day one like a new hire’s first morning: useful for defined tasks with a human checking the output, not for autonomous decisions.

Week 1: It knows your facts

Grounded answers - It answers factual questions from your own documents and records, not the internet. “What did we quote this customer last year?” returns your number, with a source.
First real time saved - Drafting, retrieval, and summarising across your systems start replacing manual searching. This is where the first hours-per-week savings appear.
Conflicts surface - The brain finds that two documents disagree. This is a feature: it exposes where your single source of truth is actually three sources that contradict each other.
Feedback begins - Every correction an employee makes in week one is the first deposit into the memory that will define month three.

Month 1: It follows your processes

Process awareness - It knows the sequence of a workflow - how a quote becomes an order becomes an invoice - and can carry a task through the steps rather than answering one question at a time.
Named entities - It knows your accounts, products, and people by name and can reason about them: which rep owns which account, which SKU replaced which.
Fewer basic corrections - The corrections shift from “that fact is wrong” to “that is right, but we handle this exception differently.” That shift is the signal the ramp-up is working.
Comparable to a human milestone - This is roughly where a well-onboarded human hire stops asking where things are and starts asking how you want things done⁴.

Month 3+: It applies your judgment

Judgment, not just facts - Hundreds of small corrections have taught it the unwritten rules. It handles the disputed invoice, the awkward reschedule, the non-standard discount the way your experienced people would.
Widening autonomy - Because the correction rate has fallen, oversight moves from every task to spot checks on the cases it flags as uncertain.
Output that passes as yours - Colleagues stop being able to tell which draft the AI started. This is the inflection point buyers are really asking about.
Still climbing - The curve does not flatten. Seasonal work, new products, and new customers keep feeding it, so month twelve is meaningfully sharper than month three.

Stage	What it knows	What it can do unsupervised	Human equivalent
Day 1	The internet, plus read access to your files	Very little - check everything	First morning
Week 1	Your facts, with sources	Drafting, retrieval, summaries	First week
Month 1	Your processes and named entities	Routine multi-step tasks with review	Roughly month 3 of a hire
Month 3+	Your judgment and exceptions	Routine work end to end, spot-checked	Roughly month 8 of a hire¹
Month 12+	Seasonal, edge, and evolving cases	Most routine work in its domain	A seasoned team member who never leaves

Why the Compression Is Possible

A human learns one task at a time, in real time, and can only work one shift. A Company Brain reads your entire document base at once, learns from every employee’s corrections in parallel, and works around the clock. The same eight-month arc plays out in weeks because the learning is not rate-limited by a single person’s attention.

What Actually Feeds the Company Brain

The curve moves because four distinct inputs feed the brain, each answering a different kind of question. Understanding them tells you where your own ramp-up will be fast and where it will need work.

Documents and data - The facts. Contracts, SOPs, past quotes and projects, product specs, and the structured records in your ERP and CRM. This is what makes day one and week one possible. It answers “what is true here?”
Processes - The sequence. How a task actually flows from step to step, including who approves what and where the handoffs are. This is what turns a question-answerer into something that can carry a job to completion. It answers “how does work move here?”
People-knowledge - The judgment. The tribal knowledge that lives in heads and never reached a document: the exceptions, the workarounds, the reasons behind decisions. It answers “why do we do it this way?”
Daily feedback - The correction. Every edit, override, and approval from your team, captured and folded back in. It answers “were we right, and if not, what is right?” This is the input that compounds.

Input	Question it answers	Where it comes from	Speed to absorb
Documents and data	What is true here?	Files, ERP, CRM, SharePoint	Days
Processes	How does work move here?	SOPs plus observed workflow	Weeks
People-knowledge	Why do we do it this way?	Corrections and conversations	Weeks to months
Daily feedback	Were we right?	Every employee, every day	Continuous, compounding

The order matters. Documents and processes get you a fast start, but they plateau. People-knowledge and feedback are what carry the curve from “knows our files” to “knows our business.” Most of the durable value lives in the two inputs that a static tool never captures.

The 80 Percent Nobody Feeds

An estimated 80 to 90 percent of company knowledge is unstructured - buried in emails, PDFs, and conversations rather than tidy database rows¹⁹. A Company Brain that reads only your structured data is learning from the smaller half. The ramp-up accelerates when it can read the unstructured knowledge too and capture the reasoning that never gets written down at all.

