AI Agents and Workflow Automation
AI agents and workflow automation that run inside your own systems.
For repeatable workflows with results you can verify.
Starting from
$6,000≈ Rp97,8jt
This is a starting point for the scope most often requested. The final quote follows the scope we agree on together.
USD is the contract currency. The rupiah figure is indicative and follows the rate recorded in the site configuration.
What the work includes
Agents with tool access to your internal systems, not a standalone chatbot
RAG over your documents: indexing, retrieval, and source citation
An evaluation set and regression tests before an agent touches production data
Human-in-the-loop on every step that moves money or reaches a customer
Logging, tracing, and a token cost dashboard per workflow
Handover as code, prompts, and an operational runbook
From scope to handover.
Start with one workflow whose answers can be checked, then evaluate before unattended operation.
One real workflow
Real cases with checkable answers
Tools & sources
Internal system access, retrieval, and citations
Evaluate & review
Regression tests and human checkpoints
Production & handover
Logs, token costs, code, and runbook
Steps that move money or reach customers retain human checkpoints. Model API costs are billed to your account.
What we build
AI agents wired into the systems you already run. In practice that means one service that takes a request, calls tools (your internal database, a vendor API, your ticket system), assembles an answer, and logs every step so the result can be traced back.
Three shapes come up most often:
- Internal copilot. An operations team asks in plain language, the agent pulls from internal systems and answers with citations back to the source record.
- Workflow automation. A document arrives, the agent extracts structured fields, validates them against your business rules, and hands them to the next system.
- Document assistant (RAG). A body of policies, contracts, or product manuals becomes a queryable index where every answer carries its source.
How we work
The first phase is always one workflow, not ten. We pick a process with clear volume and a checkable right answer, build an evaluation set from your real cases, then measure the agent against that set before anything runs unattended.
Once the first workflow is stable in production and the numbers are visible, the next one is much cheaper, because the infrastructure, logging, and evaluation pattern already exist.
What is not included
- We do not train foundation models from scratch.
- We do not claim 100 percent accuracy. Every system we ship has a measured accuracy number against your evaluation set, and an escalation path to a human for the cases outside it.
- Model API costs from the provider (OpenAI, Anthropic, Google, or another) are billed to your own account, not through us.
Cost range
From USD 6,000 for one workflow running in production, including the evaluation set, monitoring, and handover documentation. The exact figure depends on how many systems need integrating and how clean your data is today.
What would you like to improve?
Tell us about the process, the systems you use, and what gets in the way. We can define the scope from there.