Machine learning companies · Technology
Present the model that predicts one number the client already tracks.
A Coimbatore textile group plans yarn purchases from a spreadsheet and the purchase manager's memory. Every quarter it over-buys one count and runs short of another.
Scope, fee, reporting and term are agreed in writing after we have read your brief and spoken to you.
In one paragraph
Machine learning companies build predictive models for demand, churn, credit, pricing, quality and maintenance, and run them for clients. Marketing reaches the head of data or operations holding a spreadsheet forecast the team does not trust. Sales moves from a study on historical data to a live model. GullySales prepares problem pages and a study offer.
The finance head has read that forecasting models exist and asks a friend for names. Two come back, a search adds more, and the pages talk of neural networks and pipelines. Does any firm here know yarn?
The finance head does not want a platform. The need is a number to plan against, and an honest account of how wrong it will be. A sample forecast on someone's old data, with the error written down, answers that better than a list of algorithms.
Last updated 18 Oct 2026.
How buyers decide
How machine learning companies are chosen.
Buyers pick a problem first: which customers will leave, which invoice will be paid late, how much stock to hold, which machine will fail, what price clears the shelf. A firm with a page on that exact problem, and a plain note on what data it needs, gets noticed before anyone asks about algorithms.
The first purchase is a study on the client's historical data, with a written finding at the end. Buyers want to know what data is needed, how many months of it, who on their side must sit with the data team, and what happens if the data turns out too thin. A firm willing to say 'your data cannot support this yet' gives that buyer a reason to trust the rest of the proposal.
After the study, the questions move to who keeps the model alive. Models drift when buying habits change, and a head of operations wants to know who notices, who retrains, and what the client's own staff can do without a call. Cost of running it, and where the data sits, come up in the same meeting.
The problem
What usually goes wrong for machine learning companies.
What owners tell us on the first call, in their words.
“Buyers cannot tell a model builder from a dashboard builder”
Our site says insights and intelligence, and a prospect who needs a forecast ends up comparing us with a reporting tool.
“The data is never as clean as the sales call assumed”
We find missing months and two product codes for the same item only after signing, and the client feels misled.
“Accuracy gets asked for before we have seen anything”
The first call turns into a demand for a percentage, and anything we say without data is either a guess or a refusal.
“Models go live and then nobody owns them”
The client's team changes, the model drifts, and the next renewal conversation starts from an unhappy place.
“Our work sits inside client systems we cannot show”
A lender or a manufacturer will not let us name them, so our best results stay out of every pitch.
“Large firms take the big programmes”
A consulting name gets the board's attention and we only meet the data team after the budget is spent.
What we do
What we do for machine learning companies.
Everything included for machine learning companies.
Problem pages by prediction
One page for each thing buyers ask for, such as stock planning, late-payment risk or machine failure, saying what data goes in, what comes out and who acts on it.
A historical-data study offer
A written first project: the extract needed, the questions the study answers, how error is shown, and the point at which both sides can stop.
A data-readiness checklist
A short document listing the columns, the period and the people a study needs, with the common gaps named.
An accuracy explainer
A plain page that shows how a model is tested on months it has not seen, and what a wrong forecast looks like in rupees of stock or days of delay.
Anonymised problem stories
Write-ups such as a yarn buyer who kept over-ordering one count, with the client unnamed and figures left out unless the client approves them.
Upkeep and ownership note
A page that says who watches a live model, what triggers a retrain and what the client's analysts can do themselves.
How the result is measured
- Enquiries by prediction problem: forecast, churn, late payment, failure
- Data-readiness checklists returned complete
- Signed studies, and those stopped at the data stage
- Live models that grew out of a study
- Work referred by ERP or BI partners
- Months of history the client could supply, compared with what the study needed
Recorded as a baseline before work starts, so every later report has an honest comparison.
Every service, written for machine learning companies
Each has its own page: what it involves for machine learning companies, what you get, and what it is measured on.
