Become AI-native, the easy way.

To become AI-native, put AI to work across the software your company already uses.

Start with what you want to change

The board has asked for an AI strategy. A key employee is leaving. Workloads are growing during a headcount freeze. Another busy season is approaching.

Maybe you want to open a branch, expand the business, or rely less on your daily involvement. Maybe you want to lead a useful AI project and take on more responsibility.

Where do you want to take the company? Expand into new services or markets, reduce expenses, or increase revenue? What does the company expect you to deliver?

We need to understand your responsibilities and ambitions before we can explain how Rifoa can help. Those goals determine what to automate, how to measure success, and where AI can create value.

Keep the systems you already use

Rifoa builds digital employees that take your inputs, follow your instructions, and deliver the output you need. They work across departments, using your systems and your rules.

We handle setup, integration, and ongoing improvement. There’s no new software to learn or habit to pick up. We take work off your plate without asking you to change how you work.

Rifoa is the simplest way to put AI to work.

A digital employee needs a clear objective, access to the right tools, and examples of the output you expect. It can work around the clock.

We prepare it for your business: map the process, connect your systems, and test exceptions with your team. It works in the software your team already uses, without adding a separate application for every task.

We identify any required system changes before we start. Paper-based work is digitized first; work already in software is connected to the agent.

Your team checks the output against an agreed standard. The agent escalates decisions that need your judgment instead of asking you to supervise every step.

We test for the quality you expect from a strong employee, with faster delivery and a lower cost to complete the job.

Start where you can check the work

The order of automation matters. We usually start with finance, where invoices, purchase orders, and reconciliation records provide documented inputs and clearer checks.

Account reconciliation, invoice processing, month-end accruals, purchase-order matching, management reporting, and variance analysis are good starting points.

For each job, we examine the current process, missing documents, and exceptions. Then we build and test the digital employee with your team, including repeated work, long-running tasks, and approval points.

From there, move into operations: tenders, purchasing, manual entry, scheduling, and work across departments. Customer-facing sales generally comes later.

These are starting points. We work on the jobs that matter to your company, including work that crosses functions or falls outside familiar use cases.

Don’t copy another company’s roadmap

If you know which job you want to automate, start there. If you don’t, we help identify a documented workflow.

A competitor’s project reflects its priorities. Its bottleneck may already be solved in your business. Use its experience as a reference, then assess your own constraints, strengths, and growth goals.

We help decide what to automate, what to leave alone, and in what order. Finance often provides a documented starting point before operations and customer-facing sales.

Your company has its own way

Two companies in the same industry can work completely differently. The difference may be a spreadsheet, an approval rule, or an undocumented agreement.

A product is a good choice when it solves the defined job. Company-wide implementation also requires the surrounding processes, systems, and responsibilities to work together.

That’s why we believe complete automation requires a service: understanding the business, connecting systems, resolving exceptions, and taking responsibility for continued operation.

Adding another platform while leaving your team to connect everything fails that objective. We deliver completed work inside your business and take responsibility for the implementation behind it.

Start with the alternatives

Try your own team, an internal hire, an existing vendor, or tools like ChatGPT and Claude Code. If they solve the problem and keep it working, use them.

Hire an internal engineer to build and maintain the capability in-house. Use an IT firm for work that fits its systems expertise. Either way, define the work and judge the result by what gets completed, how failures are handled, and who maintains it.

A working local prototype is a starting point. Running it across your company’s AWS environment also requires permissions, monitoring, and reliable access for the wider team.

We take a successful demonstration into daily operation, including requirements that emerge when more people start using it. We apply lessons from other projects, test difficult requirements early, reuse proven approaches, and adopt new capabilities when they improve the result.

When existing approaches hit a wall and you need a fresh perspective, that’s where we come in.

More usage isn’t the goal

“Use more AI” leaves the objective undefined. Give employees a result to achieve and a clear measure of success.

404 Media reported that Accenture limited central Claude funding to $29 per person each month, with additional usage requiring project funding. Some employees had spent up to $1,000. The report does not measure the value of that usage. It shows why AI spending needs to connect to defined projects and completed work.

