Prompt engineering services turn ad hoc AI instructions into a documented system of goals, context, constraints and output rules. As a Dhaka-based company, we build that prompt strategy for international teams, test it against real inputs, version it and hand over the prompt library, test sets and documentation in accounts you own.
Each prompt is tested on real inputs from your work against written pass and fail criteria, so a change can be judged instead of argued about. You start with a four-day audit, then a pilot you judge on your own test inputs before the library is built.
Every prompt is judged on a written test set. Send one prompt you already use first: the review takes 30 minutes and costs nothing.
You get a written note on what is missing from it and what fixing it would cost, whether or not you hire us.
Our prompt engineering services deliver six things every time. You get a use case and goal map, a prompt architecture, custom prompts, testing against written criteria, a versioned prompt library, and documentation with handover. Optimisation continues against the same criteria, not by feel. You receive a working prompt system, not a list of tips.
A written map of the tasks you want AI to do, what a good output looks like for each, and which ones are worth a prompt at all.
How each prompt is put together: system instruction, context you supply, task, constraints and the output format the next step expects.
The prompts themselves, written for your data and your wording, with the reusable parts split out as templates your team can fill in.
A set of real inputs with the output you would accept for each, plus written pass and fail criteria, so a prompt change can be judged instead of argued about.
Every prompt stored in one place with a version number, what changed, who changed it and which test run it last passed.
Written notes on why each prompt is built the way it is, how to change it safely, and a walkthrough for the people who will own it.
Prompt engineering services cover content, support, operations and sales: the repeated tasks your team already hands to an AI model. Some teams now call this wider work context engineering, but it still rests on written, tested instructions. Each use case gets its own prompts, rules and test set.
Briefs, drafts, product descriptions and repurposing, each with a fixed structure and a house voice the prompt enforces. For editing the output afterwards, see AI content editing.
Ticket summaries, draft replies, tone rules and escalation wording, with refusal cases written into the prompt.
Document extraction, classification, routing rules and summaries, returned as structured output another system can read.
Research briefs, call summaries and follow-up drafts, built from fields your customer relationship management (CRM) system already holds.
Prompt engineering services test each prompt on 20 real inputs before it ships. Every output is scored against written pass and fail criteria, each failure is traced to the prompt, the context or a model limit and fixed, and the new version is logged in the library. The sample shows one support prompt going through that test.
Test run · support reply prompt, 20 real inputs
Version 1.3 18 of 20 pass
Passed the written criteria Failed, then traced and fixed
Version 1.4 20 of 20 pass
Test method · five steps
When a model version you use changes, the Managed Prompt Desk re-runs the same test set and reports which prompts behave differently.
Illustrative example. Yellow marks where a person decides; every prompt version is logged with what changed, who changed it and the test run it last passed.
Our prompt engineering services run in four fixed-price steps, each with a published scope cap. They are a four-day audit of up to 10 existing prompts, a two-to-three-week pilot on one workflow, the prompt library setup built alongside it, and an optional Managed Prompt Desk. Each step ends with a written result you can stop on.
Fixed prices on the cards at the top, live in about four weeks.
Prompt strategy suits any team whose AI output changes from one run to the next. Three buyer types hire us most, and each gets the same system tuned to a different job. Prompt engineering is still a thing: every AI feature still runs on written instructions, and those need testing whenever a model changes.
Every writer prompts differently, so the output does too. For content and copy alone, prompt engineering for marketing is the dedicated service; here, marketing prompts sit inside one library shared with your other teams.
A reply that varies by run is a support risk. You get prompts with fixed output structures, defined refusal and escalation rules, and a test set you can re-run before anything ships.
Prompts live in your codebase and break on model updates. You get versioned prompt files, a documented test set, and a re-test when a model version you use changes.
Prompt engineering services are worth it when the output matters: they design, test and document the instructions your business gives an AI model. We work test-first: prices carry scope caps, prompts are judged on an agreed test set, and personal data is covered by the processor terms of Article 28(3) of the General Data Protection Regulation (GDPR).
Outsourcing prompt work is safe when the safeguards are agreed in writing first. The main risks are leaked business logic or sample data, lock-in, and prompts nobody internally understands. As a Bangladesh-based company, we open every engagement with a mutual non-disclosure agreement (NDA), named least-privilege accounts and a data processing agreement (DPA). Reviewed By Eicra.com team
The Open Worldwide Application Security Project (OWASP) lists prompt injection first in its Top 10 for large language model (LLM) applications, and its definition reads: “A Prompt Injection Vulnerability occurs when user prompts alter the LLM’s behavior or output in unintended ways.” The AI Risk Management Framework from the National Institute of Standards and Technology (NIST) sorts this work into four functions: govern, map, measure and manage. Prompts and test data stay inside your own systems, and the documentation lets your team take over.
Before you pay for prompt engineering services, you get evidence instead of promises. You get measured commitments, a pilot on one of your own workflows that ends in a go or no-go report, and a free review of one prompt. Client case studies with numbers are added here as clients give permission to name them.
Every production prompt is run on a test set of 20 real inputs from your work before it ships.
Under the Managed Prompt Desk, your test set is re-run when a model version you use changes.
Rework is free when a prompt fails the agreed test criteria; new scope is priced first as a change request.
Prompt engineering services with us are priced per step, not per hour, and each price is on the price cards at the top. The steps are a four-day prompt audit of up to 10 prompts, a pilot on one workflow, the prompt library setup and an optional monthly Managed Prompt Desk.
A first engagement is live in about four weeks: a four-day audit, then a two-to-three-week pilot with the library setup built alongside it. Once the Managed Prompt Desk is running, prompt updates are delivered inside the monthly allowance of up to 10 updates.
A prompt strategy is the system around your AI instructions rather than the instructions themselves. It covers which tasks are worth prompting, how each prompt is structured and tested, and who maintains it. For example, a support prompt can set a role, give three sample replies and require a fixed reply format, then pass a 20-input test before it ships.
Prompt writing gives you one phrase that worked once; a prompt strategy gives your whole team a tested system. You get templates anyone can run, a test set that shows whether a change helped, version history and documentation. The difference shows when the author leaves or the model updates.
Prompt work is not model training or application development. We do not fine-tune or host models, build retrieval-augmented generation (RAG) pipelines or write the software that calls your prompts; that is AI agent development and AI integrations work. We do not sign off legal, medical or financial output, and the audit says so when a use case needs engineering instead.
Start with a four-day prompt audit at a fixed price.
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