The repetitive parts of selling and marketing stop eating your team's week.

Your team retypes the same summaries and drafts the same replies every day. Gully Sales finds which of those steps AI can take over safely, builds them into the tools you already use, and keeps a person in charge of what customers see.

A shortlist of tasks AI can take over, ranked by hours saved and risk.
Working automations inside the CRM and tools your team already opens.
A human review checkpoint on anything that reaches a customer.

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Gully Sales Private Limited builds sales, marketing and revenue operations systems for small and medium businesses across India.

In one paragraph

What is AI Sales and Marketing Automation Services?

AI sales and marketing automation puts models inside steps your team already performs: reading enquiries, drafting replies, summarising calls, updating records and preparing lists. Gully Sales grounds each one in your own records rather than the open internet, writes the instructions it follows, sets the point at which a person approves before a customer sees anything, and measures what each run costs against the time it saves.

The problem

Your team spends its selling hours on typing, not on customers.

It is Monday morning. A sales executive copies twenty enquiries from WhatsApp and the website form into a spreadsheet. Someone else writes the same follow-up email for the eleventh time this month. Your marketing person spends an afternoon tagging leads by industry so a mailer can go out. None of this work is difficult. All of it is necessary. And every hour of it is an hour nobody spent talking to a buyer, preparing a quotation or rescuing a stalled deal.

You will recognise it as

Your team retypes the same customer information into two or three systems, because none of them speak to each other.
Enquiries wait hours for a first reply, not because nobody cares, but because nobody has seen them yet.
Follow-ups, quotations and proposals are written from a blank page every time, so their quality depends on who is free.
Reports and campaign lists are assembled by hand at month end, and one person is the only one who knows how.
Someone has quietly started pasting customer details into a public chatbot, and nobody has agreed what is allowed.
You already pay for tools with AI features in them, and after the demo nobody in the business switched them on.

What it costs the business

Slow first replies lose enquiries to whoever answered sooner, and the loss never appears in a report because the buyer never tells you.
Senior people do junior work, so coaching, key accounts and pricing decisions keep getting postponed to next week.
Output quality swings with whoever happens to be free that day, which the customer experiences as an inconsistent business.
When a person leaves, the manual routine leaves with them, and the team rebuilds it from memory and guesswork.

Why it persists. Each task is small on its own. No single one of them justifies a project, so all of them stay. The tools that could take them over sit unused because somebody has to decide what the AI may touch, where its information comes from and who checks the output before a customer sees it. That decision sits between sales, marketing and whoever manages the software, so it belongs to nobody. Meanwhile the experiments that do happen are personal and unrecorded, one executive at a time.

If it stays unresolved. The manual load grows with the business, so every new customer costs more attention than the last one. Hiring becomes the only way to grow, and the new person inherits the same routines. Informal AI use spreads through the team without a record, and you discover where your customer data went only when something goes wrong.

What changes

Your systems take the routine work, and your people keep the judgement work.

In the first weeks

A written list of the sales and marketing tasks in your business, with the hours each consumes and the risk of automating it.
An agreed shortlist of the workflows to build first, and the reason each one was chosen ahead of the others.
A plain rule for what AI may and may not do with customer information in your business.

In how the work runs

Enquiries are captured, summarised and placed in front of the right person without anyone retyping them.
Drafts arrive ready for a human to check and send, instead of being written from nothing each time.
Every automated step has a named owner, a review checkpoint and a way to switch it off.
When an automation meets something it cannot handle, it hands the case to a person instead of guessing.

In sales and marketing

Faster first responses, because the gap between an enquiry arriving and somebody noticing it is removed.
Hours returned to the sales team, which can be spent on conversations rather than administration.
Steadier follow-up, so fewer enquiries go quiet simply because a busy week got in the way.

In what management can see

A log of every automated action, so you can see what the system did, on what basis, and who approved it.
A monthly view of runs completed, exceptions raised, corrections made and hours returned.

Over the longer term

A business where the next automation is a small addition, not a new project, because the data and the rules are already in order.
A team habit of treating AI output as a draft to be checked, rather than an answer to be trusted.

