AI workflow automation with evaluation and human control
AI Workflow Automation starts with one operating workflow, one accountable owner and one agreed way to judge the result. The goal is a workflow that handles the routine cases correctly on representative test cases, routes uncertain or high-impact cases to a person, and can be monitored and changed after handover. It is not a promise that every process can or should be automated.
Submit a project View the chatbot recordReviewed by David Nguyen (CEO) · Updated 6 Oct 2026
What is in scope
The service covers bounded workflows where software using AI models does part of the work, such as:
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Conversational workflows
Chatbots on messaging channels that answer, qualify or route requests and write the outcome into a CRM or ticketing system.
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Content and moderation workflows
Screening user-submitted content and flagging items into a review queue for an administrator to decide.
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Document workflows
Reading, classifying and extracting data from documents, with a check step before the data is used.
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Knowledge assistants
Answering staff or customer questions from an approved set of sources, with the source shown and gaps handed to a person; the AI knowledge assistant page walks through that workflow.
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System-to-system automation
Keeping records in step between tools such as a CRM, a helpdesk and an ERP, with retries and reconciliation when a sync is uncertain; help desk and CRM operations sets out that workflow for partner support, sales and project follow-up on one account record.
Netbase has delivered AI work of these kinds for clients, including retrieval-based knowledge assistants, document AI and MLOps pipelines, as well as the anonymised records linked below. The client work is not named.
Good fit and poor fit
Automation is worth scoping when the workflow can be described, measured and owned. Compare the two columns before you write a brief; if most of your answers sit on the right, start with a mapping milestone instead.
Good fit
- The workflow repeats often enough that consistent handling matters
- A named process owner can describe the current steps, the exceptions and what a good result looks like
- The data involved is available and you have the right to use it for this purpose
- Someone will own the evaluation cases and the threshold for going live
- One operating workflow needs building end to end
Another route fits better
- The rights to the data, or consent from the people it concerns, are unclear
- Nobody can say what a correct result is, so the automation cannot be tested
- The request is to remove human review from a decision that needs it for legal, safety or customer reasons
- The expectation is a promised saving or accuracy figure before the workflow has been measured
- Several workflows will need the same governed data, retrieval index and evaluation store; Data and AI Platform Engineering builds that shared foundation instead
Before a custom build, compare a process change, existing automation software, an internal build and other AI suppliers against the same workflow. Ask each who owns the data and evaluation cases, how exceptions reach a person, and how integrations, monitoring and running costs will be controlled. Consider a Netbase pilot when the workflow, data permissions and evaluation owner are defined. If the decision to automate is still open, start with an AI Workflow Blueprint.
What we will ask for at the start
For a first review, describe the workflow in plain steps, roughly how often it runs, the systems it touches, a few real examples of easy and difficult cases with private details removed, and who owns the result today. Say where you think a person must stay in the loop. If data rights or consent are uncertain, mention it early: it is often the deciding factor in the design.
Deliverables
- a workflow map: inputs, systems, decisions, exceptions, sensitive data and the person responsible for each result;
- a list of what the automation must not do, agreed with the process owner;
- an evaluation set of representative cases, including difficult and edge cases, with the acceptance threshold;
- the working automation, its integrations and the review or escalation screens people will use;
- access controls, logging and monitoring that let you investigate a result without copying private data into analytics;
- a runbook covering fallback, how to pause the automation, how to change prompts or rules, and cost limits;
- a handover pack with documentation, access transfer and known limitations.
How the work is done
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Map the workflow
Document the current process, the data it touches, its exceptions and the point where a person decides. Agree what is out of bounds.
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Design and evaluate
Choose the model and integration approach for this workflow, build the evaluation set and test options against it before committing.
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Build with controls
Implement the workflow with permissions, logging, escalation paths and cost limits, then test on representative cases.
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Pilot and hand over
Run a controlled pilot, compare results with the agreed threshold, then document, train and transfer ownership.
The approach is model-agnostic. Netbase works with commercial and open-source AI models and chooses per project based on the task, data location, cost and your constraints. Those options include language models from OpenAI, Anthropic (Claude), Google (Gemini) and Meta (Llama), among other commercial and open-weight models.
Milestones and acceptance
A typical plan uses four milestones: workflow mapping, evaluation design, build and pilot, and handover. Each one names its deliverables, the inputs it needs from you, and how it will be accepted.
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Model output is not acceptance evidence on its own
Acceptance is measured against the evaluation set and threshold agreed with the process owner.
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Exceptions are tested, not assumed
The pilot includes the cases where the automation should hand over, and checks that it does.
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Go-live is a decision
The process owner records whether the pilot result is good enough, needs changes or should stop.
Scope changes, such as adding a new channel or a new type of document, are handled as change requests with their own evaluation cases. See project delivery for how milestones and changes are governed.
Risks we plan for
Limited by grounding in approved sources, confidence rules and a review step where the impact is high.
Data boundaries, access control and retention are defined before build; private data is kept out of logs and diagnostics that do not need it.
Integrations use retries and reconciliation so an uncertain sync is visible rather than lost.
Usage limits and monitoring are part of the deliverable.
The evaluation set is kept so a model or prompt change can be re-tested before release.
Human control is part of the design
Every workflow in this service has a defined point where a person reviews, approves or overrides the automation, placed wherever confidence, policy or impact requires it. The people who will do that review are involved in designing the screens they use. The AI workflow automation guide connects the decision from first candidate through delivery: use its spokes to assess candidate use cases, choose between an agent and a workflow, keep model selection replaceable, define evaluation and testing, and decide where human review belongs. Before sign-off, work through the AI workflow governance questions on purpose, data, decision rights and exit. On the delivery side, Netbase engineers use AI tools to speed up coding, testing and documentation, and a named engineer reviews and owns every change that ships.
Frequently asked questions
The one that fits the workflow, chosen and tested during evaluation design. You can require specific providers, or on-premise or open-source models, where data rules demand it.
We agree a threshold on your evaluation set and report the pilot result against it. We do not promise a figure before the workflow has been measured.
No. The design reduces routine handling and keeps a person in control of the decisions that need one.
Yes. When most of the work is product building, the project is planned under Custom Product Engineering with the automation as a milestone.
You can operate the workflow yourselves using the runbook, or agree a separate scope for monitoring, evaluation re-runs and changes. Handover does not depend on buying that support.
Yes. A mapping and evaluation milestone followed by a limited pilot is often the right first step, because it shows whether the workflow is ready before a larger build is committed.
Related work
Two anonymised portfolio records show delivered scope for this service: a WhatsApp AI chatbot with CRM integration and an online classifieds platform with AI-assisted moderation. They do not establish a general accuracy rate, saving or outcome guarantee. See all records on the Work page and the evidence rules on the methodology page.
Netbase delivered a bilingual classifieds marketplace, in English and Nepali, with paid ads, search, messaging, payments and an AI filter that flags offensive content into an administrator review queue. The client is not named, and no traffic, accuracy or commercial result is claimed.
Keep Reading
The client, sector, region and CRM product are not named, and no conversation volume or business result is claimed.
Keep ReadingHave a workflow in mind?
Submit a project brief describing the process, the data and where a person needs to stay in control. OutsourcingVN is operated by Netbase JSC and is Netbase's own outsourcing-services platform.