Make your business intelligent.
AI agents, workflow automation and consulting that handle real work — answering customers, qualifying leads, and doing the repetitive tasks nobody has time for.
Most conversations about AI stop at the chatbot. Netrau builds further than that: AI agents that read a WhatsApp enquiry, decide what the customer actually needs, and either answer it, capture it as a structured lead, or hand it to a human — without a person watching every message around the clock.
We start from the same question every time: where in this business does AI create measurable value, not where can we insert AI because it is expected. That discipline is what separates a demo that impresses a visitor for thirty seconds from a system a business actually runs its operations on, day after day.
AI agents that do the work, not just the talking
A WhatsApp AI agent for a retailer answers stock and pricing questions instantly, at any hour, and only escalates to a person when the customer wants to negotiate or complain. A document-processing agent reads incoming supplier invoices and populates the accounting system automatically, instead of a bookkeeper retyping every line by hand at the end of the week. A sales agent qualifies an inbound enquiry against a simple set of criteria — budget, timeline, location — before a salesperson spends time on a call that was never going to close.
The pattern across all three is consistent: the agent removes one specific, named bottleneck, and it is connected to the systems the business already uses — CRM, accounting, inventory, payments — so the output of the agent’s work lands where someone can act on it, not in a chat log nobody re-reads.
Don't ask 'How can we use AI?' Ask 'Where can AI create measurable value?'
That reframe changes the whole engagement. Instead of bolting a chatbot onto a website because AI is expected of every modern business, we map the business’s actual bottlenecks first — lost leads, slow replies, manual data entry, reports that take a week to compile — and only recommend AI where it demonstrably removes one of them. Sometimes the honest answer is that a simpler workflow automation, not an AI agent, is the right tool for the job, and we say that too rather than overselling AI where it is not needed.
AI consulting for teams that want to move but don’t know where to start
For businesses that are not ready to build yet, we run a focused discovery process: what takes too long today, what breaks or slows down under volume, what data already exists that AI could put to work without a large new data project. The output is a prioritised list of AI opportunities ranked by effort and payoff, with a recommended starting point — not a slide deck full of buzzwords and no next action.
Once a direction is agreed, we implement it ourselves rather than handing over a strategy document and leaving. That continuity — the same team that scoped the opportunity building and supporting it — is deliberate. AI projects that stall usually stall at the handover between strategy and delivery, so we do not create that handover in the first place.
AI training so the team can run what we build
An AI agent that only the developer who built it understands is a liability, not an asset. Every AI engagement includes handover training for the people who will actually use and adjust the system — how to review what the agent decided, how to correct it when it gets something wrong, and where the boundaries of its judgement sit.
How this looks in practice.
Concept work exploring exactly this kind of problem — labelled honestly, not delivered client results.
Common questions.
Is this just a chatbot with a different name?
No. A chatbot answers questions from a fixed script and stops there. The AI agents we build read context, make a decision — answer, capture as a structured lead, or escalate to a person — and then take an action in another system: updating a CRM record, creating a draft invoice, logging a follow-up task. That connection to the systems a business already runs on is what makes it useful rather than a novelty widget on a website.
We do not have clean data. Can we still start with AI?
Usually yes, but the first project should target the messiest, most repetitive manual process rather than the most ambitious one on the list. Cleaning data as a natural byproduct of automating a real workflow — because the agent needs the data to be usable to do its job — is far more durable than a standalone data-cleanup project that nobody asked for and nothing depends on finishing.
How do you price an AI agent?
It depends mainly on how many systems the agent needs to talk to and how much judgement it needs to exercise before acting. A WhatsApp FAQ agent answering from a fixed knowledge base is a smaller project than an agent that qualifies leads, checks live inventory, and creates an invoice on approval. We scope every engagement after understanding the specific bottleneck it is solving — see our pricing page for honest starting bands by project type.
Where in your business could AI remove a real bottleneck?
Tell us what takes too long, and we’ll tell you honestly whether AI is the right fix.
Start a Project