Trading technology and AI, London
An engineering studio for brokers, prop firms and fintech teams. We build the AI chart analysis your compliance lead can sign, and we will tell you when not to build it at all.
TradingView, Lightweight Charts, SciChart or custom WebGL, wired to your data and your broker. Drawings that persist, layouts that sync, candles that agree with the statement.
Learn moreIllustrative screen built on synthetic data
What we build
TradingView, Lightweight Charts, SciChart and custom GPU charts, wired to your data and your broker.
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Pattern, level and regime detection with plain-English chart summaries that describe the market without advising on it.
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Web and mobile trading front ends, order tickets, portfolio views and prop firm dashboards, connected to broker APIs and FIX.
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Order entry, market data and drop copy over FIX, plus REST and WebSocket broker APIs, tested before they touch a live order.
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Ingestion, normalisation, tick storage, aggregation and replay, built to stay fast when the market is busy.
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Exposure monitoring and surveillance views that show what happened, down to the tick, so a desk can act and an auditor can follow.
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Research assistants, operations automation and MCP servers for trading data, with approvals, limits and audit built in.
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Logging, approvals, kill switches, evaluation and wording reviews for AI that sits close to customers and markets.
Learn moreWe also bring licensed and in-house research and ratings into trading products. All services
Where our experience comes from
Capital markets
100m+
ticks a day charted for a surveillance desk
A charting engine for a global investment bank's trading floor, with views down to the nanosecond. Routine compliance monitoring work cut by more than 70%.
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Front-office trading
6
trade capture and pricing apps on one ticket library
Bond, repo, swap, structured and RFQ booking for a UK investment bank's markets division, with new products onboarded without code changes.
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Retail banking
6
UK banking brands on one tested foundation
Digital banking products for six of the UK's biggest financial brands, delivered by the GCL team.
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Delivered by our founder and the GCL team before AlchmAI was formed. Client names are withheld under confidentiality agreements.
Shown, not claimed
Try the wording check
runs in your browser
Every AI summary passes through a filter like this before a client sees it. Type anything, or load an example.
A demonstration with a short rule list. The production filter is larger, is tuned with your compliance lead, and logs every decision. Illustration only, not advice.
Chart summaryfrom 4 detected facts
Wording check passedLogged A-20417 · model v3.2 · prompt p-114
Synthetic data
Illustration only. The summary describes this chart. It does not forecast, and it is not a recommendation.
Signed-off analysis
Levels, ranges, patterns and regimes are found by tested code. Same chart, same answer, every time.
A language model writes up only the facts it is given, in wording your compliance team approved.
Advice-like phrasing, targets and predictions are rejected before display. You tried it above.
Every output is logged with its inputs and model version, so your compliance team can review any of it.
What the analysis says
What it is built never to say
Price has closed above its 20-day high for the first time since 4 March.
Price is breaking out. Consider buying.
The range has narrowed to its smallest width in 120 bars.
A big move is coming, so position now.
Volume on this bar is 2.1 times its 20-bar average.
Smart money is accumulating.
“Digital engagement practices used by trading apps, such as gamification, can increase the trading frequency and risk taking of consumers.”
FCA, research note on digital engagement practices in trading apps. Our analysis features are built to inform, not to prompt a trade.
Where exactly the line sits depends on your permissions, your clients and your compliance team. We build the controls; they set the policy.
How we build chart analysisThree things an agency will not say
01
If a language model writes your summaries straight from a prompt, it has almost certainly told a client to consider buying, and you have no log that proves otherwise. Paste one of its sentences into the wording check above. If it is blocked, you have your answer.
02
Most charting projects should be a library, not custom code, and most AI features should wait until the data under them is clean. The Platform Review ends in "do not build this" about as often as it ends in a plan. The fee is the same either way, and so is the report.
03
Signal services sold to retail traders, products that make investment decisions without the permissions to do so, back-tests dressed up as expected returns. We have said no to each of them, and we will again.
Who we work with
Acquisition costs are up, switching costs are down, and retention now decides the year. Clients compare your charts with TradingView, expect the AI features rivals already ship, and every one of those features has to clear a Consumer Duty review.
We build: Charting upgrades, AI chart analysis written to pass compliance, and data pipelines that keep costs in line with the client book.
The 2024 MetaQuotes cut-off forced platform migrations overnight, around one firm in seven closed in the eighteen months that followed, and regulators on both sides of the Atlantic are now examining the model. Breaches must be caught as they happen, and payouts are where disputes land.
We build: Evaluation dashboards that show the metric, the time and the rule version, real-time rule monitoring on tick data, trader analytics, and integrations that survive a change of platform.
A trading app takes five to fourteen months to build and the licensing often outlasts the engineering. Investors and due diligence want a product that holds up, not a demo.
