AI Solutions

AI solutions for financial services that show their working

We build AI agents and LLM automation for brokers, funds, prop firms and trading educators, grounded in your data, constrained by your rules and logged end to end.

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You own the codeWhite-label availableWorking build from the first fortnight

Overview

Large language models are good at reading, summarising and drafting. They are poor at arithmetic, prone to confident errors and know nothing about your compliance policy unless you tell them. Useful AI for finance is therefore mostly engineering around the model: retrieval from approved sources, deterministic tools for anything numeric, structured outputs that can be validated, and a log of every prompt and response. That is how Aurion Labs builds it. Our AI equity research engine, for instance, sets three analyst agents with opposing mandates against a five-factor score computed in code, not by the model.

We work on four kinds of problem. AI agents for finance that handle support, onboarding or internal queries over a controlled knowledge base. LLM automation that reads filings, emails and documents and turns them into structured records. AI equity research that drafts coverage from SEC EDGAR data and market prices. And AI for trading operations, from desk assistants that explain a position's Greeks to tools that suggest delta-neutral rebalancing for a human to approve. Where data is the bottleneck, our data engineering team builds the pipeline first.

What we deliver

Retrieval over approved documents with source citations
Tool calls for prices, positions and maths, not model arithmetic
Structured JSON outputs validated against schemas before use
Prompt, response and tool-call logging for audit and review
PII redaction before data reaches third-party model APIs
Confidence thresholds that route uncertain cases to humans
Model-agnostic design with Anthropic Claude as a primary option
Cost controls through caching, batching and per-workflow budgets
Regression evals before every prompt or model change

What we build

AI Solutions, built for production.

AI agents for finance

Support and operations agents on Telegram, web chat or internal tools, with memory, a curated knowledge base, escalation to a human and guardrails that stop the agent straying into advice or account actions.

AgentsGuardrails

LLM document automation

Pipelines that extract structured data from filings, statements, KYC documents and emails, validate it against schemas and business rules, and route anything uncertain to a reviewer instead of guessing.

ExtractionHuman review

AI equity research

Automated research pages for stocks and ETFs built from SEC EDGAR filings and market data, with several analyst agents arguing opposing cases and every quantitative score calculated in code.

SEC EDGARMulti-agent

AI for trading operations

Desk assistants that explain positions, summarise risk and propose hedges such as delta-neutral rebalancing, always as suggestions a trader approves and never as orders placed without explicit sign-off.

Desk assistantHuman-in-the-loop

Workflow orchestration

n8n and custom Python workflows that connect models to your CRM, ticketing, Telegram, email and databases, with retries, rate limiting and alerts when a step fails or a model call times out.

n8nIntegrations

Evaluation and monitoring

Test sets built from your real queries, automated scoring of accuracy and refusal behaviour, regression checks before prompt or model changes, and dashboards tracking cost, latency and failure rates in production.

EvalsObservability

Approach

How we deliver it.

Pick a measurable task

We start with one workflow where success can be measured, such as tickets resolved without escalation or documents extracted correctly, and collect real examples from your business to form an evaluation set.

Prototype against the evals

We build a working prototype and score it against those examples, comparing prompts, retrieval strategies and models. Decisions rest on measured accuracy and cost, not on a convincing demo.

Add controls and integrations

Guardrails, human review queues, logging, redaction and permissions go in before anything touches customers or live systems, alongside the integrations with your CRM, data stores and messaging channels.

Launch, monitor, improve

We release to a limited group first, watch accuracy, cost and failure logs, then widen access. New failure cases feed back into the evaluation set, so the system improves without quietly regressing.

From the systems index

Built by us, not borrowed.

Relevant systems from the 30 we have engineered. Product and client names are withheld.

Browse the full index

AI equity research engine

Data and AI

Daily research pages for S&P 500 stocks and ETFs, written by three AI analysts with opposing mandates and backed by a five-factor composite score.

Next.jsFastAPIAnthropic APISEC EDGARPostgres

R&D

AI support agent for a prop firm

Data and AI

A Telegram assistant with memory and a knowledge base, orchestrated through n8n.

n8nTelegramLLM

Client

Windows options terminal

Trading platforms

A .NET desktop terminal with Black-Scholes and Black-76 risk graphs, stress tests, hedge suggestions, a trade journal and an AI assistant for delta-neutral rebalancing.~31k lines of C#

C#WPF.NET 8IBKR APISkiaSharp

Built

Indicator access manager

Commerce and fintech web

Admin console that grants and revokes invite-only TradingView indicator access, tracks member retention and runs a Telegram bot.

FastAPIPostgresHTMXCloudflare

Live

LiveIn production and operated by usBuiltComplete and shippedClientDelivered for a clientR&DWorking build, still in development

Why Aurion Labs

The difference is operating experience.

AI inside real trading software

Our Windows options terminal includes an AI assistant for delta-neutral rebalancing, and our research engine publishes AI-written analysis daily. We put AI into systems that sit next to real positions, so we design for failure first.

Engineering around the model

Most of the value lies in retrieval, validation, tooling and monitoring rather than the prompt. We bring full-stack and DevOps engineering, so the AI component ships as a maintained service, not a fragile script.

Built to be reviewed

Every output can be traced to its inputs, sources and model version. That makes the system easier for your compliance and risk teams to assess, and far easier for us to debug when something looks wrong.

Technology we work with

Anthropic Claude APIPythonFastAPIn8nTelegramNext.jsPostgresSupabaseSEC EDGARDockerCloudflareVercel

FAQ

AI Solutions questions, answered.

Still deciding? A 30-minute call usually answers the rest.

What does an AI solution for a financial firm cost?

Cost depends on the number of workflows, the integrations required, the volume of documents or conversations processed, and the level of review and logging your firm needs. Model usage is a running cost on top of the build, and it varies with volume and model choice. We estimate both during scoping, and we design for cost control from the outset with caching and model routing.

How long before we have something working?

A narrowly defined use case can reach a measurable prototype quickly, because we test against your real examples from the start. Production rollout takes longer, since controls, integrations, security review and a staged release all need doing properly. We agree the phases, and the success measure for each one, before committing to the full build.

Who owns the AI system and our data?

You own the code we write on full payment, unless the contract sets out a different arrangement. Your data remains yours. We configure model providers so that, where their terms allow, your inputs are not used for training, and we can keep data inside your own cloud account. Prompts, evaluation sets and configuration are handed over alongside the code.

Which AI models and platforms do you use?

We commonly build on the Anthropic Claude API, and we design systems so the model layer can be swapped or mixed if requirements change. Orchestration runs in Python, FastAPI or n8n, with Postgres or Supabase for storage and vector search. Final choices are driven by your security requirements, the task itself and measured results on your evaluation set.

Is AI-generated output compliant with financial regulation?

Compliance depends on how you use the output, and that responsibility stays with your firm. We are a software company, not a regulated adviser. What we provide is the engineering that makes compliance workable: guardrails that block advice-like answers where you need them to, human approval steps, full logs and documented model behaviour that your compliance team can review.

How do you keep the system accurate over time?

Providers update their models, your products change and new edge cases appear. We maintain an evaluation set, rerun it before any change, and monitor live outputs for drift and failures. Maintenance agreements cover these checks, knowledge base updates, provider changes and new workflows as your team finds further uses for the system.

Related services

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Prefer email or phone? hello@aurionlabs.io · +44 7832 617626