AI solution development

AI systems that do the work.

Practivo designs and builds AI for businesses: automations, reporting, voice agents, and autonomous and multi-agent systems. We connect them to the tools you already use, and a person stays in charge of anything that matters.

Example activity across one business's agents.

What we build

One system or several working together. Scroll through to see each one at work.

  1. Workflow automation

    Moves data between your systems, reads documents, and runs the steps your team currently does by hand.

    A person handles exceptions, like an invoice that doesn't match its order.

  2. Reporting and analytics

    Pulls numbers from every system into one report each morning, and answers questions about them in plain language.

    A person decides what to do about what the numbers show.

  3. Voice agents

    Answers and makes phone calls in a natural voice: bookings, reminders, follow-ups and first-line support.

    Upset callers and anything it can't resolve are transferred to a person.

  4. Customer and staff assistants

    Answers on WhatsApp, web chat and email from your prices, policies and manuals, and shows where each answer came from.

    Complaints, refunds and questions your documents don't cover go to a person.

  5. Autonomous agents

    Takes a goal, such as keeping stock above minimum, and works through the steps itself using your systems.

    Spending above a limit you set, and any action you mark for sign-off, waits for a person.

  6. Multi-agent systems

    Several specialised agents that hand work to each other, for example stock and finance, with every handoff logged.

    A person approves the result before it leaves the business.

Things we've built

Call centre · Voice agent

Rova, a digital employee that answers the phones

Rova is an autonomous agent that works inside a fleet operations platform. Its call centre agent answers customer calls in a natural voice, handles bookings and account queries, logs every call, and transfers the caller to a person when it can't resolve the issue.

Voice agent · autonomous agent · fleet platform integration

Vehicle rental

An operations console for a rental fleet

Bookings, vehicle status and maintenance in one place, with an assistant that drafts the next action for the ops team to approve.

Azure · TypeScript · fleet platform integration

Customer messaging

A WhatsApp platform for customer conversations

Runs on Meta's official WhatsApp Cloud API, so numbers don't get banned. Each business's conversations are kept separate at the database level.

WhatsApp Cloud API · Postgres row-level security

Customer service

Reply drafts your staff approve before sending

Reads incoming chats, drafts an answer in the business's tone, and waits for a person. Nothing is sent automatically.

Python · LLM drafting · review queue

Training

A learning platform that tutors each person

Generates course material and answers learners' questions, styled to match the client's brand.

Next.js · AI tutoring

Our product

Lens

Our model evaluation and monitoring tool. It grades every answer your AI gives against your own test cases before launch, then keeps watching once it's live.

Ask about Lens
Evaluating support assistant · prompt v14 0 of 6 passed

    Example evaluation. Lens runs your real test cases on every model or prompt change.

    Live monitoring · answer accuracy · 30 days Alert below 90%

    Day 23: accuracy fell to 87% after a model update. Lens flagged it, the team rolled back, and it recovered by day 26.

    Example data.
    • Evaluate before launch. Compare models and prompts on your own cases before anything reaches customers.
    • Monitor live use. Track answer quality, speed and cost on real traffic, day by day.
    • Catch drift. Get alerted when an update or a new kind of request makes results worse.
    • Keep people in the loop. Uncertain answers go to a person, and their decisions become new test cases.

    How a project runs

    Each step ends with something concrete, so you always know where the project stands.

    1. A conversation

      You tell us what you want AI to do and show us how the work happens today. We tell you honestly what will work.

      You get An honest view of where AI will help, and where to start

    2. A working prototype

      Built on your real data, calls and documents, and measured against how your team does it today.

      You get A prototype you can try, with results compared to today

    3. Into daily use

      Connected to your systems, with staff logins, an audit log of every action, and an off switch.

      You get A live system your team uses every day

    4. Looking after it

      We monitor it with Lens, report back monthly, and improve it as your team finds new uses.

      You get A monthly report and a system that keeps improving

    How we build

    A person approves anything that matters. You decide which actions run on their own and which wait for sign-off. Every action is logged.

    You own what we build. The code, prompts and data are yours, documented well enough for another team to take over.

    We choose the AI model to fit the job. We're not tied to one provider, so you can switch when prices or quality change.

    Personal information is handled under POPIA. Data stays where it's allowed to be, and only the people who need it can see it.

    Start a project

    Tell us what you want AI to handle

    One automation or a team of agents. A few lines is enough to start.

    1. You send a short brief What you want AI to do, and how the work happens today.
    2. We reply with questions So we understand your systems, volumes and constraints.
    3. If it's a fit, we talk An hour together to work out where to start.

    Prefer to write it yourself?

    What do you need? Choose any

    Mention the systems you use today, if you can.