Automation & Agent Services

AI Automation Agency for Workflows, Integrations and AI Agents

AI Automation and Agent Services, Priced Before We Build

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

2 weeksDiagnostic, $1,500 fixed — $750 credited to your pilot

3–4 weeksPilot on 20–50 of your real cases, $4,000 fixed

30 daysMonitoring after launch, fixes free of charge

YoursCode, prompts and admin access transfer at handover

Built in your own n8n, Make or Zapier accounts · Confidentiality and data processing agreements signed before any data moves · Overlap hours agreed at kickoff

Which AI automation services do we offer?

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.

AI Workflow Automation

A repeat process moved into an automated pipeline with error handling, retries, alerts and a written runbook.

AI Integrations

AI wired into the sales, finance and help-desk systems, spreadsheets and email your team already pays for — not another tool to learn.

Human-in-the-Loop AI Review

Review checkpoints, confidence thresholds, exception queues and approval logs, so a wrong output never reaches a customer unseen.

AI Agent Development

Agents that plan their own steps and call tools, inside permissions, spend caps and full action logging.

Dedicated AI Development Team

Named engineers working continuously on your AI and software backlog, in your own tools and repositories.

AI Workflow Diagnostic

Two weeks at a fixed price. Processes scored, payback estimated on your numbers, and a fixed pilot price you can take to any vendor.

See pricing

How do AI automation services work inside one process?

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.

Where the workflow ends, the agent starts and a person decides

01TriggerAn email, a form or a system event starts each run of the process the diagnostic choseAI Workflow Diagnostic600 emails a week
02WorkflowFixed steps read, classify and route every item, with retries and alertsAI Workflow Automation
03AI agentTakes only the cases whose path varies; plans its steps and calls tools inside permissions and spend capsAI Agent Development90 need judgement
04Named personBelow the confidence threshold, the case waits in an exception queue until a person approves itHuman-in-the-Loop AI Review
05Action in your systemsThe result is written into your sales, finance or help-desk tools, and every action is loggedAI Integrations

Known path: items that need no judgement skip steps 03 and 04 and go straight to step 05. 510 go straight through

Every run is retried on failure and logged; your team runs it from the runbook, or the Dedicated AI Development Team extends it.

Illustrative counts: an inbox of 600 emails a week, of which 90 need judgement, as in the rules further down this page. Yellow marks the only step where a person, not software, makes the call.

Which AI automation service do I need?

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.

Find your situation on the left, then read across
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

Two-minute fit check: which service suits your process?

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.

Answer each question, then count which service appears most
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

Six possible outcomes: Workflow Automation · AI Integrations · Human-in-the-Loop · AI Agent Development · Dedicated Development Team · AI Workflow Diagnostic. If two services tie, take the diagnostic: two weeks at a fixed price settle the question with your own numbers instead of a guess.

When to use a workflow, an AI agent, or multi-agent orchestration

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.

Rule 01: use a workflow when the steps never change

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.

Rule 02: use an agent when the path varies

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.

Rule 03: use multi-agent orchestration last

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.

The rule above all three: whatever we build, there is a confidence threshold, an exception queue and an alert to a named person wherever a mistake costs money, legal exposure or customer trust. Automation without that is a demo, not a system. The Open Worldwide Application Security Project (OWASP) lists excessive agency among the top risks for large language model (LLM) applications, and states that it “enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM”, which is why every agent runs inside permissions and spend caps. Illustrative example: a support inbox of 600 emails a week is triaged by a workflow, and only the 90 that need judgement reach an agent and a person.

Why choose us for AI automation?

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

What a typical low-cost build leaves out, and what we include
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

AI automation FAQs: cost, timing and ownership

Cost leads the list, because buyers ask it first.

How much does an automation or agent project cost?

Each 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.

What do AI automation services include?

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.

What is the difference between workflow automation and an AI agent?

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.

How long does a build take?

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.

Who owns the build, and does it run in our accounts?

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.

Book the Diagnostic