Gully Sales controls the workflows built, the checkpoints agreed, the training given and the monitoring in place. Response speed, conversion and revenue depend on your market, your offer and how your team uses what is built. We report the two separately, so you can see which is which.

Who it is for

This is for you if the routine work has outgrown the people doing it.

The businesses it suits

Businesses with a CRM or marketing tool already in place, whose team finds it slower than working by hand.
Owners who keep hearing that AI could help and want a specific answer for their own business, not a general one.
Sales heads whose team spends more of the day on records, lists and drafts than on customers.
Marketing teams whose campaign preparation takes longer than the campaign itself.
Growing companies where enquiries arrive from several channels and are handled differently in each one.
Businesses that tried an AI tool, liked the demonstration, and never got it into daily use.

What usually prompts the call

Enquiry volume has grown and first replies have slowed with it.
A key coordinator has left, taking an undocumented manual routine with them.
You are about to hire another person mainly to keep up with administration.
A tool you already pay for has AI features nobody has switched on.
You have found staff putting customer details into public AI tools.

What Gully Sales does

The work, component by component.

Workflow selection and prioritisation

We map how work actually moves through your sales and marketing week, marking every point where someone reads, decides, retypes or waits. Each step is scored on the time it consumes, how often it repeats, how structured its input is, and what it would cost if the output were wrong.

Why it matters:: Most disappointing AI projects automate the interesting task rather than the expensive one. Scoring stops the choice being made by enthusiasm in a meeting.
You receive:: A scored workflow inventory with a recommended build order and the reasoning behind it.
Business value:: Your budget goes to the few steps that return the most time at the lowest risk, and you can see why.

Prompt, trigger and action design

For each chosen workflow we write down what starts it, what the AI is asked to do, what information it is given, what it produces and where that output goes. The instruction given to the model, its role and the boundaries of its task are written as a specification a non-technical person can read and argue with before anything is built.

Why it matters:: An automation nobody can describe in one sentence cannot be reviewed, corrected, or handed to the next employee who takes the job.
You receive:: A one-page design per workflow: trigger, inputs, action, output, owner and off switch.
Business value:: Your team knows exactly what the system is doing, which is what makes them willing to use it and able to correct it.

Data dependencies and readiness

An AI step is only as good as the records it reads. We identify the fields each workflow depends on, check whether those fields are actually filled and accurate today, and either correct them or design the workflow to work without them. Where the data must be fixed first, we say so before the build starts.

Why it matters:: Automation applied to poor records does not produce poor results slowly. It produces them at volume, in your customers' inboxes.
You receive:: A data dependency map and a list of fields to be corrected, merged or made mandatory.
Business value:: You avoid paying to automate a process that was always going to fail on the quality of its inputs.

Exception handling and human checkpoints

We define what each automation must not attempt: complaints, unusual requests, large orders, anything outside its confidence. Those cases stop and reach a named person with the context attached. Anything that goes to a customer passes a human check until the workflow has earned a longer leash, and you decide when that is.

Why it matters:: The risk in automation is not the ordinary case. It is the unusual one handled confidently and wrongly, at speed, in writing.
You receive:: Exception rules, escalation paths and review checkpoints documented for every workflow.
Business value:: No customer receives an automated answer to a situation that plainly needed a human being.

Build and structured testing

We build inside the systems you already use and test on real examples drawn from your own history, not sample data. Each workflow runs against a set of past cases where the right answer is known, including the awkward ones, and we record every output, every correction and every rule changed as a result.

Why it matters:: A demonstration proves a workflow can succeed once. A test log against your own difficult cases shows how it behaves on an ordinary Tuesday.
You receive:: A test log of sample inputs, outputs produced, corrections applied and the changes each one caused.
Business value:: You see the quality with your own eyes before a single customer is affected by it.

Adoption and team training

The people who do the work today are shown what the automation does, what it does not do, how to correct it and how to stop it. Training is per role and uses your own live examples. We also agree a short, readable rule for what staff may put into AI tools and what stays inside your systems.

