Product hacking is finding the growth lever in a company's own data (customers, funnel, conversations) and building the smallest product or channel change that moves it.
What it does, in order, and what it never does.
Connections
What the system never does
KINTO brings this method to companies that know something is stuck but cannot see where.
Growth stalls where the data is unread. A food manufacturer had a standard online store, dozens of products and over a thousand registered customers, and wanted to reactivate customers with nobody able to see which ones to reach. A stablecoin payments fintech needed to reach more users with a simple flow and set prices per route from operations. A last-mile delivery operator built its dispatch sheet by hand from several platforms that did not talk to each other.
KINTO reads four kinds of signals in every project: market (what customers search and ask for), business (what the numbers show), user experience (how people actually use the product) and support (what customers complain about). This absorbs the work usually sold separately as market intelligence, process optimization, advanced UX and product strategy into one method.
Then it goes to the company’s own data:
Each finding becomes a small build: a WhatsApp agent, a dispatch extension, a pricing change.
In practice this is KINTO’s forward deployed engineering applied to growth: KINTO’s engineers work inside the client’s conversations, ERP and sales data instead of advising from outside.
Companies with customer, sales or conversation data they have never analyzed, and a team that can act on a finding. Results are stated as the cases measured them, including what is still unknown.
WhatsApp & voice agents
By analyzing a year of invoices, KINTO found that fewer than half of a food manufacturer’s customers drove almost all of its volume; we built a production WhatsApp agent, an AI calling pilot and a headless store that kept every URL.
WhatsApp & voice agents
KINTO fixed the weak link of an e-commerce brand that sells over WhatsApp with a multi-model audited agent and order-by-order reconciliation; the owner reported a month with channel sales ≈30% above their history.
Product hacking & growth
For a fintech that pays out in local currency from stablecoins, KINTO built a scan-to-pay flow for Latin American instant-payment QR codes and per-route pricing governed from operations.
Agentic websites
KINTO merged two diverging versions of a beverage brand’s site into a single bilingual, English-first codebase, with SEO and FAQs built for US search engines and AI answers.
Agentic websites
In a sector where buyers distrust by default, KINTO took a site from a product plan with no code to a multilingual catalog with transparent pricing, power draw and hosting cost, plus a guide to verify before paying.
Agentic websites
For independent professionals, KINTO built lightweight sites that qualify visitors with a self-assessment and open WhatsApp with the message already written, with accessibility and security audited.
Agentic ERP
KINTO co-developed with a US last-mile delivery operator a dispatch cockpit that reconciles platforms that don’t talk to each other and proposes assignments with a human in the loop, delivered as a working browser extension.
To reconcile ad-platform reports with real sales, match each order to its true source and measure every channel's return against the same cash, not against each platform's own attribution. In one KINTO case, one channel returned about twice the other.
To turn years of WhatsApp conversations into knowledge an AI agent can trust, classify them with a closed taxonomy, transcribe voice notes, and admit a fact only if it appears in at least two distinct, recent conversations.
It means starting from the company's own data to find where growth is stuck, then building the smallest change that tests it. In a food manufacturer case, analyzing a year of invoices showed that about 45% of customers accounted for about 85% of volume, which redirected the work.
The data the company already has: invoices, customer lists, store analytics and real conversations. In the food manufacturer case it was tens of thousands of invoice lines over 12 months, segmented by repeat frequency and route. In the real estate case it was over a hundred thousand WhatsApp messages.
Yes, as steps of the same method. KINTO reads market signals, such as ads already running and branded search, business signals such as customers going cold, user-experience signals such as mobile share and support signals such as how conversations get answered. Every case page lists these four signals.
Where there is a measurable baseline, KINTO has analyzed results-based models. In a logistics case a pay-for-results model was analyzed with no agreement closed to date, and in a beverage brand's case a shared-risk model was proposed but not implemented. Both are stated as open, not as results.
It says so. In the food manufacturer case, the agent's conversion figure of about 1 in 4 conversations ending in an order comes from KINTO's own commercial proposal and is marked estimated, because KINTO has not audited how it was calculated. Verified and estimated figures are always labeled separately.
Not exactly. KINTO's product hacking is its forward deployed engineering applied to a growth question: it starts from the client's own data to find where growth is stuck, then builds the smallest change that tests it. Forward deployed engineering is the wider way of working; product hacking is its growth-focused part.
Tell KINTO what is going on. The first conversation is to understand it, not to sell.
