Your team bought AI. It isn't producing pipeline yet.
Agents rarely fail. They get abandoned. Nobody decided what the thing should remember, nobody agreed how success would be measured before it shipped, and the person who built it moved on. I take the lead inside your stack and stay until the agents can prove what they caused, measured against a group you deliberately held back.
Rehan Sajid: founding marketer behind $5M to $170M ARR at Cohere Health, agentic GTM in oncology with one of the biggest oncology ventures, systems shipped across Vista Equity portfolio companies, and health tech oversight at Zyter.
Pipeline influenced
across GTM systems
ARR scaled
in health tech
Shipped and running
Client systems and my own products. All of it in production.
Thirty seven companies. None of them asked.
I do not send proposals or capability decks. Before a first conversation I build the company a working read of its own market: where demand is leaking, what the answer layer says about them, what I would do in the first ninety days. Dated, sourced, and checkable line by line.
Forty of those exist. Some became clients. Some replied to correct me, which is its own kind of useful. Most stay private to the person they were written for, which is why this page shows the arguments rather than the names.
It is a slow way to sell and an unreasonable amount of unpaid work. It is also the only honest way to prove I can do the job before anyone pays me to.
37
companies read in full
12
audited in one morning
0
of them asked me to
"Not under-marketed. Pre-agentic."
Insurance software, market leader in its niche
"Best rated, least found."
Behavioural health platform
"Funded like the leader, not yet the answer."
AI native ERP, Series B at the time of writing
"The best product, the quietest distribution."
AI infrastructure, ten thousand customers
"Five brands, one continuum."
Multi brand health services group
"Twelve companies, one morning."
Venture portfolio, audited in a single sitting
Six of forty. Opening lines quoted from the real pages, company names removed because those reads belong to the people they were written for.
Human in the lead. With AI.
The weight sits on the first three words. A person decides, and the AI does the work that decision needs. That goes further than human in the loop, where someone only checks what the machine already chose.
Most AI in revenue teams stalls because nobody is leading it, not because the model is weak. That is the whole diagnosis, and it is why the fix is a person with a mandate rather than a better tool.
| Human in the loop | Human in the lead | |
|---|---|---|
| Who decides what to do | The agent | The person |
| What the human actually does | Approves or rejects output | Sets direction, judges, owns the outcome |
| When the AI is wrong | Someone catches it, sometimes | The system refuses to act without a source |
| What counts as success | Activity: emails sent, briefs made | A result measured against a holdout |
| When the builder leaves | The agent drifts and dies | Ownership is handed over on purpose |
Agents don't fail. They get abandoned.
Every one of these is a missing leader rather than a missing feature. If any of the three sounds like your last twelve months, the problem was never the model.
Reason one
It forgets between runs
Nobody decided what it should remember, or for how long. So it greets the same account as a stranger every Monday and the team quietly stops trusting it.
Reason two
Nobody can prove it worked
Nobody decided how success would be measured before it shipped. At renewal the only available answer is how busy it was, and that is not an answer finance accepts.
Reason three
The builder moved on
Nobody was left whose job it was to lead it. The agent keeps running against an ICP that changed two quarters ago, and nobody owns the decision to fix or kill it.
What I sell is the lead. The agents are how I deliver it. You get the output rate of a machine and work you can put your own name on, which are usually the two things you are asked to choose between.
The questions I get in the first week.
Not hypotheticals. These are the actual sentences people say to me once the pilot is over and the agent is still running. If three or more of these are live in your team right now, that is the engagement.
Data and signals
- Our CRM is a mess. Can agents work on it anyway?
- Which signals actually predict a deal in our market?
- We bought intent data. Why is it all noise?
- A rule changes next month. Who is affected?
Adoption
- Reps opened it twice and went back to LinkedIn.
- It writes emails nobody would send.
- Marketing loves it. Sales ignores it.
- It forgets everything between runs.
Trust and compliance
- Can it touch PHI? We are under HIPAA.
- Can it send without a human reviewing it?
- Will security sign off if it runs in our tenant?
- What stops it learning across clients?
Proof
- It booked 53 meetings. Did it cause any?
- Finance wants a number before renewal.
- How big does the holdout need to be?
- Your dashboard says up. Our pipeline says flat.
Ownership
- The person who built it left. Now what?
- Who keeps it current when our ICP changes?
- Clay, HubSpot, or build our own?
- When do we stop paying for it?
