Prompt engineering for marketing is the work of turning the way your content team asks an AI model to write into a tested system. Eicra Soft Limited, based in Dhaka, builds that system inside your own AI accounts: brand-voice system prompts, a prompt library for each content type, guardrails against invented facts, and tests your editors score before writers use them.
Stop after any step: nothing commits you beyond the step you are in. We start with the one content workflow where AI output is rewritten most today: blog drafts, product copy, email, social. Its writers, tools, style rules and last 20 pieces are reviewed, the smallest prompt system that removes the rewriting is defined, and it is costed in your own numbers.
You get a written verdict — a prompt system for one workflow, a library for the whole team, an automated workflow, an AI agent, or no build at all. When nothing justifies a build, the diagnostic is where the work ends.
Prompt engineering for marketing designs, tests and maintains the instructions a large language model (LLM) follows to write for your brand. It covers the system prompt that holds voice and policy, a template per content type, the worked examples the model imitates, guardrails that check facts and tone, a test set scoring each template, and the documentation you keep.
The standing instruction every template inherits: reader, tone, banned claims, formatting, sources allowed, and what to do when a fact is missing. It is written from your style guide and your best past work. Every writer starts from the same rules, not their own habits.
One template per content type — blog, landing page, email, social, product copy — with inputs, rules and examples written in, versioned and shared. Writers pick the template for the job and fill in the brief. The output then stays the same across the whole team.
Your best past pieces, cleaned and annotated, so the model imitates what already worked for your readers instead of an average of the internet. Each example is marked with what makes it work. The model copies what your readers like, not just the words.
A second pass that checks names, numbers, quotes and claims against the source, flags tone breaks, and marks anything unverifiable for the editor. Nothing it flags goes live until a person checks it. That is how made-up facts stay out of what you publish.
Every template run on 20 briefs, scored by your editors on a fixed rubric, and iterated until the score holds; the test set stays with you. The same 20 briefs are used for every version. Each change is judged by its score, not by opinion.
A usage guide, a one-hour training session for writers and editors, and optional monthly upkeep as models and tools change, with a named engineer. Everything is handed over in your own workspace. Fixes are included for 30 days after the production handover.
A custom AI prompt library for business is one place where every prompt your team uses lives, versioned and tested. It holds a template per content type, the brand rules and examples baked in, and the test set that proved it. It makes ten writers produce one voice, kept current under Managed Ops as models change.
Which AI platform is best for prompt engineering? None is best for every task. The five AI assistants most often compared are ChatGPT, Claude, Gemini, Microsoft Copilot and Perplexity. Each template in the library is scored on your own test set in the assistants your team already licenses, and kept on the one that scores highest.
One template each for blog, landing page, email, social and product copy, with the brand rules already inside. New content types are added later as a priced change request. Each one gets its own test set, so the library grows without losing quality.
The brief fields a writer fills — audience, offer, keyword, length — so the prompt never depends on memory. The brief carries the facts, so the model works from what the writer gave it. It does not have to guess, and it makes fewer things up.
Every edit numbered and dated, with the test score before and after, so a bad change can be rolled back. The log sits in your workspace next to the templates. Anyone on the team can see what changed, when and why.
A short guide per template: when to use it, what to check, and what the editor signs off before publishing. Writers and editors also get a one-hour training session at handover. The guide is taught once, not left to sit unread.
We build each production prompt in layers and score every version on the same 20 briefs before handover. The illustrative sample below shows how a score is tracked from one version to the next; it is not a client result.
Prompt system · blog draft template
What this layer holds · result in the pilot
System prompt · brand voice
Template inputs
Brief · filled by the writer
{audience}{offer}{keyword}{length}One template per content type
Few-shot examples
Guardrails
A second pass before the editor, on every output.
Editor sign-off · a person signs
Click a layer, or a number below, to switch the result
When a model changes, Managed Ops re-tests every template on the same briefs and updates the guardrails.
Illustrative example. Yellow marks the step where a person signs; every item traces to a template version, a test run and an editor’s score.
Prompt engineering for marketing teams runs in five steps. A free scoping call comes first, then a five-day diagnostic that reviews one workflow and quotes the pilot. A one-to-two-week pilot builds, tests and hands over its prompt system; production adds the other workflows one at a time, then Managed Ops. Each step closes with a written result.
| Step | Time | Price | You receive |
|---|---|---|---|
| Scope | 30 minutes | Free | A call on one content workflow and a diagnostic quote |
| Diagnose | 5 days | $99, credited to the pilot | Failure patterns named and the pilot priced in writing |
| Pilot | 1–2 weeks | $399 fixed | One workflow’s prompt system, tested on 20 briefs and handed over |
| Production | 3–4 weeks each | $800 per further workflow | Remaining content types, each with its own test set and sign-off |
| Managed Ops | Monthly, optional | $499 a month, up to five workflows | Re-tests when models change, new templates and a monthly review |
A named team: a prompt engineer who writes and tests the templates, an editor who scores output against your rubric, and a project manager who runs the weekly review. You know each of them by name. The weekly review is where scores, changes and open questions are agreed with your team.
