AI and workflow automation that takes repetitive tasks off your team
Most of what gets asked for as AI is a process and integration problem wearing a new label. We are happy to build the model-driven part, but only after the boring part that actually saves the hours.
AI and workflow automation agency in Bangladesh
Codebumble builds AI automation and workflow automation for businesses in Bangladesh and worldwide. We connect the tools you already use with n8n or custom code, and add AI where it genuinely helps: reading documents, sorting requests, summarising records and answering questions over your own data.
Our CTO, an AI automation specialist, leads this work, and the approach is practical. We measure the manual process first, automate with plain rules wherever possible, and use a model only where judgement is needed, with a person reviewing anything the system is unsure about. That keeps running costs low and results predictable.
The problem
The pilot impressed everyone and changed nothing
The common pattern is a proof of concept that works beautifully on a curated sample, then meets production and quietly stops being used. Usually because it was never connected to the systems where the work lives, nobody owned the failure cases, and the output still needed a human to check every row. The automation that survives is the kind that is wired into the process, monitored, and allowed to hand back to a person when it is unsure.
What our ai automation service includes
Workflow orchestration
Multi-step automations in n8n or code, connecting the tools you already pay for so that a form submission, an invoice or a support ticket moves without anyone copying fields between tabs.
LLM features inside your product
Retrieval, summarisation, extraction and classification built with Mastra and the Anthropic or OpenAI APIs, with the prompts, evaluations and fallbacks in version control rather than in someone's browser tab.
Document and data processing
Turning PDFs, emails and spreadsheets into structured records your systems can use, with a confidence threshold that routes the uncertain cases to a person instead of guessing.
Internal assistants over your own data
A question-answering layer over your documentation, tickets or records, scoped to what the asker is allowed to see and honest about when it does not know.
Monitoring and cost control
Token spend, latency and failure rates on a dashboard, with alerts, because an automation nobody is watching is an outage nobody notices.
Our approach
How we run ai automation engagements
We measure the manual process first
How many times a week, how long each time, and what it costs when it goes wrong. If those numbers do not justify the build, we tell you before we quote rather than after.
Deterministic first, model second
Anything a rule can do reliably, a rule does. The model is used where judgement is genuinely required, which keeps cost, latency and the surface area for weird failures all much smaller.
A human in the loop where it matters
Confidence thresholds, review queues and a clear escalation path. Full autonomy is a decision you make later, on evidence, not an assumption we build in from the start.
Tech stack
What we reach for first, and what we will argue you out of.
Frequently asked questions about ai automation
Tell us what is not working
A thirty-minute call, no deck and no obligation. You will leave with a straight answer about whether this is worth building - including if that answer is no.