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Machine learning companies · Lead generation

Find the planner who already distrusts the forecast sitting in a spreadsheet.

Lead generation for a machine learning company means reaching the people who own a number they cannot predict, such as stock, churn, late payment or machine failure. GullySales works on problem-led pages, a study offer and outreach that opens with the buyer's own planning problem.

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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 lead generation means

Lead generation is the combined work of every channel that brings in a new enquiry, whether from search, adverts, referrals or a stall at an exhibition. GullySales runs these channels together and reports the number of enquiries each one produced, not the activity behind it.

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.

  • “The buyer searches for a number, not a method”

    A purchase head wants next quarter's yarn requirement, and a collections lead wants to know which invoices will slip. Neither types neural network into a search box. A lead source that speaks in algorithms misses the person holding the spreadsheet.

  • “An enquiry without data is half an enquiry”

    A message saying we want a forecast tells you little. The useful lead names the thing to predict, the system the records sit in and how far back they go. Without those three facts, the first call becomes a guessing exercise.

  • “The first lead is a study, not a model”

    Buyers rarely approve a live model on first contact. They approve a bounded look at their own history. Lead generation that offers only a full build asks the buyer to commit before anyone has seen the data.

  • “Partners hold leads the website will never see”

    ERP resellers and BI consultants hear clients ask for forecasts they do not build. Those requests never reach a search engine, so a firm without partner introductions only sees part of the demand.

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. Prediction-problem landing pages

    One page for each thing buyers want predicted, naming the input data, the output and the person who acts on it.

    Result: Each enquiry arrives already tagged with the problem the buyer wants solved.

  2. An enquiry form that asks about data

    Fields for what is to be predicted, which system holds the records and how far back they go.

    Result: Every lead carries enough detail for a sensible first conversation.

  3. A study offer page

    A bounded first project on the client's own history, with the stopping point written out for both sides.

    Result: A finance or operations head can approve a small step without a long procurement.

  4. Outreach built on one planning problem

    Short messages to planning, risk and collections heads that open with a visible forecasting problem in their trade.

    Result: Message drafts that start with the recipient's number, not with machine learning.

  5. A partner introduction note

    A one-page description of the problems you take on, written for ERP resellers and BI consultants to forward.

    Result: Partners know which client requests to pass to you.

How the result is measured

  • Enquiries generated
  • Cost per enquiry
  • Enquiries by source

Recorded as a baseline before work starts, so every later report has an honest comparison.

Read the full demand and lead generation page

What to capture

Ask for the prediction problem and the records before any call.

The number the buyer wants predicted
Next month's demand for one product, a borrower's chance of default, a machine's time to failure. A buyer who cannot name it is not ready for a study.
Where the records sit today
ERP, loan system, historian or spreadsheet. It tells your team in advance how much cleaning the data will need before any model.
Years of history available
A forecast needs enough past cycles. A short history means the first piece of work is data collection, which you should say plainly.
Who owns the data
The person who can release it. Many studies stall because the planner asks and the data owner was never told.
What decision the output feeds
A reorder, a collections call, a maintenance slot. It shows whether a model will ever be used or is only curiosity.

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 cannot yet describe one prediction problem in a sentence, so a lead page would attract buyers you have no clear answer for.
  • No one on your side can read an enquiry about data and reply with a real question, so leads would only be forwarded.
  • Your delivery team is full with live models that need care, and a new study would be taken on without anyone to run it.

How it works

How the lead generation 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

    Understand your capacity

    We ask how many enquiries your team can answer well, who answers after hours, and what a good enquiry looks like. Without this, the system fills a funnel nobody is watching.

  2. 2

    Audit current sources

    We read your call log, WhatsApp chats, enquiry sheet and platform dashboards. The baseline is recorded: enquiries by source, reply time, conversion and cost per order.

  3. 3

    Strategise the channel mix

    Search and maps are chosen for people ready to buy, ads for people who are not yet looking, and content and referrals for trust.

  4. 4

    Implement capture and follow-up

    Pages, forms, tags, the lead record and the first-reply message are built and tested end to end. We submit test enquiries ourselves before any media money is spent.

  5. 5

    Measure and optimise

    Enquiries are traced to source and judged by what became orders, not by volume alone. Channels that fill the sheet with poor enquiries are cut back. How often you are reported to is agreed in writing.

Where this fits

Three pieces of sales work, in this order.

This work sits inside sales process design, at stage 1, prospecting and lead generation. 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
Should we chase every enquiry that mentions AI?
No. Sort by whether the buyer can name a number to predict and has records behind it. Enquiries that only want AI somewhere are best answered with a short note on what you do and do not build.
Where do our best leads come from?
Your own enquiry log will show it. GullySales sets up the tagging so you can see which problem pages, outreach and partners bring studies that get signed, and which only bring conversations.
Do we need a large ad budget?
Not to begin. A few well-built problem pages and careful outreach to named roles come first. Paid search is added for the problems where buyers already search by name.
Should the enquiry form ask about the data?
Yes, in two or three plain questions. A form that asks for the problem, the system holding the records and the years of history lets you reply with substance and removes enquiries from people who have no data to start with.
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.
How soon will we see results?
It depends on the trade, the channel and what is already in place. After we have read your brief and spoken to you, we say what we would expect to move first and how it will be measured against the baseline. Nothing here is guaranteed.
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: leads and follow-up

Also for machine learning companies

Or start from the overview: sales and marketing for machine learning companies.

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