AI marketing services are worth buying where the work is repetitive and checkable, and worth refusing where accuracy and brand judgment decide the outcome. Gatilab builds the workflows, the review step, and the measurement, and is direct about the tasks where handing the job to a model costs you more than it saves.
Delivery is remote, with calls in IST. You keep the code, the accounts, and every login in your own name.
Three failures account for most of what we get asked to rescue. All 3 are scoping problems wearing technical costumes.
The first result of adding AI to content is usually more output. The second is a reader who can tell, and a brand that reads like every competitor. Volume is the easy part and almost never the constraint that mattered.
A workflow without a named reviewer publishes its own mistakes. The question is not whether the model is wrong sometimes, it is who is accountable when it is, and whether that person has time budgeted for the job.
Most of what a marketing team repeats is not writing. It is reformatting, tagging, briefing, summarising, and moving things between systems, which is exactly where automation pays and where almost nobody points it.
Six pieces of work, each scoped on its own. Most engagements start with one and add the others once the first has paid for itself.
Mapping what your team actually repeats, then automating the parts that are checkable and leaving the parts that are not. The audit usually finds more value in operations than in drafting.
Structured research, SERP and competitor synthesis, and briefs that give a writer something to argue with rather than a paragraph to expand.
Drafting assistance where it helps, with a named reviewer, a factual check, and a voice standard that a model cannot quietly drift away from.
Metadata, internal linking, repurposing, and publishing pipelines. The unglamorous work where automation reliably pays for itself.
Tracking whether the AI-assisted work performs differently from the rest, because the only argument that settles this is your own data.
Where AI is used, how it is disclosed, what never goes into a prompt, and which claims always need a human source. Written down before it becomes a problem.
Prices are in rupees and exclude tax. The scoping sprint is a fixed fee and comes off the build price if you go ahead.
For teams who need a real number and a written plan before committing to a build.
The fee comes off the build price if you go ahead with us.
For building the workflow, the review gates, and the measurement around them.
If the work does not warrant a build, we quote the smaller fix instead.
For teams who want the workflow run and improved rather than handed over.
Month to month. A contract you cannot leave is a hostage situation.
Development and consulting are delivered remotely, so location changes meeting times and payment method rather than price. Hosting is never resold.
Overruns come from work nobody scoped. Every stage produces an artifact you check before the next one starts.
We read what already exists before quoting. Most of the risk in this kind of work is invisible from the outside, so pricing it without looking is a guess dressed as a number.
You leave with: a written scope with the assumptions and risks named
Work happens on an environment your team can reach, reviewed weekly. You see it grow rather than getting a reveal at the end and a week to react.
You leave with: a staging URL you can click through as it grows
Error handling, logging, and rollback are built with the feature, not after it. The question is never whether something fails, it is whether you find out before your customers do.
You leave with: monitoring, alerting, and a rollback path that has been tested
Documentation your next hire can read, every account in your name, and a dependency list short enough to maintain.
You leave with: documentation, credentials, and no lock-in
Vague quotes do the most damage in exactly this kind of work. Half of this list is reasons we say no.
Scope, cost, ownership, and the cases where we are the wrong choice.
No. Drafting assistance is a part of the workflow, never the whole of it, and every piece has a named human reviewer accountable for accuracy and voice. If what you want is volume with no review, that is cheaply available elsewhere and we are the wrong choice for it.
Search engines reward useful, accurate content and penalise thin content regardless of how it was produced. The risk is not the tool, it is skipping the review and the information gain. Our workflows put those back in, which is most of the cost.
Research synthesis, briefing, metadata, internal linking, repurposing across formats, and moving work between systems. In practice the operations layer pays back faster than drafting does, and it carries far less brand risk.
This page is about using AI inside your marketing work. Getting cited by ChatGPT and other answer engines is a different job, covered on the AI Search Optimization page linked below.
Send the current situation, what you have already tried, and the outcome you need. The more specific you are, the more specific the reply.
Prefer to talk first? Message us on WhatsApp. Budgets under ₹27,000 are better served elsewhere and we will say so rather than take the project.