Why it matters:: An automation that a team distrusts gets quietly bypassed, and then you are paying for the tool and the manual work together.
You receive:: Role-based training sessions, recordings, a one-page guide per workflow and a written acceptable-use rule.
Business value:: The workflows get used as designed, and the team can tell the difference between a good output and a wrong one.

Monitoring, tuning and governance

After go-live we watch what actually happens: how many runs completed, how many exceptions were raised, how often a human corrected the draft, and where output quality drifts as your products, prices or people change. Tuning is done against that record, and reviewed with you.

Why it matters:: AI outputs drift as the business changes around them. Without monitoring, a workflow that was accurate at launch becomes inaccurate without anybody noticing.
You receive:: A monitoring report showing runs, exceptions, correction rates and hours returned, reviewed on an agreed rhythm.
Business value:: Problems are found in your dashboard rather than in a complaint from a customer.

What you will have at the end.

A scored inventory of sales and marketing tasks, with hours consumed and automation risk noted for each one.
A one-page design specification for every workflow built: trigger, inputs, action, output, owner and off switch.
A data dependency map naming the fields each workflow reads and which of them must be corrected first.
Written exception rules and escalation paths, with a named person responsible for each type of case.
The working automations themselves, live inside your CRM, marketing tool or messaging channels.
A test log showing sample inputs, the output produced, the corrections made and the rules changed before go-live.
An anonymised sample pack of before-and-after outputs taken from your own workflows.
A plain-language acceptable-use rule for AI and customer data, written for your team rather than for lawyers.
Role-based training sessions with recordings and a one-page guide for each workflow.
A monitoring report or dashboard covering runs, exceptions, corrections and hours returned.
A handover document so a new employee can run, change or switch off the automation without calling us.

How it runs

The engagement, step by step.

1
Discovery and workflow mapping
We sit with the people who do the work and follow a week of sales and marketing activity end to end: where enquiries arrive, who touches them, what gets retyped, what gets waited on and where things fall through. We look at the systems as they are actually used, not as the process document describes them.
You provide:
Access to the team for interviews, a view of your CRM and marketing tools, and examples of recent enquiries and campaigns.
We produce:
A map of the current workflow with time, volume and handover points marked at each step.
Done when:
You recognise your own business in the map and agree it is accurate.
2
Candidate scoring and scope agreement
Every candidate task is scored on time consumed, frequency, how structured its inputs are, and the cost of a wrong output. We recommend a small starting set, explain what we deliberately left out, and agree the scope in writing before anything is built.
You provide:
A decision on priorities and one person authorised to approve scope.
We produce:
A scored candidate list, a recommended build order and a written scope for the first phase.
Done when:
The scope is signed off and both sides know exactly what is being built first.
3
Data and access preparation
We check the records each chosen workflow will read: are the fields filled, consistent and current. Gaps are either corrected, made mandatory at entry, or designed around. System access, permissions and the rule for what customer data may leave your systems are settled here.
You provide:
Administrator access to the relevant systems and a decision on data handling boundaries.
We produce:
A data dependency map, a correction list and a written data handling rule for AI steps.
Done when:
Each workflow has reliable inputs, or a documented reason why it is being deferred.
4
Design and build
Each workflow is specified on one page, reviewed with you, and only then built inside your existing systems. Triggers, actions, exception rules and review checkpoints go in together, so there is never a version that runs unsupervised because the safeguards were left for later.
You provide:
Review of each design page and a subject expert who can say what a good output looks like.
We produce:
Approved design pages and the configured workflows in a test environment.
Done when:
You have approved the design for each workflow and seen it running on test cases.
5
Testing and supervised pilot
We run the workflows against real past cases where the correct answer is known, including the difficult ones, and record every output. Then a supervised pilot runs on live work with a person reviewing each output before it goes anywhere near a customer.
You provide:
Historical cases, a pilot team, and reviewers willing to mark output honestly.
We produce:
A test log, a corrections summary and the tuned workflows ready for wider use.
Done when:
Output quality is acceptable to your reviewers on live cases, in their own judgement.
6
Adoption and handover
Training runs per role using your own examples. Owners are named, the acceptable-use rule is circulated, and the handover document is written so the knowledge does not depend on us or on one enthusiastic employee.
You provide:
Team time for training and a named internal owner for each workflow.
We produce:
Training sessions with recordings, one-page guides and the handover document.
Done when:
The team is running the workflows without needing us for daily operation.
7
Monitoring and tuning
Runs, exceptions and correction rates are reviewed on an agreed rhythm against the baseline we recorded at the start. Where output has drifted or the business has changed, rules and instructions are tuned and the change is logged.
You provide:
Attendance at the review and honest feedback from the people using the workflows.
We produce:
A monitoring report, a tuning log and a recommendation for the next workflow to build.
Done when:
Each review closes with a written record of what changed and what will be watched next.

