Fintech software development for investment platforms
Your investment platform, with AI that does the reading and leaves every decision to people.
For private markets, wealth, trading and market data platforms with live users. We build AI beside your regulated core, from your own documents and data, and ship it under your name.
- Drafts from the documents users upload
- A person applies, edits or rejects each value
- Your core system stays the record
Three ways to start, from $5,000: Build Sprint · Planning Sprint · AI Scorecard
app.keelstone.example / northgate / deals
Northgate Capital · Deals in draft
AI draftedDeally by Datics AI · Try it step by step →
Illustrative demo on a fictional firm. Not client work.
Who it is for
Built for established fintech platforms.
Each page leads with the modules that fit that kind of platform, the questions its buyers ask and where most teams start.
Building banking, lending or payments software? Tell us what it does, and we tell you plainly whether we fit.
Book a 30-minute Product ReviewWhy fintech AI stalls in established platforms
What gets in the way, and how we handle it.
- 01
A regulated core nobody wants to touch
Services beside the core
AI runs as separate services that reach your core only through its APIs, so the regulated system and its database stay as they are.
- 02
Hours of copying from decks and filings
Drafted from the documents
Agents read the uploaded files and suggest each field beside its current value; a person applies, edits or rejects it.
- 03
Answers users cannot check
Cited, from your own data first
Answers come from the platform's own data and documents and cite the filing, story or page behind them.
- 04
AI that could act on money
Agents prepare, people decide
Agents research and prepare; orders, deals and interest go ahead only after a person confirms.
- 05
Users who must never see each other's data
Your roles, in every answer
Every agent works inside the roles your platform already has, scoped to the deal, fund or account in view.
Why AI earns its place in your product
Move deals faster
Days of form filling, drafted.
Issuers and analysts review suggestions instead of retyping documents.
Keep users
Research they can check.
Cited answers and clear next steps, inside the platform they already trust with money.
Easier to review
Built for regulated work.
People decide, sources are shown, and your core stays the system of record.
Modules by Datics AI
Start from AI we have already built, not from zero.
Fintech AI you can see before you buy: each module runs on a fictional firm and builds on AI we have built for live investment and trading products.
Issuers upload their deck and financials, and the deal drafts itself for them to review.
Fits: Private investment marketplaces, crowdfunding and Reg D platforms, deal room software
Diligence forms pre-filled from the manager's documents, each value linked to its source.
Fits: Fund administration, allocator and wealth platforms, private markets data products
Issuers see which investors fit their round; investors see deals that fit them.
Fits: Private investment marketplaces, deal platforms, investor CRMs
Investors ask about a company or the market and get an answer that cites its sources.
Fits: Investing and trading apps, market data and research products, wealth platforms
From research to a prepared order in one conversation, and the investor confirms it.
Fits: Investing and trading apps, brokerage front ends
AI and automation added as separate services that talk to your regulated core through its APIs.
Fits: Any regulated fintech product with its own core system
Deallyby Datics AI
AI deal drafting for private investment platforms
Issuers upload their deck and financials, and the deal drafts itself for them to review.
Fits: Private investment marketplaces, crowdfunding and Reg D platforms, deal room software
Vettlyby Datics AI
AI due diligence and onboarding for fund platforms
Diligence forms pre-filled from the manager's documents, each value linked to its source.
Fits: Fund administration, allocator and wealth platforms, private markets data products
Matchlyby Datics AI
AI investor matching and deal recommendations
Issuers see which investors fit their round; investors see deals that fit them.
Fits: Private investment marketplaces, deal platforms, investor CRMs
Citelyby Datics AI
Cited AI research answers over market data
Investors ask about a company or the market and get an answer that cites its sources.
Fits: Investing and trading apps, market data and research products, wealth platforms
Tradelyby Datics AI
AI investing agents that prepare orders investors confirm
From research to a prepared order in one conversation, and the investor confirms it.
Fits: Investing and trading apps, brokerage front ends
AI beside your coreby Datics AIService
AI services around a regulated core system
AI and automation added as separate services that talk to your regulated core through its APIs.
Fits: Any regulated fintech product with its own core system
Also for your product
Your product, inside Claude and ChatGPT.
