Machine learning companies · Social media
Post the worked example, so a planner sees their own spreadsheet in it.
Social media marketing for a machine learning company means showing the thinking behind a prediction in a form a planner or founder can follow in a feed. GullySales works on a small set of posts built from real problems, without client data or broad claims.
Scope, fee, reporting and term are agreed in writing after we have read your brief and spoken to you.

What social media means
Social media is the ongoing posting your business does on platforms such as Instagram, Facebook and LinkedIn to stay visible to customers and prospects between purchases. GullySales plans a posting calendar, writes captions and designs the images so the page looks active and answers what a visitor would ask.
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Sales and marketing for machine learning companies, the overview.
Last updated 18 Oct 2026.
For machine learning companies
Where it usually goes wrong, and what we would do.
“A feed rewards the plain example”
A post that shows a forecast against actual sales for a made-up retailer is understood in a glance. A post about model architecture is scrolled past. The reader decides in seconds whether the problem looks like theirs.
“LinkedIn carries the buyers, other platforms mostly carry noise”
Heads of operations, finance and data spend their working hours on LinkedIn. Effort spread across every platform thins the posts that matter. Pick the one place where the planner already reads.
“Client confidentiality limits what can be shown”
Credit, payment and customer data cannot appear in a post. Published datasets and invented examples let you show method without breaking a contract. Buyers can tell the difference and respect the care.
“Founders and engineers need different posts”
A founder wants to know whether churn can be predicted at all. An engineer wants to see the validation method. Writing one post for both pleases neither, so each post is aimed at one reader.
What we do
What we deliver for machine learning companies.
Every deliverable, what it covers for you, and the result it is there to produce. Nothing here is an extra.
A post series on prediction problems
Posts that each take one problem, such as late payment or machine failure, and show input, output and what the planner does next.
Result: A bank of posts you can publish in turn without rewriting.
Worked examples on public data
Forecasts built on published datasets, with error shown and weak spots marked.
Result: Examples that carry no client information and still show method.
A founder profile rewrite
The profile and featured section rewritten around the problems you solve and the study you offer.
Result: A profile that tells a visiting buyer what to ask for.
A comment and reply routine
Short guidance on replying to questions on posts, and on moving a serious question to a call.
Result: Replies that stay accurate and lead somewhere.
A check on posts before publishing
A list for removing client names, figures and anything a compliance adviser would query.
Result: Each post passes the same review before it goes out.
How the result is measured
- Posts published
- Engagement rate
- Enquiries from social profiles
Recorded as a baseline before work starts, so every later report has an honest comparison.
Who it is for
This is written for these machine learning companies.
- Demand and inventory forecasting specialists
- Credit scoring and collections modelling firms for lenders and NBFCs
- Churn and customer lifetime value modelling for subscription businesses
- Predictive maintenance and quality prediction for plants
- Pricing and promotion optimisation for retail and FMCG
- Fraud and anomaly detection for payments and insurance
- Time series and sensor data specialists
- MLOps and model monitoring providers
How it works
How the social media work runs
The work itself, in order. How an engagement starts, from your brief to a written scope, is set out on our method page.
- 1
Understand your audience and platforms
We identify which platforms your specific buyers actually use and what kind of content they respond to.
- 2
Audit current presence
We review existing pages, past content performance and any gaps in consistency.
- 3
Build the content calendar
A plan is created, balancing genuine value with occasional direct offers.
- 4
Implement and post
Content is created and published consistently on the agreed schedule.
- 5
Measure and optimise
We track enquiries alongside engagement and adjust the content mix based on what actually performs.
Where this fits
Three pieces of sales work, in this order.
This work sits inside sales process design, at stage 0, foundation. The audit comes before it, and sales management keeps it running.
Step 1
Sales audit
Find where deals are being lost and what to fix first.
Start here if you cannot say where orders are lost.
Step 2 · you are here
Sales process design
One written way to sell, from first enquiry to repeat order.
Start here if every salesperson sells differently.
Step 3
Sales management
A target, a review routine and someone who owns the number.
Start here if the team has targets and no routine.
FAQ
Questions owners ask before they call.
Not here? More answers, or ask on WhatsApp.
Which platform should we start with?
Is it safe to share model results?
Do we need a video or a podcast?
How much does it cost?
How long is the contract?
Who will actually do the work?
What do you need from us?
What if we are not happy with the work?
More for machine learning companies
Everything else we would do for you.
Closest to this: content and social
Also for machine learning companies
Or start from the overview: sales and marketing for machine learning companies.
Your next practical step
Tell us what you are trying to grow, and we will read it before we call.
Eight short steps, mostly taps. Skip anything you would rather answer on the call.