AI-enabled go-to-market execution
We are AI-native.
We built AI into the core of how we work, on your data, with our operators in the loop the whole way, for the moment when the plan needs proof and the model needs context.
Context
Your data, curated.
Your calls, your pipeline, your market, your buyers, assembled so the model can act. Get this right and the rest works.
Execution Plan
Quick wins now.
Not a roadmap for someday. Each win has an owner, the accounts it applies to, and the reinforcement that makes it hold.
Execution Support
We run it, then hand it over.
We execute alongside your team, then transfer it. The outcome is the goal, not our continued presence.
A model is only as good as the context you give it.
A public model knows the internet, not your win rates, your sales cycle, or which of your accounts were ever going to buy. So we build the context first, put AI on top of it, and keep our operators in the loop the whole way.
That is the difference between an AI firm and a firm that rents AI licenses.
The method
Six phases, starting from the quantified problem
Step one is non-negotiable: quantify the problem and build the issue tree. Everything downstream validates against that number.
Phase 1
Quantify the problem
Understand the big problem and attach a number to it, tied to a KPI that matters to both the portfolio company and the fund. Build the issue tree, state the quantified problem as a hypothesis, and lay out the possible root causes. Everything downstream validates against it.
Phase 2
Context engine
Not discovery. Stand up a data warehouse that holds everything needed to investigate the root causes: carefully conducted interviews and call transcripts, CRM and opportunity data, win-loss, competitive positioning, ICP, and buyer personas. Vague inputs produce vague answers.
Phase 3
Interrogate and validate
Put an agent to work on the dataset and have a conversation with the context engine. Surface the problem points, then validate the quantified problem and every root-cause hypothesis against what the data actually shows.
Phase 4
Build and test
Quick wins, not a roadmap. With a complete picture of the current state, determine what to build now. Depending on what the analysis surfaces, that might mean cleaning the data, or standing up the bot, agent, or app the team can keep using. Rapid build, rapid test.
Phase 5
Launch into go-to-market
Launch into the go-to-market team and keep the pairing in place for a set time after go-live. Adoption is hard, so coach the team, run a customer feedback loop, find out why anything is going unused, and keep tweaking until it is genuinely usable.
Phase 6
Transition and transfer
Teach the team to run the whole mechanism, then transfer it to the portfolio company. Our job is not to be there in perpetuity. This transitionary phase is the final deliverable.
AI Advisor Plus One
Your context, our operator, and the system.
Every engagement pairs three things. Context comes from you, the data only you have. Expertise comes from a Cortado operator matched to your problem, who validates every output and is accountable for it. Execution runs through the system, at speed. Miss any corner and it breaks.
AI drafts. The operator decides. Nothing reaches you as a model's answer with our logo on it.
Human in the loop, always. Agents are informed by your data and checked by our experts, start to finish.
We augment your team. We leave the expertise with your people, not a dependency on us.
We question the answers. We ask the right questions, then pressure-test what comes back.
What you keep
You keep a working system your team owns.
Most firms leave a slide deck. We leave a working system built on your proprietary data, and our operators keep pairing with it after we go.
The system
A context engine your team owns, the one we call AI Advisor, plus a library of skills and apps built on top of it. The data inside is yours.
The access
It meets your team where they work: a web interface, tied into tools like Slack, in the flow of work.
The standard
Every output clears two bars: it looks like Cortado, and a human can read it, use it, and build on it.
The exposure
Your team is already leaking data into personal accounts.
Your people are already using AI, on personal accounts, right now. That is three exposures, today:
Data leak
Proprietary data going into public models.
IP bleed
Third-party IP and trademarks bleeding into your tools and messaging.
Legal risk
Protected information consumed in ways that carry real legal risk.
A private system on your own data closes the gap. We handle it as a product problem, not a rulebook.
The catch
Why you still want the operator
You can generate a comp plan with an AI tool in thirty minutes. For some jobs, that is the right call.
The reason you call a firm like ours is that you do not yet know what good looks like. A model will tell you its answer is good with total confidence, every time, including when it is wrong. An operator who has run this motion tells you which answer survives contact with a real sales team.
Analysis is getting cheaper every month. Judgment is not.
AI Advisor
A first read, before you ever speak to us.
Tell AI Advisor where your growth is stuck and it points you at the likely cause, drawing on our playbooks, research, and case history.