What Speeds the Curve Up, What Slows It Down

Two companies can start the same Company Brain and reach month three at very different points. The difference is rarely the technology. It is a short list of accelerators and brakes that you control.

Accelerators

✓ One focused workflow - depth in a single high-value process beats shallow coverage of ten
✓ A named process owner - someone whose job includes correcting the AI daily
✓ Authoritative sources marked - telling it which price list wins removes weeks of guessing
✓ High task volume - more repetitions per week means more corrections and a steeper curve
✓ Experienced people engaged - the judgment you want captured has to be corrected in, not wished in

Brakes

✗ Scattered, contradictory data - the brain learns conflicting facts and stalls on which is current
✗ No owner, no corrections - the feedback loop dies and the curve flattens early
✗ Boiling the ocean - vague, everything-at-once scope means nothing gets deep enough to matter
✗ Locked systems - if it cannot read the real data, it cannot learn the real business
✗ Set-and-forget expectations - treating it as software to install rather than a colleague to train

The single biggest brake is data. Analysts are consistent that data, not algorithms, is the enterprise AI bottleneck - Deloitte reports data governance is the top priority for a majority of chief data officers, precisely because ungoverned data produces confident, wrong answers¹⁰¹⁵. The good news is that you do not fix it first and start later. You fix it as a by-product of use.

Do not wait for perfect data - a two-year cleanup before you begin is how projects die. Start with the data around one workflow¹⁸.
Let feedback rank your sources - the brain learns which document is authoritative because your team keeps correcting toward it.
Fix at the point of pain - the contradictions the brain surfaces in week one are a prioritised, free data-quality audit.
Assign the corrections - a workflow with no owner is a workflow with no feedback, and feedback is the curve.

“Data-centric AI is the practice of systematically engineering the data used to build an AI system.”

- Andrew Ng, founder of DeepLearning.AI and LandingAI¹²

Ng’s point is the whole game for ramp-up. With a fixed base model, the thing you engineer is the data and the feedback - and that is exactly what the daily loop does, automatically, as your team works¹³.

Want a realistic ramp-up timeline for your business?

Book a 30-minute call. We will map one high-value workflow and what its curve would look like.

Book a Demo →

An ascending coil representing the compounding feedback loop of a Company Brain

Why This Is Not a Bigger Context Window, a Wiki, or a Generic LLM

The most common objection is a reasonable one: why not just use a model with a huge context window, or point a generic assistant at our wiki? Because none of those things ramp up. They start and stay where they are. The distinction is worth making precisely.

A context window is working memory, not company memory

Per session, then gone - whatever you paste into a prompt is forgotten when the session ends. Tomorrow’s prompt starts from zero.
Paid every time - you re-supply and re-pay for the same context on every request. A bigger context window is a bigger bill, not a memory.
Not shared - one person’s carefully assembled context does not help the colleague next to them.
No accumulation - a correction you make today changes nothing about tomorrow. There is no curve because there is no memory to climb.

A wiki stores what someone had time to write

Frozen and partial - it holds the version someone last edited, which is usually out of date, and only what they chose to document.
No sense of current - it cannot tell you which of two conflicting pages is authoritative today.
It informs, it does not act - a wiki is a filing cabinet. A Company Brain reads the cabinet and then does the work.
It misses the why - the reasoning behind decisions almost never gets written into a wiki, which is exactly the knowledge that matters most.

A generic LLM knows the world, not your world

No grounding - without your data it answers from the internet, confidently and often wrongly for your context.
No permissions - it has no concept of who is allowed to see what, which makes it a compliance problem the moment it touches real data.
No learning from you - it does not get better at your company from your corrections. It is the same next quarter as it is today.

Property	Big context window	Wiki / SharePoint	Generic LLM	Company Brain
Persists across sessions	No	Yes (static)	No	Yes
Improves from feedback	No	No	No	Yes
Shared across everyone	No	Yes	No	Yes
Permission-aware	No	Partly	No	Yes
Captures the reasoning (why)	No	Rarely	No	Yes
Takes action, not just answers	No	No	Limited	Yes
Survives staff turnover	No	Partly	No	Yes

This is also why 400,000 generic copilots can be deployed and still not know your company: grounding on documents is table stakes, but grounding without accumulation is a demo that never becomes an asset. The ramp-up curve is the asset. If a tool has no curve, you are renting capability, not building it.