Ways to work with us

Choose the depth that matches where you are today.

Automation opportunity assessment

A short engagement that maps your sales and marketing workflows, scores the automation candidates and returns a written recommendation with a build order. It stands on its own; you may take it to any implementation partner.

Workflow build sprint

A defined build of an agreed set of workflows: design, data preparation, exception rules, testing, training and handover. Ends with your team operating what was built and a document that lets them maintain it.

Build and run

The build, plus a continuing arrangement in which we monitor runs and exceptions, tune the workflows as your products and team change, and add new automations in an agreed order.

Review of existing automation

For businesses that have already built AI workflows and are unsure whether they are safe, accurate or actually used. We audit what is running, test it against real cases and report what to fix, keep or retire.

Why Gully Sales

What you are actually choosing when you choose us.

We start with your week, not with a tool.

The first question is where your team's hours actually go. Software follows that answer. If the honest recommendation is to fix a process or a data field rather than buy anything, that is the recommendation you get.

Sales and marketing knowledge, not only technology.

Gully Sales works on pipelines, enquiries, campaigns and customer conversations every day. That is why the automations we design fit how selling in an Indian SMB actually happens, rather than how a workflow diagram imagines it.

A person stays in charge of what customers see.

Human review checkpoints are built in from the start, not added after an embarrassing message goes out. You decide, workflow by workflow, when something has earned the right to run with lighter supervision.

Built inside the systems you already pay for.

Wherever your current CRM, marketing tool or messaging platform can do the job, we use it. New software is proposed only when there is a specific gap, and we say what the gap is.

Written down, so it survives your team changing.

Every workflow has a design page, an owner, an exception rule and a handover document. When the person who ran it moves on, the automation does not become a mystery nobody dares touch.

We tell you what we do not control.

We are accountable for the workflows, the testing and the monitoring. Market conditions, your pricing and how your team follows up are yours. Our reporting keeps the two apart instead of blurring them into one number.

Where it applies

The same service, in different businesses.

Industry	The situation	How it applies	Likely benefit
Industrial manufacturing and engineering	Enquiries arrive by email, phone and marketplace portals with drawings and specifications attached, and an engineer reads every one before it can be quoted or routed.	An AI step reads each enquiry, extracts the product, quantity, location and application, drafts a summary and routes it to the right engineer, holding anything unusual for a person to open first.	Technical staff open enquiries already summarised, so the first response goes out sooner and fewer enquiries sit unread over a busy production week.
Multi-branch clinics and healthcare	Patient enquiries come through calls, forms and messages across several branches, and front-desk staff handle them differently, with follow-up depending on who was on duty.	Enquiries are captured into one record, summarised with the service and branch identified, and follow-up drafts are prepared for staff to review and send, with anything clinical or sensitive escalated to a person immediately.	Every branch answers to the same standard and no enquiry is lost between shifts, while clinical judgement stays entirely with your people.
Building materials and hardware distribution	Dealer and site enquiries arrive through WhatsApp all day, order details live in chat threads, and the CRM is updated in the evening if at all.	Conversations are summarised into structured records, repeat order patterns are flagged for the territory manager, and reminder drafts for quiet dealers are prepared for a human to approve and send.	The pipeline reflects what is actually happening in the field, and dealers who have stopped ordering are noticed within the month rather than at the quarterly review.
Professional services and design studios	Every proposal is written from scratch, senior time goes into assembling scope and case examples, and enquiries wait for the one person who writes well.	A drafting workflow assembles a first proposal from your approved scope language, past project examples and the enquiry details, which the consultant then edits and prices before it goes out.	Proposals reach the client sooner and read consistently, while pricing and professional judgement remain with the consultant who owns the relationship.
Education and training institutes	Admission enquiries surge in season across forms, calls and social channels, and the counselling team cannot reach everyone while interest is still warm.	Enquiries are captured, categorised by course and readiness, and given a prepared first response for the counsellor to check, with genuinely interested applicants pushed to the top of the call list.	Counsellors spend the season talking to applicants who are ready to decide, instead of sorting a list that keeps growing faster than they can work through it.