Agree on what finished work looks like. Measure time saved, errors caught, and the full cost of completing the job, including checking and maintenance.

More usage matters when it produces better results.

Give agents the difficult work

AI is already helping solve open mathematical problems. Complex business work deserves the same ambition. Test the difficult requirements before deciding they cannot be automated.

Show us the task you think AI can’t handle. We test missing information, unusual cases, long sequences of work, and decisions that require human judgment.

Give agents complete departmental processes, not just email drafting. Test work that currently depends on one exceptional employee to keep it running.

Our ambition is to automate digital work across a business, from repetitive entry and scheduling to complex operations. Human effort should go where it creates value.

The goal is to assign a batch of work and return to review the completed output. We’re building toward that experience job by job, testing reliability and defining approval requirements. A powerful model needs a dependable implementation to deliver it consistently.

Connect the departments

Connect agents so each job contributes to the next. An order moves into operations, purchasing gets the requirements, and finance gets the records. Shared context reduces manual handoffs; live dashboards show progress without someone having to request an update.

We’re turning our own company into one giant AI: connected work, shared memory, and live dashboards. People set the direction and make the calls that require them.

We use what we build and take responsibility for it in our own operations. At Rifoa, we’ve put AI to work in tax workflows, technical work, and reception calls. Sales email is next.

The goal is a company that can grow without everything depending on one person, department, star employee, or you.

Automate work in batches. Connect what you build. Give the next job to an agent. Your company becomes AI-native over time.

Our objective is maximum AI acceleration: turn advances in models into useful work across your business, with less delay between a new capability and its adoption.

Some work should come later

We generally automate internal work before persuasive, customer-facing interactions. That gives us room to test and improve before customers use it.

Reception has a narrower purpose and a clear handoff. A sales call involves timing, trust, personal judgment, and reactions you can’t fully anticipate.

Evaluate the whole experience: response delays, running costs, and whether customers want to engage. A model’s ability to respond doesn’t prove that the interaction works.

Tavus already offers live conversational video AI. We expect AI sales conversations to become more ordinary over the next year or two as the experience improves. Customer acceptance must develop alongside capability, so test the experience before making it the default.

For a construction company, the crew still does the physical work. We automate the invoices, tenders, purchasing, and coordination that support the work on site.

We keep it working

Processes, models, and software change after launch. Monitoring, maintenance, and improvement need clear owners.

We charge a fixed setup fee for the agreed work. After that, choose a monthly service for monitoring, fixes, changes, and upgrades, or have your internal team take over.

We agree on model, hosting, and other running costs upfront. Our managed service covers model selection, replacements, and upgrades based on the job.

Your team’s feedback helps us improve the workflow, test changes, and introduce better models. We keep checking performance as the volume and complexity grow, and give you progress updates and completed work.

You own what we build. We can keep improving it or document the implementation and train your team to take over.

No vendor lock-in. Continue with us because we remain useful. Your independence keeps us accountable to keep creating value.

What would that give you back?

If digital employees take on work that occupies several salaried people, what is that capacity worth to your company?

Measure output, quality, delivery time, and cost. If you want the capacity of five or fifty additional employees, evaluate it against those measures.

Use that capacity to serve more customers, open a branch, launch a product, or handle seasonal demand without extending the team’s working day.

Involve the person responsible for investment. Evaluate the project by how the company operates, not just against the price of a ChatGPT subscription.

A successful implementation gives the business evidence to act on. Completing a difficult job builds confidence to automate the next process and expand the scope.

That shift changes the trajectory of the company.

A business can thrive and still be inefficient

The world is incredibly inefficient and surprisingly forgiving. A business can keep doing things the old way and still make money.

Japan is a useful example. Its Small and Medium Enterprise Agency describes businesses still handling orders by fax and phone. Electronic ordering is advancing, but older methods remain.

It’s easy to understand why an owner might feel little urgency to change. Customers have known them for twenty years. They trust the work. Orders come through relationships built over decades. The business doesn’t need a constant supply of new leads.

A company could have no website or email address and still use a fax machine for orders. Whether it makes $3 million a year or another amount, the owner’s question is practical: does the business provide the life they want? That figure is an illustration, not a reported result from a Japanese company.