The ones under Proof are the hardest, and the reason most agent projects do not survive a budget review. They are also the ones I would want to answer first.
It booked 53 meetings. It caused none of them.
Fifty three meetings is the number most vendors would put on a slide. It is also meaningless on its own, because nobody knows what would have happened if the agent had never run. So here is one of my own campaigns reporting that it did nothing.
440 people messaged, 53 booked. 60 held back at random and never contacted, 11 booked anyway. So the campaign caused zero bookings rather than 53, the system says so on its own screen, and it has stopped recommending that campaign entirely.
It refused to learn from a result it could not attribute, and it changed its own behaviour because of what it measured. It runs on live patient records under UK GDPR, which is a harder environment than most of the ones I am asked about.
Being precise: I am not claiming the campaign did harm. A holdout of 60 is far too small to carry that claim, and it would be the same bad statistics as claiming it worked. What it establishes is that there was no lift to find, and that the honest next move was to change the targeting rather than renew the spend.
An agent that can say this survives the budget review. Most cannot, which is why most get switched off in the quarter after someone asks.
How I lead it.
I work inside your team rather than alongside it, and the engagement is built to end. The last step is the one most people skip, and it is the reason the first three are worth paying for.
I read your market and your stack
Then I sit with the team who are actually meant to use the thing. Most agent projects are designed away from the people expected to open them, which is why reps go back to LinkedIn by the second week.
I ship into your environment
Your CRM, your data, your tenant. Not a demo on my infrastructure that you later have to rebuild. If security needs to sign off, that conversation happens in week two rather than the week before launch.
One page on what it touched and what it caused
Measured against a group held back on purpose. Same page, same cadence, whether the news is good or not. The week it causes nothing, you will hear that from me first.
Your team leads it without me
Named owner, documented decisions, and the reasoning behind what it remembers and for how long. If you would rather I stayed, there is a retainer. The point is that it is a choice rather than a dependency.
Memory that only believes what it measured
Two thirds of companies now have AI agents nobody uses. Not because the agents were bad, but because they forget everything between runs, nobody could prove they worked, and no one was trained to keep them alive.
Bolting on a memory tool does not fix that. It just remembers more, including the things that were never true and the ones that stopped being true. I build a layer that is harder to satisfy than that, and it runs inside your infrastructure rather than a vendor's.
It will not record a claim without the source
Anything above low confidence has to carry the customer's own words. No inference stored as fact, and every entry traceable back to the message it came from.
It forgets what stopped being true
An objection is worth re-testing after eighteen months. A constraint never expires. Memory that treats those the same is a system confidently acting on something the customer said in 2024.
It stops the send, not just informs it
Memory that only advises gets ignored. This sits in the path: if what it knows about a person means they should not be contacted right now, nothing goes out.
It learns from proof, not from activity
A campaign that ran without a control group teaches it nothing and is recorded as history. Only a result measured against people deliberately left alone is allowed to change what it expects next time.
Failure rates above: MIT 2026 on enterprise GenAI pilots, Deloitte Tech Trends 2026 on agent production rates, Gartner on agentic project cancellation.
Fixed prices. No discovery call to find out what it costs.
Most teams have bought AI and have nobody who has shipped it end to end. That is the gap I fill. I design, build and run the agents inside your environment, on your infrastructure, measured against a group that did not get them so you can tell what it actually caused.
Embed: 10 days
$7,500Ten days. Where revenue is going missing in dollars, each item checkable by you in under a minute, plus one working agent built on your own data that you keep either way.
- Fixed price
- One agent, running
- Credited against the build
Build Sprint
$35,000Five weeks. Two to three agents in production on your infrastructure, the memory layer underneath them, and your team trained to run it without me.
- In production, not a pilot
- Memory layer included
- 30 day support tail
Install
$15,000Two to three weeks. The memory layer and the tooling on top of the AI programs you already have, so they stop starting from zero and you can tell which ones earned their place.
- Works with what you own
- Enablement included
- Governance written down
Your infrastructure, always
IncludedEverything runs inside your environment. No third party holds your customer data, which is also the answer to the only question that stops these projects in healthcare and finance.
- Self hosted
- SOC2 compliance
- Data sovereignty
Ready to put agentic AI to work?
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Send me the agent nobody uses.
Tell me what you bought, what it was supposed to do, and what it actually did last month. I will tell you whether it can be made to prove what it caused, or whether it should be switched off. That answer is free either way.
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