Good Remedy / We make AI work

Does your AI actually work?

Does it lose the plot? Make things up? Forget what you already fixed? Say it checked something it did not? Tell you the job is done when it obviously is not?

AI can be incredibly capable and still make you babysit the whole job. Good Remedy works on that part.

Book a 20-minute conversation
Show us one job. We will use the conversation to see whether there is a useful first step.
Professional reviewing documents beside a laptop
The question is not whether the AI can make something impressive. It is whether the thing actually works.
The everyday problem

AI is smart. That is not the same as dependable.

You do not need a new vocabulary to recognize the problem. You already know what it feels like.

Lose the plot?You start with one job. An hour later it is quietly solving a different one.
Make things up?Facts, sources, numbers, explanations. Whatever fills the hole.
Pretend it did things?“I checked the file.” “I tested it.” “I reviewed the sources.” Did it?
Forget corrections?You fix the mistake. It agrees. Three turns later the old mistake is back.
Call fake done?The work looks polished, so the AI announces success while important pieces are still missing.
Need constant babysitting?Your best people are still remembering the context, checking the claims, fixing the handoffs, and keeping the whole thing on the rails.

Does your AI work, or are you the thing making it work?

What Good Remedy does

We work on the part between “wow” and “this actually works.”

We do not make the model sound smarter. We make it harder for the job to quietly drift, fake, forget, or stop halfway through.

Keeps the job from drifting

The goal, the important facts, the corrections, and the decisions stay attached to the work.

Makes the AI show what it did

If it says it checked the file, used the source, or finished a step, that should be something you can verify.

Catches fake “done”

Good-looking is not the same as finished. We make the missing part impossible to hide.

Keeps fixes from disappearing

The correction you made yesterday should still matter tomorrow.

Gets the work back on track

When the AI goes sideways, the whole job should not snowball with it.

Finishes with something that works

Something another person can actually read, use, send, build from, or make a real decision with.

Who this is for

If AI is already in the work, this should feel familiar.

Two colleagues reviewing documents together
Agencies & client-service teams

Client recommendations, proposals, reporting, research, strategy decks and other work that has to hold up in front of somebody else.

Operators & revenue leaders

Sales, account work, recurring research, decision support and operating jobs where senior people are still the hidden quality-control system.

AI & innovation leads

You already have models or workflows in use. The problem is what happens after the demo, once the work gets longer, messier, or changes.

Implementation & automation partners

You can build the automation. You need the job around it to survive handoffs, exceptions, changing instructions and real-world use.

Common entry jobs: proposals, client delivery, reporting, account research, sales follow-up, implementation work, decision support and workflow automation.
Three ways in

Build it. Fix it. Check it.

You do not need to diagnose the system before you call us. Tell us what keeps happening.

Build

You know the job. The AI still needs you to make it work.

We turn a recurring business job into something AI can help do without depending on one senior person to hold the whole thing together.

  • Define what good actually looks like
  • Set up the sources and handoffs
  • Make the job repeatable
  • Keep the human decisions human
ExampleOur account-research workflow is ad hoc and dependent on senior people.
Build itScoped after discovery
Fix

The AI workflow exists, but the team keeps repairing it.

We find where the job is losing context, making bad assumptions, creating rework or depending on manual rescue, then rebuild the weak part.

  • Find where the work breaks
  • Stop repeating the same correction
  • Make handoffs survive
  • Reduce hidden babysitting
ExampleThe workflow exists, but the team keeps restoring context and correcting outputs manually.
Fix itScoped after discovery
Check

It looks done. You want to know if it really is.

Give us one consequential piece of AI-assisted work before it goes out, gets relied on, or becomes somebody else's problem.

  • What holds up
  • What needs evidence
  • What is still a human decision
  • What should not go out yet
ExampleA client recommendation is about to go out and someone needs to know whether the facts and assumptions hold up.
$3,500Diligence Readout
The paid wedge

Diligence Readout

$3,500
Five business days / one work object
+What holds up
+What needs evidence
+What remains a human decision
+What should not go out or be used yet
+What should happen next
Book a 20-minute conversation
What you get

No guessing what the deliverable is.

Bring one proposal, report, recommendation, deck, research package, plan or other important AI-assisted job. We return a bounded Diligence Readout so the unresolved parts cannot hide behind polished output.

Illustrative Diligence Readout

Client recommendation / pre-send check

Example of the public-facing result. Not a customer result.
Holds up

The core recommendation matches the supplied facts and the stated objective.

Needs evidence

Two market-size claims are stated confidently but have no supporting source attached.

Human decision

The pricing exception and final client position still belong to the account owner.

Should not move yet

One performance claim depends on an assumption the current record does not support.

An actual internal example

The proposal looked ready. It wasn't.

17slides that looked close to ready
→
13stronger slides after the work was actually finished

The strategy was good. Some of the work underneath it was not finished. Good Remedy kept what worked, corrected what did not, and removed four slides that were doing more pretending than helping.

Internal example. Not a claim of customer ROI, market validation, or universal performance improvement.
Who is behind it

People who have actually had to make work hold together.

Good Remedy is operator-led. The site should not make you hunt to figure out who you are talking to.

BL Benjamin Lopez
Founder / Product & Operations

Benjamin Lopez

Benjamin built Good Remedy after a career in operations and client-facing systems across Bloomberg, Mount Sinai, BAYADA and multi-location healthcare. His focus is the part between an AI answer and work another person can actually rely on.

BloombergMount SinaiBAYADAOperations + AI-assisted work
JC Jeff Cohen
Commercial Strategy & Client Development

Jeff Cohen

Jeff brings more than 20 years of B2B sales leadership across Fox News Digital, Thrillist, Macy's Media Network, American Media and Uproxx. He leads commercialization, buyer conversations and client development for Good Remedy.

20+ years B2B sales leadershipFox News DigitalThrillistMacy's Media NetworkUproxx
Useful before you call

Five questions before AI-assisted work becomes company action.

Does it know the job? Can it show the facts? Did it really do what it says? What is still a human decision? Does the thing actually work?

Send me the one-pager →
Two professionals reviewing documents together
Show us one job

Book a 20-minute conversation.

A deck. A proposal. Research. Reporting. A recommendation. An AI workflow that keeps breaking. Tell us what is happening and we will use the conversation to see whether Good Remedy is a fit.

This starts the conversation by email. Nothing is uploaded to a third-party form service. We will reply with scheduling options.