The owner may accept slow growth, flat revenue, or a modest decline. They may have no interest in becoming a Fortune 500 company. Existing relationships can support a good business and a comfortable life for a long time.

That doesn’t make the systems efficient. It means the business has enough other advantages to live with the inefficiency.

Closer to electricity than a website

We think AI changes that calculation.

You could miss much of the internet revolution and still run a viable business. Customers could find you through people they knew. A computer might improve the operation without changing its foundations.

Our view is that AI will become closer to electricity: a capability almost every business depends on, even if its owner has no particular interest in the technology.

Sam Altman and Jakub Pachocki compare the opportunity ahead with electrification. We agree with the scale of that comparison. Electricity changed what people could produce, where they could work, and what a normal standard of living could include. We expect abundant intelligence to reach just as deeply into the economy.

Imagine extraordinary workers who can learn a new field, investigate a problem, build a system, and keep improving it. They work across finance, operations, engineering, research, and customer service. Their knowledge travels between departments instead of leaving when someone resigns.

“300-IQ workers” is how I picture the ambition, not an IQ score assigned to a current model. The important capability is intelligence you can put to work: solving problems, taking action, and producing something useful.

Once that capability is dependable and widely available, running a company without it may feel like running one without electricity. Long-standing relationships will still matter. The cost and capability of the work behind them will change.

Intelligence should become a basic right

This reaches beyond a company’s expenses.

Clean drinking water changes what a person can expect from life. Hospitals change which illnesses they can survive. Electricity changes what a community can build. We believe access to powerful intelligence belongs in that conversation.

Altman has argued that access to AI may eventually be considered a fundamental human right. We believe it should be one. Wherever someone is born, they should be able to learn, solve problems, and build a livelihood with this technology.

That should apply in the United States, Somalia, and everywhere else. If intelligence becomes fundamental to earning, learning, and participating in society, access cannot remain a privilege reserved for a few companies or countries.

Imagine what a country could do with far more intelligence available to its people: better hospitals, faster research, stronger schools, more productive farms and factories, better public administration, and more businesses started by people who lacked the money or expertise.

A 100× improvement in what a country can accomplish is the scale of ambition we have in mind. It isn’t a measured forecast of national income. We want to raise living standards and expand what a country can do across industries and everyday life.

A country’s capacity to act

Intelligence at that scale would also change power and influence.

History offers different examples of capabilities that shape a country’s position. Drinking water sustains a population. Electricity supports an economy. Nuclear weapons carry enormous geopolitical weight. Their purposes and consequences differ, but each shows how access to a fundamental capability can change what a country can do and how others respond.

Access to the sea and major trade routes is another useful comparison. Maritime connections helped shape the UK’s trading power, Singapore’s growth as a trading centre, and Venice’s history as a maritime power. Geography alone doesn’t make a country wealthy. The parallel is access: an opening to capabilities and markets that would otherwise be harder to reach.

We expect access to intelligence to carry similar strategic importance, affecting productivity, living standards, scientific capability, and influence across sectors. I struggle to think of many developments with comparable reach.

That’s why we take this work seriously. Helping a business put AI to work is one practical part of bringing that capability into the real economy.

The employees win. The shareholders win.

An employee gets time back and a chance to do more valuable work. An owner gets more capacity and fewer points of dependence.

Every employee should be able to delegate digital work, not just senior managers who already have teams.

Leading a useful AI project builds experience, demonstrates ownership, and creates opportunities for more responsibility. Those skills can also help someone start a business.

Maybe the ambition is a promotion, a pay rise, a new industry, or your own company. Build toward it by delivering work others can evaluate and trust.

Some jobs will change or disappear. AI will reshape how people earn, how companies grow, and what it costs to get work done. The economic changes extend beyond a software budget.

The opportunity also reaches beyond changing existing jobs. AI gives people resources to build businesses and pursue work that was previously beyond their reach.

The gains should improve life as well as business performance: proper breaks, time with family and friends, and room to think, build, and pursue meaningful work.

We hate watching people spend their lives on work a machine could do.

AI can be complicated in the background. Your day shouldn’t have to be.