Why the Feedback Loop Compounds Into a Moat

The curve does not just rise. It compounds, and compounding is what turns a ramp-up into a competitive advantage. The mechanism is simple, and its consequences are not.

The mechanics of the loop

The AI proposes - a draft, a classification, an action, based on what it knows so far.
A person responds - they accept, edit, or override. Each of those is a signal.
The signal is captured - the correction is folded into the Company Brain, tied to the context it applies to.
The next task starts higher - the similar task next week begins from the corrected version, not the original mistake.
The gain is shared - because the memory is shared, everyone benefits from one person’s correction, not just the person who made it.

Because corrections accumulate and are shared, the curve is not linear - each week’s improvement builds on every prior week’s. This is the feedback loop that a static chatbot structurally cannot have, and it is why month twelve is not just twice month six.

Memory Makes the Model Sharper and Cheaper

Purpose-built memory is measurably better than brute-forcing context. On long-conversation benchmarks, a dedicated memory approach reached scores in the low-to-mid 90s while using roughly 6,900 tokens per query, against about 26,000 tokens for stuffing the full context in - and it improved temporal reasoning by nearly 30 points over its own prior version¹¹. Accumulated memory beats a bigger window on both accuracy and cost.

Why it becomes a moat

It is specific to you - your corrections encode your business. A competitor cannot copy them because they never had your exceptions or your customers.
It widens over time - a company that started its loop a year ago is a year of corrections ahead, and the gap grows, not shrinks.
It survives turnover - the knowledge does not walk out the door, so you are not endlessly rebuilding the same lead. This is the cure for institutional amnesia.
It is infrastructure, not a feature - Gartner now frames the memory layer behind agents as essential infrastructure, projecting that over half of AI agent systems will rely on it by 2028⁷⁸.

“Context graphs are rapidly emerging as a foundational technology concept with massive promise for enterprise AI agent initiatives for guardrailing, observability, evaluation and self-learning.”

- Gartner, The New Essential Infrastructure for Agentic Systems⁷

“Self-learning” is the operative word. The reason to care about the ramp-up curve is that a system with a real feedback loop is still climbing long after a static tool has stopped - and that difference compounds into a lead competitors cannot buy back.

How to Accelerate Your Own Ramp-Up

You cannot skip the curve, but you can steepen it. These steps are the difference between reaching “knows our business” in eight weeks versus eight months.

Pick one high-value, high-volume workflow - choose a process that runs often and costs real time. Volume gives the loop repetitions; value makes the payback obvious. Resist starting everywhere at once.
Name a process owner - one person whose job explicitly includes reviewing and correcting the AI daily for the first weeks. Without an owner, there is no feedback, and without feedback there is no curve.
Connect the real systems - give it read access to the live sources for that workflow: email, Teams, SharePoint, CRM, ERP. Learning from a stale export is learning last quarter’s business.
Mark the authoritative sources - tell it which price list, which SOP, which record wins when documents conflict. This removes weeks of the brain guessing which of three contradictions is current.
Correct relentlessly for four weeks - the first month of dense corrections front-loads the curve. Every override now is worth many later.
Keep a human in the loop, then widen - check everything at first, then move to spot checks on flagged cases as the correction rate falls. Most enterprises run explicit human checkpoints for the first 60 to 90 days¹⁶.
Measure the curve monthly - track unassisted completion, corrections per task, time saved, and escalation rate. Falling corrections and rising completion mean it is working.
Then expand, do not restart - once the first workflow is solid, add the next on the same memory. The second use case ramps faster because the brain already knows your business.

Ramp-Up Readiness Checklist

You can name one workflow that runs often and costs real time
That workflow touches at least two systems the AI can connect to
You have a process owner who will correct the AI daily
You can point to the authoritative source when documents conflict
Experienced people are willing to spend time correcting for a month
You have baseline numbers for the workflow to measure against
Leadership accepts an 8 to 12 week arc, not an overnight switch
You are starting with one use case, not five

None of these steps is technical. That is the point: the ramp-up is paced by how well you feed and correct the brain, which is entirely within your control - and it is the same discipline that makes a human hire productive faster, applied to something that never leaves.

How Superkind Fits

Superkind builds a Company Brain and the AI employees that work on top of it. The design goal is the curve itself: get useful fast, then get specific to your business through daily feedback, and keep the knowledge when people leave.

