Before co-founding Papermap.ai, an AI-powered no-code business intelligence platform, in July 2025, Simeone Nortey, a software developer, found himself spending less time coding and more time answering questions from work colleagues. The marketing team wanted data insights, but getting those answers usually meant digging through databases or running complex queries.
It’s a familiar situation in many growing businesses. Engineers want to spend their time writing code, while everyone else just wants clear answers from data. To bridge that gap, companies often rely on dashboards, hire analysts, or invest in expensive data tools, solutions that can be slow, complex, and costly.
For Papermap.ai, a Ghanaian startup operating across Africa and the US, the solution may lie in an AI agent that understands how business users actually talk.
According to Isaac Sarfo, co-founder and CEO, Papermap’s AI doesn’t just respond to English prompts. It understands Pidgin, Twi, French, and other natural languages, allowing non-technical users to query company data as they would ask a colleague.
However, Papermap did not start as an AI analytics company.
“We originally started by building an inventory management platform powered by AI,” Sarfo told in an interview. “But we quickly realised that the upside for analytics made a whole lot more sense.”
Inventory, he explains, was only one data pipeline. Most businesses sit on multiple streams of data, from payments and users to ads and operations, that rarely connect cleanly. Today, Papermap is a no-code data platform that lets companies centralise their data and query it without hiring a data team.
Instead of using SQL, which specifies what data to retrieve rather than how to retrieve it, or Python, a versatile programming language, users can simply ask questions in natural language. Papermap’s AI agents generate the code, pull the relevant columns, and return charts, insights, or reports in real time.
Why natural language matters
For Papermap, “natural language” is not just a user experience feature. “It can be Pidgin. It can be Twi,” Sarfo says. “I can ask it a question in Twi, and it will act as a data analyst and pull the data I’m looking for.”
That localisation reflects a broader thesis: Across Africa, advanced analytics faces major hurdles: high costs, limited skills, and challenging data environments. In some countries, broadband alone can eat up 44% of monthly income; fewer than 5% of young people have advanced data or cybersecurity training, and roughly half of big‑data projects fail or stall due to complexity, skill gaps, and expensive setup. Much like mobile money allowed the continent to bypass traditional banking infrastructure, Sarfo believes agentic AI can help startups skip the expensive data stacks that dominate the US market.
This localisation reflects a broader belief about why many African businesses struggle with data analytics. The challenge is not only cost, but also complexity and context. Sarfo argues that just as mobile money allowed the continent to bypass traditional banking infrastructure, agentic AI can help startups skip the expensive, technical data stacks common in the US.
By meeting users where they are linguistically and operationally, Papermap aims to make data less intimidating and more usable, opening advanced analytics to businesses that would otherwise be locked out.
“We actually think of ourselves as that complex data infrastructure,” he said. “The difference is that we abstract away the difficult part of the work.”
A “glass box” alternative
As data volumes explode, Sarfo cites estimates that over 80% of global data has been generated in just the past few years, and traditional analytics workflows are becoming harder to sustain. Engineers are writing code faster, but analysing the data behind it is lagging.
Papermap’s answer is what Sarfo calls a “Cursor for data”: a tool that accelerates analysis for both non-technical users and trained analysts.
“We are more of a glass box AI,” Sarfo said. “When you ask a query, you can see exactly what happened, the tables it pulled from, the code it wrote.”
Papermap’s primary customers are growth-stage businesses caught in what Sarfo calls the “infrastructure gap”: too large for spreadsheets, but unable to justify a six-figure data engineering team.
In the US, the company targets businesses earning between $10 million and $100 million in revenue, a segment that represents roughly 10% of SMBs but accounts for nearly half of GDP and employment.
Africa, however, demands a different approach.
“In Africa, it’s more of an API play,” Sarfo says. Instead of selling directly to thousands of merchants, Papermap embeds its AI inside platforms that already have scale.
In Ghana, Papermap works with VDL Fulfillment, a fulfillment company serving over 5,000 merchants. Merchants can now ask questions directly within the platform, such as how many orders were fulfilled, what failed, and what’s delayed, without waiting for support teams. A multi-tenant architecture ensures strict data separation.
In Nigeria, Papermap partners with fintech company Wallets, helping it turn payment data into forecasting and credit-underwriting tools for merchants. Another partnership with healthtech startup DoktorConnect aims to let users query personal health data and receive AI-generated insights ahead of doctor visits, a project that could eventually reach millions of students.
Localisation also shapes how Papermap distributes its product. With WhatsApp dominating communication across Africa, the startup is building a WhatsApp connector that allows business owners to text questions like, “How much money did we make this week?”
Behind the scenes, the AI writes the code, runs the query, and returns an answer that can inform real decisions, without dashboards or exports.
“Our goal is to accelerate AI adoption on the continent,” Sarfo says, “and help data be used more in day-to-day business decision-making.”
Papermap’s first backer is Jeff Dean, Google’s chief scientist. The company said it raised $500,000 pre-seed round in August 2025 and plans to raise a $5 million seed round later this year.
Policy, however, remains a challenge. Sarfo argues that innovation is moving faster than regulation across much of Africa. While he sees progress, he believes governments need to engage more seriously with AI as a foundational technology.
Papermap says it has already begun working with public institutions. On January 8, 2026, one pilot with a division of Ghana’s Ministry of Finance replaces hundreds of Excel sheets used by district assemblies to report project spending, reducing errors and speeding up reporting through AI-driven workflows.
Six months into active operations, Papermap’s growth has not been frictionless.
“We had a really good product, but the go-to-market has not been the easiest,” Sarfo admits. Still, usage metrics are climbing, with more active users, longer sessions, and increasingly complex queries.
API partnerships, in particular, are accelerating adoption faster than traditional SaaS sales globally.
For Sarfo, success comes down to one outcome: businesses making decisions based on data, not guesswork. “Every business had to become an internet business,” he says. “AI will be the same.”
