Some of the most valuable data in consumer finance is also the most opaque. Technov8 resolves it into three things people and machines can both act on — intent, ability and relationships. 19.9 million applications, structured at the moment of decision and linked to what happened next.
Conventional scoring infrastructure measures people by a history they were permitted to build. Millions never got the chance to build one — so the file comes back thin, stale or empty, and a large and commercially significant population reads as a blank. Credit-invisible is not the same as credit-unworthy.
A record of borrowing that never happened, refreshed on a cycle measured in weeks. It is structurally silent on the people who need answering most — and silent at exactly the moment a decision has to be made.
Structured fields declared at the point of real intent: what they earn, what they owe, when they are paid, what they need and why. Each record carries its capture context, and the outcome that followed where we hold it. Not a snapshot — a continuing signal.
Scoring was built for people with a past
We built ours for people with a present
Complexity is not the product. Clarity is. Every record that passes through the network is resolved into three answerable questions — the three that decide whether a financial relationship should exist at all.
Captured at the moment of application, not inferred weeks later from browsing exhaust. Amount, purpose, urgency and timing — stated by the person themselves.
Declared income, pay cycle and committed outgoings, captured at application — then read against what actually happened, so you can see how far a declared figure held up before you rely on it.
The match between a person and the offer that genuinely fits them — routed with permission, measured by what happened afterwards, and improved every cycle.
The hard part was never storing the data — it was making it legible. Technov8 sits between the machine and the human, turning a question anyone can ask into an answer grounded in real outcomes.
A Technov8 record is not a name and an email address. It is a structured financial picture declared by the person at the moment of application, carrying its own capture context — and it is the combination, not any single field, that is hard to assemble after the fact.
From training a model on outcome-labelled data to routing a live offer to the right person in real time. Every engagement is scoped, permissioned and measured against your own numbers.
Structured, outcome-labelled records for training and evaluating financial models — including the thin-file population most training sets cannot see. Delivered as governed datasets with a forward holdout, so your evaluation never goes stale.
Who is in the market, for what, and how urgently — as a signal rather than a list. Query an audience, subscribe to a segment, or call an API for a real-time in-market answer. Scores and flags, not personal data.
Put your offer in front of the people it actually fits, at the moment they are looking. Profiles are matched on declared affordability and stated need, routed with permission, and scored on what happened after the click.
Append affordability, intent and outcome signal to your own file and measure the difference against a holdout you control. If it does not move your conversion or your cost per funded customer, you have your answer quickly.
We operate inside consumer finance, where getting this wrong is not a growth problem — it is an existential one. So we would rather answer the provenance question first than last.
Records are collected at the point of application. Consent wording, source and capture timestamp travel with each record, so provenance can be shown rather than claimed.
Records are structured to a common schema and matched so repeat activity can be followed over time. Where a match is probabilistic, we say so — and we tell you which key it was matched on.
Access is tiered. Signal products return scores and flags rather than personal data, and aggregate views suppress small cells. No arbitrary queries, no individual selection.
Every engagement is scored on outcomes you can verify against your own holdout. What we claim, you can test.
Nobody buys data intelligence on a promise. Every engagement starts with one scoped question and a measurable answer — and you decide whether it worked, against a holdout you control.
What decision are you trying to improve, on which population, and what would count as success? We agree the fields, the volumes and the metric before anything moves.
A bounded run against a sample, or a match against your own file. Nothing goes to production and nothing is signed beyond the pilot itself.
Results are read against a control group you hold back, on your metric — approval rate, conversion, cost per funded customer, loss. If it does not move, you have your answer cheaply.
Move to a standing feed, a live endpoint or a licensed dataset, with refresh cadence and governance documented up front. Scope expands when the numbers justify it, not before.
Regulation differs by market. The three questions do not. Wherever a person asks to borrow, intent, ability and relationship are the same three unknowns — and the same engine resolves them.
The signal that matters — what someone earns, owes, needs and when — is declared the same way in every market. Only the wrapper changes.
Permission, provenance and access tiering are configured per market and enforced at the data layer, not bolted on as policy after the fact.
Every market feeds the same engine. What is learned about behavior in one becomes the head start in the next — new markets are a partnership, not a rebuild.
Whether you want to train on it, query it, match against it or enrich with it — the first conversation is the same: which decision are you trying to improve, and how would you know it worked?