Services
Trading technology and AI services
Step 01
Trading charts
The chart is the part of a trading product clients judge first.
A chart that stutters in a fast market, loses drawings on refresh or shows a candle that disagrees with the account statement costs trust that is hard to win back. We integrate the major charting libraries properly and build custom charts where no library fits.
A good fit if
Clients complain about your charts, or you are choosing a library and want a second opinion before you sign a licence.
You keep
Your licences, your data agreements and the code.
What we deliver
- TradingView Advanced Charts and Trading Platform integrations, including the datafeed, symbol search and broker adapter
- Lightweight Charts builds for fast, small charts in web and mobile apps
- SciChart or custom WebGL charts for tick data, order book heatmaps and very long histories
- Saved layouts, drawings that persist across devices, and multi-chart workspaces
- Indicators checked against reference values, so your RSI matches everyone else's
Typical stack
- TradingView
- Lightweight Charts
- SciChart
- WebGL
- WebSocket
- React
- TypeScript
Step 02
AI chart analysis
Detection in code, explanation in plain English, and nothing that reads like a tip.
Chart analysis has moved from a nice extra to an expected feature on retail platforms. We build it in two parts: detectors that find structure on the chart, and a language layer that explains what was found. The design keeps the output descriptive, so it informs clients without recommending trades.
A good fit if
You want AI on your charts and need it to pass a compliance review, not just a demo.
You keep
The vocabulary, the sign-off and the logs. Your compliance lead approves every phrase a client can see.
What we deliver
- Support and resistance, ranges, swings, trend structure and breakout detection
- Classical chart pattern recognition with written rules for what counts as a match
- Volatility and regime classification
- Chart summaries written in wording your compliance team has approved, in several languages
- Annotations drawn directly onto TradingView or Lightweight Charts
Typical stack
- Python
- TypeScript
- Claude
- OpenAI
- Evaluation suites
- Lightweight Charts
Step 03
Trading platforms and apps
Front ends for brokers, prop firms and investing apps.
From a white-label web terminal to a full mobile app, we build the screens traders use all day and connect them to the systems that actually hold positions and money.
A good fit if
You are launching a trading product, or the one you have was built in a hurry and is starting to show it.
You keep
Your broker relationships, your repositories and your cloud accounts.
What we deliver
- Web trading terminals: watchlists, order tickets, positions, history and account views
- Prop firm dashboards: evaluation progress, rule monitoring, payouts and trader analytics
- Mobile apps with real-time quotes and charts
- Integrations with broker APIs, bridges, liquidity providers and FIX sessions
- Back-office and dealing desk tools for operations teams
Typical stack
- React
- React Native
- TypeScript
- Node.js
- Python
- FIX
- PostgreSQL
Step 04
Real-time market data
Every chart and every AI feature depends on data that arrives on time and agrees with itself.
We build the pipes: feed handling, storage and distribution, with the monitoring to notice when a vendor sends something odd before your clients do.
A good fit if
Your charts and your statements sometimes disagree, or data costs are growing faster than your client base.
You keep
Your vendor contracts and a replayable history you can audit.
What we deliver
- Feed handlers and normalisation across vendors and venues
- Tick and bar storage with fast range queries
- Candle aggregation that matches your platform's session times and time zones
- Historical replay for testing, demos and incident reviews
- WebSocket distribution with snapshots, throttling and backpressure
- Alerts for gaps, stale prices and outliers
Typical stack
- Python
- TypeScript
- ClickHouse
- TimescaleDB
- Kafka
- Redis
- AWS
Step 05
AI agents for trading teams
Narrow agents, with fixed tools, spending limits and a person in the loop.
The agents that earn their keep in trading businesses do one job well. A research assistant that answers from your documents and cites them. An operations agent that reconciles yesterday's trades and flags the breaks. Wherever money or clients are involved, a person approves the action.
A good fit if
A team spends hours a day on work that is repetitive but too important to get wrong.
You keep
Every approval. Nothing moves money or touches an order without a person.
What we deliver
- Research assistants over filings, news and internal notes, with citations
- Operations agents for reconciliation, reporting and onboarding checks
- MCP servers that give models permissioned, read-only access to market and account data
- Approval queues, audit logs and kill switches
- Evaluation suites that run before every release
Typical stack
- Claude
- OpenAI
- MCP
- Python
- TypeScript
- pgvector
Step 06
Ratings, research and analytics integration
Showing third-party analytics to clients, with the right labels around it.
Many platforms license technical ratings, research or sentiment data to show their clients. The integration is the easy part. Disclosures, entitlements and records take more thought, and we build them in from the start.
A good fit if
You license analytics from a provider and need to present them in a way your compliance team is comfortable with.
You keep
The entitlements and the records of who saw what.
What we deliver
- Integration of licensed research, ratings and sentiment feeds
- Display rules for source attribution, timestamps and disclosures
- Entitlements by client type and jurisdiction
- Records of what was displayed, to whom and when
Typical stack
- REST
- WebSocket
- TypeScript
- PostgreSQL
Step 07
Controls for AI features
If an AI feature sits near clients or markets, someone will ask what it said, and why.
We build the systems that can answer that question: logging, review, testing and the ability to switch a feature off quickly when something looks wrong.
A good fit if
You have an AI feature live, or nearly live, and no quick answer to 'what did it tell our clients last Tuesday?'
You keep
The off switch, and a log that answers the question before anyone has to ask it.
What we deliver
- Prompt, model and output logging, with retention you control
- Wording checks for advice-like language, price targets and guarantees
- Human approval steps and kill switches
- Evaluation sets and regression tests for every model or prompt change
- Documentation that links each control to the risk it addresses
Typical stack
- Python
- TypeScript
- OpenTelemetry
- PostgreSQL
Where we draw the line
Work we turn down
- Signal or tip services sold to retail traders
- Products that make investment decisions for retail clients without the right regulatory permissions in place
- Marketing tools that promise returns or present back-tested results as expected performance
- Anything designed to get around a platform's or regulator's rules
Show us the chart your clients complain about.
Most first calls start with a problem rather than a service name. Tell us what is not working. If it is worth fixing, the Platform Review is where it starts.