We build a connector so your users can ask their own AI assistant about their data in your product, each signed in with their own login and seeing only what they can already see. It starts read-only, only the fields you approve leave your product, and actions each user approves can come later.
How we build connectorsHow many onboarding files are waiting for my review?
Five. The oldest arrived Tuesday, and two are missing a signed form.
Proof
Our fintech work.
AI we have built for Cosmic AI, Peret, a regulated private investment marketplace and an investment administration platform.
57 featured AI case studies across our work
Regulated investment platform
Private markets · Deal drafting
Pitch decks become drafted deals, and issuers approve each field before publishing
Issuers retyped decks and financials into long deal forms. Now AI suggests each field, and the issuer applies, edits or rejects it.
Cosmic AI
Investing apps · Agents to orders
Retail investors go from research to a confirmed order in one conversation
Investors juggled data, news and a separate broker. Now agents research and prepare each trade in chat, and nothing is placed until they confirm.
Investment administration platform
Fund platforms · Due diligence
Portfolio & Sub-Client Investment Administration
AI-assisted fund onboarding, due diligence and document intelligence inside an investment administration platform.
More fintech work
Regulated investment platform
Private markets · Investor matching
Investors get matched deals and document-backed answers, while every decision stays theirs
Investors read long deal files alone and issuers hunted for backers by hand. Now AI recommends, checks and drafts, and people decide.
Cosmic AI
Investing apps · Cited research
Investors get answers that cite the filing, news story or post behind them
Filings, news, social posts and market data sat on different sites. Now one chat answers from all of them and cites the source behind each answer.
Peret
Trading analytics · Prediction markets
Traders see AI forecast edges in an inherited app, finished without a rebuild
Peret had another developer's half finished build. We proved what worked, fixed the rest for beta and delivered its prediction network.
How it works
Three ways to start, each at a fixed price.
A 30-minute review tells you which one fits. Whichever you pick, you know the production price before you commit, and your fee counts toward it.
Fintech Platform Product Review · 30 minutes, no chargeStart here. We look at your platform with you, agree the outcome you want for your users, and tell you which of the three below fits.
Book a 30-minute Product ReviewNo AI yet, and a module fits
Build Sprint
$10,000 fixed3 weeks
Pick it when: One of our modules delivers the outcome you want.
Deally · Vettly · Matchly · Citely · Tradely
- One user outcome (2 to 3 related capabilities) working in your staging, behind a feature flag
- Pull requests to your repo, built on sample deals, documents and synthetic accounts
- Quality and safety evals, the cost per user and a packaging recommendation
- A fixed-price quote for production
Something new, or a plan first
Planning Sprint
$5,000 fixed2 weeks
Pick it when: You need something our modules do not cover, or want a plan before you build.
- Where AI fits across your platform, including beyond our modules
- A prioritized capability map, with what each one depends on
- A readiness review of your architecture, data, integrations and permissions
- A fixed-price quote to build it and take it to production
AI already built or tried
AI Scorecard
$5,000 fixed2 weeks
Pick it when: You have already built or tried an AI feature in your platform, live or not.
- One AI feature you have built, tested in staging on 50 of your own cases, with names and account details removed
- Tests for invented numbers, missing sources, another user's data and prompt injection
- Cost per user and a packaging recommendation
- A scorecard, ranked fixes, draft security answers and a fixed quote
Not a compliance audit or certification.
How the AI Scorecard worksProduction, at a fixed quote
We take what your Sprint built or planned live for your users, by pull request, with no standing production access. It typically includes load testing at your peak and a spend cap per firm. Your Sprint or Scorecard fee counts toward it.
After go-live, optional support
The product is finished and yours to run. If you want us to stay on: a named lead engineer, a monthly quality and cost report, testing before any model change, and more capabilities, each at its own fixed quote.
Stop after any Sprint and keep what you got.
How a Build Sprint runs
- In the setup week we map your core system, data and roles and agree what the Sprint touches.
- We follow your coding standards, tests and CI; your engineers review and merge every pull request.
- Half at signature, half when the Sprint Pack is delivered.
- Your time: about 2 to 3 hours a week from a product owner, plus an engineer to review pull requests.
Need integrations first? AI beside your core covers AI services beside your core. Ask in the Product Review.
Built for regulated platforms
Controls your customers' security reviews ask about.
Each control is a design fact about how the module is built and how we work, not a compliance claim.