We build: First versions on partner broker APIs, charts and data done properly, and the controls that due diligence asks about.
Students now expect to upload a chart and have it read. The apps doing this hand out so-called educational price targets, and the FCA is prosecuting people who stray from education into promotion.
We build: Descriptive chart analysis, screeners and simulators that teach without tipping.
People hired to find signal spend months on symbology, corporate actions, timestamps and look-ahead bias before a model is tested.
We build: Clean tick and bar stores, point-in-time data, replay, and the same schema for history and live.
Not sure you fit? If your product puts market data in front of people, there is a good chance we can help.
How an engagement works
Weeks 1 to 2
Read-only access. Findings report, costed plan and a prototype where it helps. You sign off the baseline we will measure against.
Weeks 3 to 8
Working software every two weeks on an environment you can use. Any AI wording is agreed with your compliance lead before a client sees it.
Go-live
A runbook, an evidence file your compliance lead can read in one sitting, and an off switch for every AI feature.
Day 90
What changed, measured against the baseline you signed at the review.
Why AlchmAI
72
engineering guides with working code
48
sourced articles on AI and markets
We publish how we think, so you can judge our work before you speak to us.
Latest writing
Agentic AI vulnerability research found PHP2Shell, two unauthenticated PHP RCE bugs. Find PHP across a trading firm's estate and patch it with human approval.
16 min read
Self-hosting LLM inference explained for trading firm CTOs: what DeepSeek's DeepInfer engine does, when self-hosting pays, and an AI bill of materials gate.
18 min read
A RAG ingestion guard in Python: find hidden LLM instructions before embedding, quarantine them, and fail the build if complaint routes vanish.
16 min read
CoreWeave's bonds touched a 15% yield and its shares fell 10%. What the AI credit trade means for brokers, prop firms and quant teams, in plain numbers.
8 min read
AI agents trading accounts: eToro's Claude and ChatGPT connectors, Dimon's 'liability transfer' warning and regulators asking how to shut agents down.
8 min read
Claude can now fan one job across 1,000 agents with no launch approval. TD Bank's mortgage agents show the agentic workflow automation pattern that works.
8 min read
Questions
No. AlchmAI is a technology company. We build software for businesses that operate in financial markets. We do not give investment advice, publish trading signals or manage money for anyone. When we build AI features for clients, we design them to describe market data rather than recommend trades, and our clients stay responsible for their own regulatory permissions.
It depends on your licence position, your data volumes and how far you want to customise. TradingView's libraries are what most traders already know. Lightweight Charts is small and open source. SciChart is built for very large, fast datasets. We compare them properly on our Charts and AI page, and we will give you a straight answer on a call.
Usually, yes. We integrate through whatever your stack exposes: REST and websocket APIs, FIX sessions, platform APIs or a read replica of your database. Scoping is where we confirm what is possible, before you commit to a build.
In layers. Detection is done by deterministic code, not by a language model. The model only writes up facts it is given, using a vocabulary your compliance team approves. A separate check rejects outputs with advice-like wording, price targets or predictions before anything reaches a client, and every output is logged so it can be reviewed later.
Signed-off analysis is how we build AI chart features that a compliance lead can approve: deterministic code finds levels, ranges and patterns; a language model writes up only those facts in an agreed vocabulary; a wording check blocks anything that reads as advice; and every output is logged for a person to review. Two parts of it, the summary and the wording check, run live on the AlchmAI home page.
AlchmAI was founded by Kush Chheda, who has spent more than ten years in trading technology across prop firms, FX, crypto and investment banks, including a charting engine for a trading-floor surveillance desk and the ticket library behind six trade capture applications. AlchmAI is part of the GCL group.
Mostly trading businesses with roughly 5 to 150 staff: big enough to have clients on a platform every day, too small to carry a bank's technology budget. The buyer is usually the founder, CEO or head of product, with the CTO and the compliance lead involved from the first call.
Every engagement starts with a two-week Platform Review. We take read-only access to your charts, data feeds and any AI feature, test them against what your clients actually see, and hand you a findings report, a costed plan and, where it helps, a working prototype. Fixed price, agreed before we start, and credited in full against a build that begins within 60 days. The report is yours whichever way you go.
The Platform Review is a fixed price, agreed before we start and credited against a build that begins within 60 days. Build work is quoted from the plan the review produces, so you know the number before you commit to anything larger. We are happy to talk figures on the first call.
You do. We hand over the repositories, infrastructure code and documentation, and we avoid building on anything that would tie you to us.
No. We are based in Mayfair and work with teams across the UK and Europe, remotely or in person when that helps.
Thirty minutes and a shared screen. You will leave with our first three recommendations whether or not you hire us.
