ManDoNothing
Enterprise AI implementation
Put AI to work in your existing systems.
ManDoNothing provides enterprise AI implementation and integration services across Asia Pacific. We work alongside your team to choose one business workflow, connect the data and tools it needs, and deliver an AI agent with agreed checks, human oversight and a plan for ongoing improvement.
Discuss your workflow ↗Start with a workflow people can evaluate.
A useful first project has a clear owner, recurring work, representative examples and an outcome the team can check. Your team brings the business judgment; we help turn it into a working system.
- Information is spread across documents and systems, and staff must gather it before making a decision.
- A process involves repeated data entry, routing or follow-up across tools that already work for the business.
- An AI prototype exists, but permissions, exception handling, evaluation and day-to-day ownership still need to be resolved.
What the engagement covers
We agree the deliverables and acceptance decision for each stage before building. The scope can grow after the first workflow has been evaluated.
| Stage | Work and deliverables | Decision to make |
|---|---|---|
| Define the pilot | Workflow map, available data, system access, baseline and acceptance criteria. | Is there enough access and evidence to test this use case? |
| Build and integrate | A scoped workflow, system connections, AI agent behavior and human approval points. | Does it complete the agreed task with the right permissions? |
| Evaluate and release | Results on representative examples, exception checks, operating instructions and a recovery plan. | Do the business owner and system owner accept it for the agreed use? |
| Handover and improve | Named ownership, access and documentation handover, plus agreed monitoring and improvement work. | Which issues should be fixed next, and which need a new scope? |
AI integration around the systems you use
We review the interfaces and access available in your CRM, document tools and operational systems. The integration plan specifies what the agent can read, what it can change, and where a person must approve an action. Existing systems remain the source of business records.
Business Ontology is our way of mapping shared objects, relationships, rules and permissions. MandoForge supports the connections between those systems and agents. These choices follow the workflow and the available access.
When evaluation identifies a model capability gap, reviewed business examples can support model customization and post-training. Training is considered within the agreed data permissions and evaluated before a model update is released.
How do you accept an AI agent?
Agree a baseline and acceptance set before implementation. Include normal work, incomplete information and exceptions. Keep acceptance examples separate from examples used to tune the workflow or model.
- Task quality: check outputs against a business rubric and, where relevant, their source records.
- Action correctness: check permissions, required approvals and the record left in the destination system.
- Failure handling: check what happens when data is missing, a connection fails or a person rejects a proposal.
- Operational fit: measure latency, usage cost and human review effort against the agreed baseline.
What to bring to the first conversation
Describe the work you want to change, the tools involved and the person who owns the outcome. A few sanitized examples of normal and difficult cases help us define a useful first scope. Access requirements and data-sharing arrangements are agreed during scoping.
Questions before we start
What does enterprise AI implementation cost?
The main cost drivers are the workflow scope, number and condition of system connections, data preparation, evaluation requirements and ongoing support. We define the initial scope before quoting. Model usage, hosting and third-party charges should be identified separately so you can assess both delivery and operating costs.
How long does an AI implementation project take?
The schedule depends on access approvals, data readiness, integration work and the time needed for business review. We agree milestones after checking those dependencies. A small pilot and a production rollout have different acceptance requirements, so a demo date alone is not a go-live commitment.
Can AI agents work with our existing CRM and business systems?
We start by checking available APIs, permissions and workflow rules. Where the required access is supported, agents can read context and carry out approved actions through those interfaces. If an interface or permission is missing, we identify that dependency before agreeing the implementation scope.
Do we need an in-house AI or IT team?
You can start without an in-house AI team. A business owner still needs to explain the work, review examples and accept the result. Someone with authority over the relevant systems must approve access; we work with your existing provider when that is how your systems are managed.
What happens to our data and access permissions?
During scoping, we agree the data sources, permitted uses, access roles, model providers and deployment constraints. We define approval points for consequential actions and decide what evidence is retained. Data use for model training requires an agreed scope and permissions.
What happens after the first pilot?
We review the acceptance results together and decide whether to release, revise or stop. The next scope specifies operating ownership, documentation and access handover, support responsibilities and future evaluation. Ownership and licensing of project materials are set out in the agreement.
Start with work worth improving.
Tell us how the work happens today, which systems it involves and what you want to change.
Discuss your workflow ↗