- Lead generationFind the planner who already distrusts the forecast sitting in a spreadsheet.
- SEORank for the forecast a buyer needs, not for the technique behind it.
- Social media marketingPost the worked example, so a planner sees their own spreadsheet in it.
- Content marketingWrite the guide a purchase head reads before asking anyone for a forecast.
- Buyer personasKnow the four people who sit around a forecasting study before writing to any.
- CRMKeep each enquiry attached to the prediction problem and the data it needs.
- Follow-up systemsFollow up each study until the planner has a decision to make.
- WhatsApp marketingUse WhatsApp for the data-request thread, not for broadcasting the pitch.
- Digital marketingJoin search, outreach and partners so each prediction problem has a path in.
- WebsiteBuild the site around the numbers you predict, with the data requirements stated plainly.
Worth a page and a campaign of their own
One page per prediction problem · A study offer on the client's own history · Plain statements of data needed · How accuracy is measured and shown · Model upkeep after go-live · Anonymised problem write-ups · Introductions to heads of operations and finance · Handover to the client's team
Priority campaigns
Campaigns for what machine learning companies most want to sell.
Each one planned around when your buyers decide, and measured against the baseline.
Problem-led search and ads
Pages and ads for phrases such as demand forecasting for manufacturers, customer churn prediction and predictive maintenance for plants.
Heads of operations and finance outreach
Short messages to people who plan purchases or collections from spreadsheets, each built on one visible planning problem.
Sample forecast on public data
A worked example on a published dataset, with the error shown and the weak months pointed out.
Referral from ERP and analytics partners
Contact with ERP resellers and BI consultants whose clients ask for forecasts they do not build.
Beyond search
Where we reach buyers of machine learning companies, beyond Google.
Search matters, but it is rarely the only way this industry's buyers find a supplier.
Planning and purchase heads in manufacturing and distribution
Firms with obvious stock and demand swings, such as textile, auto-component and FMCG distributors, get outreach built on one planning problem.
Risk and collections heads at lenders
Credit teams read careful material written in general terms, and your own compliance adviser checks it first.
Subscription and app businesses
Founders and growth heads know their churn rate but cannot see who leaves next. The approach starts from that gap.
ERP resellers and BI consultants
Their clients ask for forecasts they do not build. An introduction tells them which of those requests to send to you.
Plant and maintenance managers
Failure prediction is explained on the shop floor's terms, one machine type at a time.
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.
- You want a guaranteed Google ranking or a guaranteed number of leads. Nobody honest can promise either.
- You need enquiries by next week and have nobody to answer them.
- You want posts and reach reported, not enquiries and orders.
How it works
How the work runs for machine learning companies
The work itself, in order. How an engagement starts, from your brief to a written scope, is set out on our method page.
- 1
Choose the problems you have solved
Past projects are sorted by prediction problem, keeping those where you can show method, error and a client who acted on the result.
- 2
Write for the planner
The site is rewritten around the number the buyer wants to predict, in words a purchase head would use.
- 3
Package the study
The study offer and the data-readiness checklist are shaped together, so a first project is easy to approve.
- 4
Reach the people who plan
Problem-led search, outreach and partner introductions are set up around the chosen problems.
- 5
Follow each study to a decision
Each study is tracked to a proposal or a stop, with the reason a stalled one stopped.
Where this fits
Three pieces of sales work, in this order.
GullySales builds every engagement from three pieces of sales work. This page is not one of them, but the same three steps apply to the business behind it.
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
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 machine learning companies ask before they call.
Not here? More answers, or ask on WhatsApp.
Can you publish an accuracy figure for our models?
Our client data is poor. Do we hide that?
We work for banks and cannot name anyone. Is that a problem?
Why would a ten-person firm beat a big consultancy?
How is this different from marketing an AI development company?
How much does it cost?
How long is the contract?
How soon will we see results?
Who will actually do the work?
What do you need from us?
Part of Technology. See the other industries in the sector.
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.