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Inseed

AI for fintech apps

Fintech apps, made smarter without a rewrite.

We add the AI features that matter in money products, an in-app assistant, document understanding, spending insights, and fraud and risk signals, to the app you already ship.

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You have a working app. Leadership wants AI in it.

Your app is in production. It moves money, it holds account data and PII, and it already passed the reviews that let a fintech product exist. Now leadership wants AI inside it: an assistant that answers "where did my money go", extraction that reads a statement instead of making the user type it, a fraud signal that catches the scam before support does. The idea is right. The way most shops respond is not.

Every quote you get back starts with "first we rebuild". In a money product that is not a quarter of work, it is re-opening the ledger, the payment path, and the compliance flows your team already signed off on. The real constraint in fintech is not the model, it is touching a system that moves money and is already audited. So we do not touch it. We add AI as a layer beside the app you have, wired to the data you already collect, and the parts that handle the money stay exactly where they are.

What we build

Four AI features that earn their place in a fintech app.

These are the patterns we see land in money products. Each one sits beside your existing ledger, payment, and onboarding flows, not inside the path that actually moves funds.

In-app money assistant

A conversational assistant that answers questions about the user's own account, transactions, fees, and statements, grounded in their data rather than the open web, and scoped to servicing rather than regulated financial advice.

What it changes
A customer gets a straight answer about a charge or a fee at 11pm without opening a support ticket, and low-value questions stop landing in your queue.
How it is built
Retrieval over the user's own account data, with guardrails that refuse to give regulated investment advice and hand off to a human when a question crosses the line.

Document understanding

Structured extraction from the documents your users already upload: bank statements, pay stubs, receipts, tax forms, and identity documents for KYC.

What it changes
Onboarding stops being a wall of manual typing. The app reads the document, pre-fills the fields, and flags what it is unsure about.
How it is built
A document-parsing step plus an LLM extraction pass, with a rules-and-human checkpoint before anything is written to an account record.

Spending insights and categorization

Turning a raw transaction feed into clean categories, trends, and a plain-language readout: the subscription the user forgot, the month that ran hot, the fee that keeps recurring.

What it changes
Users see their money in words instead of a wall of line items, and the app becomes something they open on purpose rather than only when a payment is due.
How it is built
Deterministic categorization and aggregation compute the numbers first; model-generated summaries sit on top and cite the transactions behind them.

Fraud and risk signals

A layer that reads transactions, logins, and messages, flags anomalies and scam patterns, and surfaces the risky few for review instead of the entire firehose.

What it changes
Your risk team stops watching everything and starts reviewing the flagged minority, and customers meet fewer bad actors before they lose trust.
How it is built
Anomaly detection and rules compute a risk score first; a model classification pass tunes it to your policy and feeds a review queue rather than auto-blocking on a guess.

Proof

What we have actually shipped in fintech.

We name only work we can stand behind, and we are precise about who delivered it. The first three are the fintech track record; the last three are the AI and mobile depth behind the features above.

Alight Electricity

Built by our founder

A mobile app that took a Nepali municipality's electricity billing from all-cash to online payments. Our founder built the whole iOS and Android app end to end, including the payment-gateway integration, and shipped it through App Store and Play Store review himself.

  • Integrated the e-Seva payment gateway with an authorize, deduct, and reconcile flow
  • Bill history plus a next-cycle cost estimate from the billing model
  • Early project (~2019), later retired when the government centralized the feature; no public link or adoption metrics

College Cost Secrets

Frontend built by our founder and team

A college-cost and financial-planning platform with four role-based portals for families, agents, admission professionals, and admins. Our founder and team built the frontend, including the calculators and the search-and-compare flows.

  • Federal Student Aid Index (SAI) calculator and financial plan
  • College search, favorites, and side-by-side comparison with saved searches
  • Four-role RBAC frontend across family, agent, professional, and admin
See our edtech work

Artizan

Delivered by Inseed

An AI market-intelligence and valuation app for art collectors and investors: browse artists, track market trends, and get AI price valuations for artworks. Our founder shipped it to production across mobile and web and built the AI price-estimation layer.

