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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.

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Scope, fee, reporting and term are agreed in writing after we have read your brief and spoken to you.

Realistic editorial illustration of software development companies at work in India

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.

Also for machine learning companies

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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

Read the full social media marketing page

Posts to plan

Each post should show one prediction problem worked on open data.

Why a moving average fails after a promotion
A chart on public retail data showing the error around a sale, with a short note on how a model adds the promotion as an input.
What a clean sensor log looks like
A before-and-after table from a public equipment dataset, aimed at a maintenance head who has never seen a model-ready file.
Questions to ask before sharing data
A checklist post for planners on masking, ownership and who signs off, with no client named.
Founder explains a missed forecast
A candid post from your own work or a public dataset, showing what went wrong and what was changed in the method.
Engineer walks through a validation split
A diagram showing how past data is held back to test a model, written so a finance head can follow it.

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

Not for

It is not the right fit if.

  • Every example you have comes from a client who has not agreed to be shown, so the feed would repeat generic definitions.
  • No one will answer comments from planners and data heads, which are the first signal a buyer sends.
  • You want followers, not enquiries, and have no study offer for the people who read the posts.

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. 1

    Confirm audience, platforms and access

    We note which platforms your buyers use and check that the pages sit under your own business accounts, with you as owner and us as added roles.

  2. 2

    Review the current presence

    We read the existing pages, the posts that drew replies and the gaps in posting.

  3. 3

    Build the calendar

    Themes, formats and dates are set out for your approval, with space for offers and news.

  4. 4

    Create, approve and publish

    Posts are made, approved by you and scheduled. Replies to the public are handled in your voice.

  5. 5

    Record enquiries and adjust

    Enquiries are logged against the post or platform where they began, and the mix changes to follow what brings them.

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.

  1. 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.

  2. 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.

  3. 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.

Tell us what you need Your brief is read before we call. Scope, fee and term in writing.

FAQ

Questions owners ask before they call.

Not here? More answers, or ask on WhatsApp.

Tell us what you need
Which platform should we start with?
LinkedIn, because the people who plan stock, collections and maintenance read it for work. Other platforms can be added later if a clear audience shows up there.
Is it safe to share model results?
Share only results from public data or from your own test, with the method described. Anything that came from a client needs their written agreement first, and credit work needs your compliance adviser.
Do we need a video or a podcast?
No. A clear chart and a short explanation do the work for this audience. A recorded walk-through of a worked example can come later, once the written posts show what readers respond to.
How do we post when every project is under confidentiality?
Rebuild the example on public data and describe the method, not the client. A forecast on a published retail or weather dataset shows how you think. Never post a client's figures, even masked, without written permission from the client.
How much does it cost?
There is no price list, because the work differs by business. The fee is scoped after we have read your brief and spoken to you, and put in writing before anything starts, split into our time, your media spend and production.
How long is the contract?
The term is agreed in writing after we have spoken, along with the fee and the reporting. It is never a default twelve months, and renewal is decided on the numbers against the baseline recorded at the start.
Who will actually do the work?
The person you speak to before anything starts. We work from one office in Nagarbhavi, Bengaluru, with no account managers in between.
What do you need from us?
Your brief, and a conversation. After that, access to whatever this work touches, such as your website, your accounts or your enquiry record, and time with the people who handle your enquiries.
What if we are not happy with the work?
Tell us and the plan changes. Everything is reported against the baseline, so a number that is not moving is visible to both sides. The term is in your written scope, and the refund and cancellation policy sets out the rest.

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.

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