As a Dhaka-based company, our AI automation services put AI inside a process you already run, for international companies: workflow automation, integrations, human review, agents, a dedicated team and a fixed-price diagnostic. Every build ships with error handling, a human checkpoint where mistakes cost money, and a runbook your team can run after handover.
Start with the $1,500 Diagnostic See the six services
Built in your own n8n, Make or Zapier accounts · Confidentiality and data processing agreements signed before any data moves · Overlap hours agreed at kickoff
Our AI automation services come as six separate engagements, each with its own scope and price. Five of them build or run a system, and the sixth, the diagnostic, decides which system is worth building first. Most clients use two or three of them together, usually starting with one workflow.
A repeat process moved into an automated pipeline with error handling, retries, alerts and a written runbook.
AI wired into the sales, finance and help-desk systems, spreadsheets and email your team already pays for — not another tool to learn.
Review checkpoints, confidence thresholds, exception queues and approval logs, so a wrong output never reaches a customer unseen.
Agents that plan their own steps and call tools, inside permissions, spend caps and full action logging.
Named engineers working continuously on your AI and software backlog, in your own tools and repositories.
Two weeks at a fixed price. Processes scored, payback estimated on your numbers, and a fixed pilot price you can take to any vendor.
AI automation services work inside one process by splitting it: a workflow runs every step that never changes, and only the cases whose path varies go to an AI agent. Below a confidence threshold, a named person approves before anything reaches your systems. Every run is logged, retried on failure and covered by the runbook.
The AI automation service you need depends on the problem, not the technology. If the steps never change, you need workflow automation. If the path depends on the case, you need an AI agent. If a wrong answer costs money, add a human checkpoint. If nothing is written down yet, start with the diagnostic.
| Your situation | What you need | Typical starting point |
|---|---|---|
| Same steps every time, high volume, rules written down | Workflow Automation | Pilot, 3–4 weeks |
| AI tools bought but not connected to your systems | AI Integrations | Pilot, 3–4 weeks |
| Output goes to a customer, a regulator or a payment | Human-in-the-Loop | Free calibration sample, then a pilot batch |
| Steps depend on what the AI finds in each case | AI Agent Development | Pilot, 3–4 weeks |
| A permanent backlog, not one project | Dedicated Development Team | Monthly per engineer, 3-month minimum |
| A long list of ideas and no agreed priority | AI Workflow Diagnostic | Fixed price, 2 weeks |
The fit check is five questions about your process. Read across your answer and note the service named; whichever service appears most often is where to start. If the diagnostic appears twice or more, start there instead, because a guess at this stage costs more than two weeks of measurement.
| Question | Option A | Option B | Option C |
|---|---|---|---|
| 1. How predictable are the steps? | Identical every time → Workflow Automation | Depends on the case → AI Agent Development | Nobody has written them down → Diagnostic |
| 2. What happens if the output is wrong? | Someone fixes it quietly → Workflow Automation | A customer or regulator sees it → Human-in-the-Loop | We honestly do not know → Diagnostic |
| 3. How many systems are involved? | One or two → AI Integrations | Three or more, handing work along → AI Agent Development | None yet; it is all manual → Workflow Automation |
| 4. How much work is coming? | One process to fix → Workflow Automation | A permanent backlog → Dedicated Development Team | Unclear, needs scoping → Diagnostic |
| 5. Do you have volumes and a success metric? | Yes, both written down → skip ahead to a pilot | Volumes only → Diagnostic | Neither → Diagnostic |
Three plain rules decide between a workflow, an agent and orchestration. We apply them in this order and tell you when the cheaper option is the right one.
If you can draw the steps on paper and they do not change, a plain workflow wins. It is cheaper, faster, easier to test and far easier to debug at three in the morning.
Adding a model to a process that needs no judgement only adds cost and a new way to fail.
Use an agent when the next step genuinely depends on what the previous step found, and writing every branch by hand would be unmanageable.
An agent buys flexibility and costs control: permissions, spend caps, action logs and much more testing. Worth it when the variation is real, wasteful when it is not.
Orchestration is for work that crosses several systems or teams, where each part has a different owner and the handoffs need recording.
Most companies do not need it in year one. If a single agent with more tools would do the job, we build that instead and say so.
AI automation pays off only when it keeps working after the demo, so every build is judged on errors, exceptions and hours saved. We sign a data processing agreement (DPA) under Article 28(3) of the General Data Protection Regulation (GDPR) and design human checkpoints in line with Article 14 of the EU AI Act. Reviewed By Eicra.com team
| Item | Without | With EICRA |
|---|---|---|
| Price | Hourly, discovered as you go | Fixed, agreed in writing before any work starts |
| Proof it works | A demo on sample data | Tested on 20–50 real cases, with before-and-after numbers |
| When a step fails | It fails silently | Retries, fallback path, alert to a named person |
| Wrong AI output | Reaches the customer | Held at a confidence threshold, routed to an exception queue |
| Ownership | Built in the vendor’s account | Built in yours; credentials and intellectual property (IP) stay with you |
| After launch | Support billed per request | 30 days of monitoring with free fixes |
| If your team takes over | No documentation | Written runbook, export and training at handover |
| Contracts | A freelancer invoice | Company contract with a non-disclosure agreement (NDA), a DPA and data transfer terms where they apply |
Cost leads the list, because buyers ask it first.
More answersEach step has a fixed price agreed in writing before work starts. The two-week diagnostic and the pilot carry the prices shown at the top of this page, with half the diagnostic credited to the pilot; production is quoted after it. Model and tool fees are paid by you directly to their providers.
AI automation services put AI inside work your team already does: workflows that move data between systems, integrations that add AI to your tools, agents that plan steps, and human review where mistakes cost money. The deliverable is a running system with error handling and a runbook, not a demo.
A workflow runs a fixed sequence you defined in advance. An agent plans its own steps, chooses which tools to call and adapts to what it finds, inside permissions and spend caps you set. Use a workflow when the path is known; use an agent when the path depends on the case.
The diagnostic takes two weeks and a pilot three to four weeks; production timing is quoted after the pilot and depends on how many users and systems are involved. Most delays come from access and approvals on the client side, so we list every login and approval we need at kickoff.
You do, and it runs in your own accounts wherever the architecture allows. As a Bangladesh-based team we work through named, least-privilege access that is removed at handover. You receive admin access, an export of the build and a runbook, so your team can run it without us.
Tell us one repeat process. We will tell you whether it is worth automating.
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