  • AI price estimation from image, dimensions, artist, and comparable sales across 17+ auction houses
  • Weighted valuation models with an explanation screen so the number is trusted
  • Live on the App Store and Play Store, with a Next.js web app
How we build with React Native

The LYVE App

Inseed project, live on both stores

A two-sided marketplace where our founder built a Stripe escrow model that holds a buyer's money and releases it to the seller once the trade completes. Money movement, held balances, and payout logic in a live mobile app.

  • 3,000+ users across iOS and Android
  • 200+ marketplace transactions on a Stripe escrow model
  • Real-time chat and dispute paths around the payment flow
Read the LYVE case study

Playlists

Delivered by our team via Toptal

A live app with an AI search that turns a vague natural-language query into results. The same retrieval pattern is what powers a money assistant that can answer "show me every subscription I am paying for" over a transaction feed.

  • GPT-4o query enrichment plus embedding-based semantic search
  • Staged matching pipeline that always returns usable results
  • Reported +200% growth in AI-driven searches after launch
How we build semantic search

Straight answer on the gap: we have not yet shipped a production LLM feature inside a bank-grade, regulated fintech app. What we have shipped is the pieces separately, real money movement (Stripe escrow in LYVE, a payment gateway in Alight), an AI valuation and market-intelligence product (Artizan), and semantic search in a live mobile app (Playlists). The audit is where we prove the specific feature on your app, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up under fintech load.

Coinbase, one of the largest crypto platforms, runs its consumer app on React Native, and Shopify runs it across commerce apps that move real money at scale. These are not our clients. They are proof that the framework we build on already carries security-scrutinized, money-moving products, so the question for yours is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your app, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a board-ready cost model. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Fintech questions, answered straight.

How is sensitive financial data handled when a prompt goes to an LLM?

This is the part most teams get wrong. There are two safe patterns: de-identify or tokenize the data before the call so no account number or raw PII leaves your boundary, or send it only to a provider on a no-training endpoint under contract. We default to a no-training endpoint with account identifiers stripped, and we log every field that leaves the app so the data path is auditable.

Can you add AI without touching our payment or ledger code?

Yes, that is the whole point of how we work. The AI feature sits beside the money-moving path as a separate layer, reading from the data you already collect, not writing to the ledger. The parts of your app that move funds and passed review do not change, so you are adding a feature, not re-opening an audit.

Will an AI assistant give financial or investment advice we are liable for?

Not unless you want it to, and we would advise against it. We scope the assistant to servicing questions about the user's own account, transactions, and fees, with guardrails that refuse to give regulated investment or tax advice and hand off to a human when a question crosses that line. The line is a product decision we set with you and enforce in the prompt and the retrieval scope.

Do you integrate with our core banking, Plaid, or payment provider?

We read from the APIs you already have rather than replacing them. If you expose a transaction feed through Plaid or a similar aggregator, the AI features consume that. On the payments side we have shipped real integrations before: a Stripe escrow model in LYVE and a payment gateway in Alight.

How accurate is AI document extraction for statements or KYC?

Accurate enough to save time, not trusted blindly. The extraction pass reads the document and pre-fills the fields, but a rules-and-human checkpoint sits before anything is written to an account record, and the model flags low-confidence fields for review. For KYC specifically the human review stays in the loop by design, because the cost of a silent error there is not worth the few seconds it would save.

Is React Native robust enough for a payments-heavy fintech app?

Yes, and we can point at live evidence. LYVE runs a Stripe escrow marketplace with held balances and payouts in production. Alight shipped a payment-gateway flow to both stores. Beyond our work, Coinbase runs its consumer app on React Native and Shopify runs it on high-volume commerce.

Ready to add AI to your fintech app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call