Beside your core
Services that reach your system of record only through its APIs and the roles it already has.
Sample data in Sprints
Sprint work uses sample deals, documents and synthetic accounts, and ships as pull requests to your repo.
People decide
Orders, deal values, interest and KYC go ahead only when a person confirms them.
We work within your SEC, FINRA, AML and KYC, and privacy requirements. Compliance depends on the complete product, contracts, policies and operating environment; Datics does not present software engineering alone as legal certification, and nothing a module shows is investment advice on its own.
How we handle financial data and decisionsIn AI and data since2018
- AWS: Select Tier Partner
- Anthropic: Partner · Claude models
- CopilotKit: Agent user interface
- LangGraph: Production agent engineering
- Databricks: Governed AI & agent workflows
- Cursor: Agentic development environment
- Microsoft Azure: Production AI delivery
- Google Cloud: Production AI delivery
AI shipped inside products for
Questions we hear on the first call
Straight answers before you book.
The questions fintech product and engineering leaders ask most.
Can you add AI without touching our regulated core?
Yes. That is how we work on regulated platforms: AI runs as services beside your core and reaches it only through its APIs, so the application and its database stay the system of record.
Can the AI place a trade, publish a deal or approve KYC?
No. Agents research, draft and prepare. A person confirms every order, applies every deal value and makes every investment and KYC decision.
How do you stop the AI making up numbers?
Values come from the documents and data on your platform, each linked to its source. When a value is not there, the field stays blank and is flagged, never guessed.
Can each user see only their own deals and accounts?
Yes. Agents work inside your existing roles, such as admin, issuer, investor and sub-client, and each assistant is scoped to the deal, fund or account in view.
Which brokers and data providers have you worked with?
Robinhood behind explicit consent as the broker; Kalshi and Polymarket market data, Financial Modeling Prep, Yahoo Finance, and news, filings and social data. We scope yours in the Product Review.
Do you need our customers' data in the Sprint?
No. Sprints run on sample deals, sample documents and synthetic accounts in your staging, and code reaches your product as pull requests.
How do you choose a fintech software development company?
Look for a team that has shipped AI into live investment or trading products: regulated systems of record, brokers and market data, and roles that keep users apart. Ask who decides when the AI is wrong, how sources are shown and who owns the code. We answer each of these in the Product Review, with the work behind it.
Who owns the code?
What we build for your product lives in your repository and ships under your name. The reusable engine underneath stays ours and is licensed to you; the terms are in your contract.
Do you have SOC 2?
Not yet. We work in your staging by pull request, under your accounts, and in production only through access you grant and can revoke.
Where are you based?
Datics is headquartered in Austin, Texas, with a named lead engineer and US-hours overlap on every engagement.
Fintech insights
How we think about the hard parts.
Answers for product and engineering leaders, from the team that builds it.
AI agents
Where should human in the loop AI review happen?
Human in the loop AI works when review happens field by field, before anything ships. Learn where to place checkpoints and how to measure accuracy.
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How does RAG security scope retrieval to user permissions?
RAG security in multi-tenant SaaS starts with whose documents a question may search. Learn how to filter by tenant and permission before ranking.
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Where should AI agent security enforce permissions?
AI agent security in a multi-role SaaS product rests on one rule: the agent gets the user's permissions and nothing more, enforced in code, never in the prompt.
Rana Umar Majeed · Sep 28, 2026RAG and AI assistants
RAG evaluation: how do you test that citations hold up?
RAG evaluation must check more than whether citations exist. Test that each cited passage supports its claim and that answers decline when sources are silent.
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What enterprise AI security questions do buyers ask?
Enterprise AI security reviews ask where data and models run, who reaches production, what the AI may do alone and how it is logged. Here is how to prepare.
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How do you design tools for LLM tool calling?
LLM tool calling works in an existing product when each tool maps to a real, approved endpoint with a clear name, description and schema. Here is how.
Rana Umar Majeed · Oct 21, 2026See it on your own platform.
Bring one product, workflow or AI feature. In 30 minutes we tell you which of the three ways to start fits, what it would cost to serve per user and what your compliance team will ask.
Own several fintech products? Datics for private equityÂCalls run in US Central time. No generic AI presentation: we discuss your product, users and production constraints.