Company Brain as the foundation - a persistent, shared memory built from your documents, processes, people-knowledge, and daily corrections. It is the asset; the AI employees are what act on it.
Learns your company, not the internet - the brain is grounded in your reality and sharpened by feedback, so it gets better every day at how your specific company works.
Connected to the real systems - email, Teams, SharePoint, CRM, ERP, and API software like SAP, Salesforce, HubSpot, DATEV, and Lexware. No island solution, no tool chaos.
First AI employee in about two weeks - we start with one high-value workflow so the loop has volume and the payback is visible early.
Feedback built into the workflow - corrections your team makes in the normal course of work feed the brain automatically, so the curve climbs without extra process.
Knowledge survives turnover - what the brain learns stays after a person resigns or retires, so you never repay the same ramp-up.
Runs on your terms - deployable within your infrastructure with encrypted connections, role-based permissions, and audit logs for DSGVO and EU AI Act alignment.
Outcomes, not seats - priced against measurable results on a workflow, not per-user licences for a tool nobody finishes onboarding.

Dimension	Generic AI assistant	Superkind Company Brain
Day 1	Generic answers from the internet	Generic answers plus your connected data
Week 1	Same as day 1	Grounded answers from your files, with sources
Month 3	Same as day 1	Applies your judgment and exceptions
Learning	None from your corrections	Improves every week from daily feedback
Turnover	No knowledge to lose or keep	Knowledge stays when people leave
Pricing	Per seat	Per outcome on a workflow

Pros

✓ Built for the curve - useful fast, specific to you in weeks, better every day after
✓ Knowledge is an owned asset - it survives turnover instead of walking out
✓ Sits on your stack - connects to existing systems, nothing to rip out
✓ Feedback is automatic - corrections happen in the flow of work
✓ Outcome-based pricing - pay for results, not licences

Cons

✗ Not instant magic - the curve is fast but real; month three beats week one
✗ Needs a process owner - the loop requires someone to correct it early
✗ Needs real system access - it must read your live data to learn your business
✗ Not a self-serve toy - overkill if you only need a simple one-off automation

What to Expect by When: A Decision Framework

Use this to set expectations internally and to judge whether your ramp-up is on track. If your reality is far below the right-hand column at each stage, it is almost always a data or ownership issue, not the model.

By this point	A healthy ramp-up looks like	If it does not, check
End of week 1	Grounded answers from your files; first hours saved	Are the right systems actually connected?
End of month 1	Follows the process; corrections shift to exceptions	Is someone correcting it daily?
End of month 3	Output passes as your team’s; oversight narrows to spot checks	Are experienced people engaged, or juniors?
Month 6	Handles most routine work in its domain; second use case added	Did you expand on the same brain or restart?
Month 12	Sharper than month 3; seasonal and edge cases covered	Is the feedback loop still running?

Start Now

✓ The curve starts today - every week of corrections is a week you cannot get back later
✓ Data improves as a by-product - the brain surfaces contradictions to fix¹⁸
✓ Knowledge banked before turnover - capture judgment while the experts are still here
✓ Compounding lead - a year of feedback is a year competitors cannot copy

Wait for Perfect Data

✗ Perfect never arrives - a full cleanup before starting is how projects quietly die
✗ No curve, no learning - the memory only improves through use
✗ Knowledge keeps leaving - every resignation in the meantime is judgment lost¹⁰
✗ The gap widens - competitors who started are pulling ahead each quarter

The framework leads to one conclusion: the best time to start the curve was a year ago, and the second best time is now. Waiting does not de-risk the ramp-up. It just delays the day the AI finally knows your business - and moves that day further out every quarter you postpone.

Frequently Asked Questions

A Company Brain is useful on day one for general work and gets genuinely specific to your business over 8 to 12 weeks of daily use. By week one it answers factual questions from your documents. By month one it follows your processes and knows your named accounts. By month three it handles routine work the way your best people would, because it has absorbed hundreds of small corrections. The curve never fully flattens - it keeps improving as long as your team keeps working with it.

A Company Brain is a persistent, shared memory layer built from your documents, processes, people-knowledge, and the daily feedback of your employees. It is not a chatbot and not a single model. It is the durable knowledge asset that your AI employees sit on top of, so they answer and act the way your company actually works. Unlike a person, it does not leave, forget, or take its knowledge with it when someone resigns.