Marketing prompt work takes two forms: one or two writers rewriting AI drafts need a prompt system, and a team of ten with five content types needs a shared library. Content that moves through several tools without a person is a workflow automation; software that publishes, replies or decides on its own is an AI agent.
| Your situation | What fits |
|---|---|
| One or two writers rewriting AI drafts | A prompt system for one workflow |
| A team of ten writers with five content types | A shared prompt library |
| Content that moves through several tools without a person | Workflow automation |
| Software that publishes, replies or decides on its own | An AI agent |
1. What does the team produce with AI?
2. How many people write with AI?
3. Are brand and style rules written down?
4. Must the content move through several tools automatically?
5. Should the AI publish, reply or decide on its own?
Answer the five questions and the checker suggests the smallest fit for your team. That may be a prompt system for one workflow or a shared library for a larger team. It may also be a workflow automation, when content moves between tools, or an AI agent, when software acts on its own.
One content type, one or two writers: a system prompt, templates, examples and guardrails, scored on 20 of your own briefs and handed over in one to two weeks. The pilot price is fixed. Work on other content types is quoted only after you have seen the pilot scores.
An estimate from five answers; the diagnostic makes it final.
How the checker decides
An AI prompt engineering company is judged on templates still in daily use a year later, not on a demo. We deliver custom AI prompt development as tested, versioned systems, priced in writing, in your AI accounts, with editor sign-off on real output. Personal data in briefs is handled under Article 28 of the General Data Protection Regulation (GDPR).
Article 28(3)(a) GDPR says the processor “processes the personal data only on documented instructions from the controller”; the brief you sign is that instruction for any personal data it contains.
This is where prompt work tends to break down. Prompts are copied from a course or a colleague and never tested on your own content. Each writer ends up with a different voice, and the work starts again whenever a model changes.
A staged process replaces guesswork with evidence. A named team builds one workflow first and scores it on 20 of your own briefs. You then get the prompts, tests and notes in your own accounts, and only after that is anything else quoted.
Outsourcing AI prompt engineering to an offshore team is safe when accounts, data and ownership are settled in writing before the first draft. As a Bangladesh-based company, we work inside your AI accounts and workspace, assign prompts and tests to you, sign a data processing agreement before personal data flows, and hand back access at handover.
Every commitment below is in place before any brand document, sample or login is shared. That means a mutual NDA, a data processing agreement and the transfer terms your country requires. Access is kept to the least needed, on your own accounts. Certifications are listed only when they are held.
Everything built for you stays yours. Templates, test sets and notes are made in your accounts and assigned to you in the contract. They are never reused for another client. Model settings and personal data are agreed before any sample is uploaded, and rework is free when a template fails its agreed score.
Before you pay for production, you get evidence instead of promises. That means measured commitments, a pilot scored by your own editors on your own briefs before production is quoted, and a free 30-minute call on your content workflow. Named client results are published here only with each client’s permission.
A free scoping call on the content workflow where AI drafts are rewritten most; if the tools and style rules are known, you leave with a quote.
Every template is run on 20 of your briefs and scored by your editors on a fixed rubric before it is handed over.
Of fixes after handover, with the library, tests and usage guide in your workspace and training for your writers and editors.
Marketing teams ask nine questions before the first brief. They cover what each step costs, how long it takes, what a real prompt looks like, how prompts are tested, who owns them, which AI tools are supported, fixing prompts you already use, how customer data is protected, and what we do not do.
More answersA five-day diagnostic costs $99, credited to the pilot. The pilot for one workflow is $399 fixed, each further workflow is $800, and Managed Ops is an optional $499 a month for up to five workflows. A single prompt carries no separate fee; your AI provider bills its own usage.
A working prompt system for one content workflow is in your writers’ hands in two to three weeks. First comes a five-day diagnostic that reviews the workflow, the style rules and samples of past content, then a one-to-two-week pilot that builds, tests and hands over the templates. Further workflows take three to four weeks each, one at a time.
A brand-voice blog template is one example. A generic prompt is a one-line request typed from memory, so every writer gets a different result. The template is a specification: the reader, what the brand may and may not say, the structure, the sources allowed, the examples to imitate and the checks to pass. It is tested on real samples.
Every template is run on 20 of your briefs and scored by your editors on a fixed rubric until the score holds. Invented facts are reduced, not ruled out: the prompt limits the model to sources you supply and asks it to flag missing facts. A guardrail step checks names, numbers and quotes, and AI content editing catches the rest.
You do. Prompts, examples, test sets and documentation live in your workspace and your model accounts from the first draft; ownership is written into the contract. Your brand documents and customer data stay in your systems. Our offshore team works through least-privilege access, signs a data processing agreement before personal data is used, and is removed at handover.
We build for the AI assistants your team already licenses, most often ChatGPT, Claude, Gemini, Microsoft Copilot or Perplexity. Each template is scored on your own test set in those tools and kept on the one that scores highest, inside your own accounts.
Yes. The five-day diagnostic reads the prompts your writers already use, records where the output fails, and defines the smallest prompt system that fixes it. The review covers twenty past pieces, your style guide and your current prompts. The pilot is quoted only after that written result.
Work starts under a mutual NDA and a data processing agreement with processor terms under Article 28(3) of the GDPR, and international transfers use standard contractual clauses. We work with least-privilege roles in your accounts, and customer names and identifiers are removed from briefs before they enter a prompt or test set unless your DPA allows them.
This service covers prompts for marketing and content teams only. Prompt systems for support, operations or product teams are covered by Prompt Strategy, and brand voice rules themselves are defined in Brand Personality Design. We do not fine-tune models or build the software that calls your prompts.
Book the free 30-minute call, or go straight to the five-day prompt diagnostic.
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