Industrial manufacturing and engineering

The situation:: Enquiries arrive by email, phone and marketplace portals with drawings and specifications attached, and an engineer reads every one before it can be quoted or routed.
How it applies:: An AI step reads each enquiry, extracts the product, quantity, location and application, drafts a summary and routes it to the right engineer, holding anything unusual for a person to open first.
Likely benefit:: Technical staff open enquiries already summarised, so the first response goes out sooner and fewer enquiries sit unread over a busy production week.

Multi-branch clinics and healthcare

The situation:: Patient enquiries come through calls, forms and messages across several branches, and front-desk staff handle them differently, with follow-up depending on who was on duty.
How it applies:: Enquiries are captured into one record, summarised with the service and branch identified, and follow-up drafts are prepared for staff to review and send, with anything clinical or sensitive escalated to a person immediately.
Likely benefit:: Every branch answers to the same standard and no enquiry is lost between shifts, while clinical judgement stays entirely with your people.

Building materials and hardware distribution

The situation:: Dealer and site enquiries arrive through WhatsApp all day, order details live in chat threads, and the CRM is updated in the evening if at all.
How it applies:: Conversations are summarised into structured records, repeat order patterns are flagged for the territory manager, and reminder drafts for quiet dealers are prepared for a human to approve and send.
Likely benefit:: The pipeline reflects what is actually happening in the field, and dealers who have stopped ordering are noticed within the month rather than at the quarterly review.

Professional services and design studios

The situation:: Every proposal is written from scratch, senior time goes into assembling scope and case examples, and enquiries wait for the one person who writes well.
How it applies:: A drafting workflow assembles a first proposal from your approved scope language, past project examples and the enquiry details, which the consultant then edits and prices before it goes out.
Likely benefit:: Proposals reach the client sooner and read consistently, while pricing and professional judgement remain with the consultant who owns the relationship.

Education and training institutes

The situation:: Admission enquiries surge in season across forms, calls and social channels, and the counselling team cannot reach everyone while interest is still warm.
How it applies:: Enquiries are captured, categorised by course and readiness, and given a prepared first response for the counsellor to check, with genuinely interested applicants pushed to the top of the call list.
Likely benefit:: Counsellors spend the season talking to applicants who are ready to decide, instead of sorting a list that keeps growing faster than they can work through it.

How it is measured

Baseline: Before anything is built we record the present state: how long each chosen task takes today, how often it runs in a week, how complete the records it depends on actually are, and how quickly enquiries currently receive a first human reply. These numbers come from your own systems and the people doing the work, and we agree them in writing, so the later comparison is against something you accept.
Reporting: Runs, exceptions and correction rates are reviewed weekly through the pilot, then monthly. Time and conversion measures are reported monthly against the baseline.
Measurement period: Time savings appear first, once a workflow is running on live work. Conversion, forecast reliability and productivity measures need several of your own sales cycles before the comparison means much.

The metrics that matter here

Data completeness on the specific fields each workflow depends on, before and after.
Lead response speed: time from an enquiry arriving to the first human reply.
Lifecycle conversion at the stages the automation touches, stage by stage.
Automation adoption: how many runs are used as designed rather than quietly bypassed.
Exception rate and human correction rate for each workflow.
Forecast reliability, once records update as work happens rather than at month end.
Reporting time: hours spent each month assembling the numbers by hand.
Revenue productivity: pipeline and closed business per salesperson.

Questions buyers ask

Before you enquire, the answers you will want.

Which workflows give the biggest gain in time and conversion?