Yes, but be precise about what kind. On day one a Company Brain already has a capable base model and immediate access to whatever documents and systems you connect. It can draft, summarise, search, and answer general questions competently. What it does not yet have is your judgment: the exceptions, the unwritten rules, the reasons behind decisions. That specific knowledge is what the following weeks build.

Month three is when the daily feedback loop has run long enough to change behaviour, not just knowledge. By then the Company Brain has seen how your team corrects its drafts, which exceptions apply to which customers, and how your processes really run versus how they are documented. This is the point where a human hire typically stops asking basic questions too. It is where output starts to look like your own team produced it.

Four inputs. Documents and data give it the facts - contracts, SOPs, past projects, ERP and CRM records. Processes give it the sequence of how work gets done. People-knowledge gives it the judgment that never made it into a document. Daily feedback from employees corrects and sharpens all of it. The fourth input, feedback, is the one that compounds and the one a static system never gets.

Three things. Scattered or contradictory data means the brain learns conflicting facts and has to be told which is current. No process owner means corrections do not happen, so the loop stalls. And starting with a vague, everything-at-once scope means nothing gets deep enough to matter. Companies that pick one high-value workflow, give it a clear owner, and correct it daily ramp up several times faster than those that boil the ocean.

No. A context window is per-session working memory that is paid for on every prompt and forgotten when the session ends. A Company Brain is persistent, shared across every user, permission-aware, and it accumulates. You can paste a million tokens into a prompt and the model still will not remember your correction tomorrow. The difference is the difference between short-term and long-term memory.

A wiki stores what someone had time to write down, in the version they last edited, with no sense of which page is current or who is allowed to see it. A Company Brain reads across your live systems, resolves conflicts, respects permissions, and captures the reasoning that never gets written into a wiki. It also acts, not just informs. A wiki is a filing cabinet; a Company Brain is a colleague who has read the filing cabinet and remembers every conversation about it.

That is the entire point. In most companies, when a senior person resigns or retires, their judgment walks out with them and the team re-learns it the hard way. A Company Brain captures that knowledge as it is used, so it stays after the person is gone. The ramp-up you paid for once does not have to be repaid every time someone changes jobs.

Every time an employee corrects a draft, overrides a suggestion, or approves an action, that signal is captured and folded back into the Company Brain. The next similar task starts from the corrected version, not the original mistake. Because the corrections accumulate and are shared across everyone, the system improves in the direction of your specific reality every week - and the gap between it and a generic tool widens over time.

Track four things monthly: the share of tasks the AI completes without correction, the average number of corrections per task, the time saved per process, and the escalation rate to a human. A healthy ramp-up shows corrections falling and unassisted completion rising month over month. If those numbers stall, it is almost always a data or ownership problem, not a model problem.

Almost certainly yes. Data quality is the most common blocker, but the fix is not a two-year cleanup before you begin. A focused Company Brain starts with the data around one workflow, learns which sources are authoritative through feedback, and improves the data as a by-product of use. Deloitte reports data governance is the top priority for a majority of chief data officers precisely because waiting for perfect data means never starting.

A Company Brain can be run inside your own infrastructure with encrypted connections, role-based permissions, and audit logs, so personal data stays under your control and the right to erasure remains enforceable. Most internal process-automation use cases fall into the minimal-risk category under the EU AI Act, which becomes fully applicable in August 2026. Compliance is a design decision made at setup, not an afterthought.

Early on, you keep a human in the loop on anything that matters, exactly as you would with a new hire. The AI flags low-confidence cases for review rather than acting on them, every action is logged, and the correction becomes training for next time. Industry surveys show most enterprises run explicit human-in-the-loop checkpoints for the first 60 to 90 days, then widen autonomy as the error rate falls.

Sources

Henri Jung

Co-founder of Superkind, where he helps SMEs and enterprises deploy custom AI employees that actually fit how their teams work. Henri is passionate about closing the gap between what AI can do and the value it creates in real companies. He believes the Mittelstand has everything it needs to lead in AI - it just needs the right approach, and the patience to let a Company Brain learn the business the way a great hire would.

Ready to start the curve?

Book a 30-minute call with Henri. We will pick one workflow, sketch its ramp-up timeline, and show you what your Company Brain would know by month three - no commitment, no sales pitch.