The Canvas, On Real Data
Wire agents to your tools, watch each step run, and open up the reasoning behind any result.
Build It Visually. Take the Code With You.
Many visual platforms require workflows to run within their proprietary runtime. LumiMerse lets you export compatible pipelines as Python, use your own model keys, and retain an inspectable execution history.
Common platform constraints
- Workflows run inside the vendor’s own runtime
- Portability depends on their export format, where one exists
- The output is usually data handed off to another system
- How much of the reasoning you can inspect varies by tool
- Model access is often routed and billed through the platform
LumiMerse
- Export compatible pipelines as Python and run them yourself
- Deploy that code wherever you like — it does not need us to run
- Ship an application, with charts your client can explore
- Open any run and see what each agent received and decided
- Bring your own model key — we do not mark up inference
Build visually because it is faster. Keep the code because your client will want to know what happens if you walk away.
Three Jobs a Chat Window Alone Doesn't Finish
Assistants can reach external tools in some setups. What a chat session does not do is run unattended for months, accumulate state across every previous run, or hand back a finished artefact. All three below are real builds — open them on the Use Cases page.
Property tax protest agent
Finds comparable homes, runs the statistics to prove an over-appraisal, maps the evidence — then fills in the official Texas protest form. It does not stop at the analysis and leave you the paperwork.
Outcome: a completed, ready-to-submit form — not a summary of what you could do next.
Daily market data screening
Screen the market every morning before anyone is awake, apply your criteria, and append the results to a dataset that compounds day after day. It runs on a schedule, keeps every previous run, and tells you when something looks wrong.
Outcome: a research dataset that builds itself while you sleep.
Document review at scale
Run one extraction across a whole folder of contracts, reports or applications. The same fields pulled from every file, processed in parallel, with the exceptions ranked and flagged for a person to look at.
Outcome: a consistent, traceable pass over a stack nobody had time to read.
Built for People Who Build for Others
The people turning messy, judgement-heavy work into something repeatable — usually on someone else's behalf.
AI & automation consultancies
Ship a working prototype in the first week of an engagement instead of the last, and turn each build into a template you can sell again.
Learn MoreIndependent AI builders
One person, real client applications. Build visually, drop into Python when you need to, and deliver something you can hand over and support.
Learn MoreSME innovation teams
Automate the judgement-heavy work that never justified a data-science hire — without waiting on a roadmap or a vendor.
Learn MoreFrom Raw Data to Decisions You Can Defend
Connect Your Data
Link documents, spreadsheets, databases, Python tools and APIs. Point it at whatever the client already has, in whatever shape it arrives.
Build Reasoning Agents
Use the visual canvas to wire together specialist agents — for data preparation, extraction, analysis, ranking, visualisation and reporting.
Validate & Deploy
Inspect every agent step, assumption and output. Add human approval gates where judgement matters. Then deploy it as something your client can use.
- Runs your real tools
- Quantifies uncertainty
- Checks the constraints
- Human approval gate
Start From a Working Application
You don't begin at an empty canvas. Pick a template, run it on sample data, then swap in your own. Build your own private templates once you have something worth repeating — and deploy them into the next client's workspace instead of starting over.
Browse Templates- Document Review Application
- Data Analysis & Visualisation Agent
- Scenario Comparison Application
Working With
Grid compliance work used to mean weeks of manual assembly just to produce something defensible. LumiMerse cut that down and kept the audit trail intact. That’s the part that actually matters when the regulator asks questions.
Your Keys, Your Logic, Your Audit Trail
You choose the model, you keep the keys, and every step of every run stays inspectable — so you can show a client exactly how an answer was reached.
Bring your own model
Point agents at your own model API keys — OpenAI, Anthropic, Azure or a local model. You control which model runs and what it costs.
Inspect every step
Open any run and see what each agent received, decided and passed on. Run history is kept so you can go back to a result weeks later.
It runs without us
An exported pipeline runs on your own infrastructure with no dependency on LumiMerse. That is the answer when a client asks what happens to their system if your subscription lapses.
Dedicated deployment
Private deployment options are available through an assisted enterprise engagement — talk to us about what your environment requires.