In most Indian SMBs the two heaviest are enquiry handling and follow-up drafting. Reading an enquiry, summarising it and getting it to the right person is high volume, highly repetitive and low risk to automate. Drafting the first version of a reply or proposal saves senior time. Reporting comes third. We do not assume this for your business, though. The scoring exercise measures your own hours before anything is recommended.

How long before an AI workflow is running live?

It depends on how many workflows are in scope, how clean your records are, and how many systems must be connected. An assessment is short. A build takes longer when data has to be corrected first, which is common and worth doing. We give you a scoped plan with stage gates after the assessment rather than a number now, because a timeline quoted before we have seen your data would be a guess.

What do you need from us before building?

Access to your CRM and marketing tools, time with the people who currently do the work, examples of real enquiries and campaigns, and one person who can approve scope and data decisions. During testing we need historical cases where you know the correct answer, and reviewers willing to mark output honestly. Without honest reviewers the testing stage produces agreement rather than accuracy.

How will we know the AI is actually saving time?

Against the baseline recorded before the build. We measure data completeness, response speed, conversion at the stages touched, adoption, exception and correction rates, reporting time and revenue productivity. Time and adoption measures move first. Conversion and forecast measures need several sales cycles. We report what we control separately from what depends on your market and your team.

Is our customer data safe if AI is involved?

Data handling is decided before anything is built. We agree what may leave your systems, what must stay inside them, which tools are approved and who may use them. That becomes a written rule for your team, in plain language. Where a workflow can be built without customer data ever leaving your environment, we prefer that design, and we tell you when a chosen tool does not allow it.

Will this replace people in our team?

The workflows we build take over reading, sorting, retyping and first drafts. They do not take over judgement, relationships, negotiation or handling an upset customer. In practice the same team handles more enquiries and spends more of the day with buyers. If your intention is headcount reduction rather than capacity, say so at the start, because it changes what should be built and what should not.

Do we have to buy new software for this?

Often not. Many businesses already pay for a CRM or marketing tool with automation and AI features nobody has switched on. We use what you have wherever it can do the job. Where there is a genuine gap, we name the gap, explain what a new tool would add, and leave the buying decision with you. We do not take commissions from software vendors.

What happens when the AI gets something wrong?

That is planned for, not hoped against. Anything reaching a customer passes a human check until you decide otherwise. Cases the workflow is uncertain about, complaints, unusual requests and large orders stop and go to a named person with context attached. Every run is logged, so a wrong output can be traced to its inputs and the rule corrected rather than the whole workflow abandoned.

4 more questions

Is our data good enough for AI to work on?

Partly, and we will tell you which parts. An AI step reads your records, so poor data produces poor output at speed. The dependency map identifies only the fields a chosen workflow actually needs, which is usually far fewer than the whole database. You correct those, and the rest can be cleaned later or alongside. You do not need a perfect CRM before starting.

How is this different from ordinary workflow automation?

Ordinary automation follows fixed rules: if this field changes, send that alert. It handles structured, predictable steps well. AI automation is for steps that need reading and judgement on messy input, such as understanding a free-text enquiry, summarising a conversation or drafting a reply. Most businesses need both, and we use rules wherever rules are sufficient, because they are cheaper and easier to check.

What is not included in this service?

We do not write your organisation-wide AI policy or risk framework, build custom models, or take over your IT security. We do not run your sales team. Building your website, running paid campaigns and creating content are separate services. If a workflow depends on something outside our scope, we name it in the assessment rather than discovering it halfway through the build.

Can this handle enquiries in Indian languages?

Usually yes for the major Indian languages, and it is worth testing rather than assuming. During the testing stage we run real past enquiries in the languages your customers actually use, including mixed English and regional language messages, and you see the output quality yourself. Where accuracy is not acceptable, that workflow keeps a human reviewer permanently rather than being pushed live.

Talk to us

See what is worth automating before you spend anything on it.

The first conversation is about your week, not a software demonstration. If the honest answer is that your process or your data needs attention before any automation, we will say so and tell you what to do about it.

No obligation and no sales script
A reply from someone who does the work
Your details are never sold or shared

Get a free audit of how you sell, and a scored report of where the work is.

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