Manual work in.
Finished work out.
Trod builds AI agents and automations that take repetitive work off your team, proven on your real data before anything ongoing.
Fixed scope, priced up frontProven on your dataNo long-term lock-in
No deck. A working system.
The first deliverable is a working system running on a sample of your real work, measured against acceptance criteria you agreed to before the build started.
You watch the pipeline run on a sample of your own documents before committing to anything ongoing. The proof is not a testimonial. It is the working run.
The person in the discovery call is the person writing the code, presenting the measured results, and answering for the system once it is in production.
A fixed-scope pilot with a written pass/fail bar, done in weeks rather than quarters. Not a transformation engagement.
From invoice to balanced journal entry.
Invoices arrive as a PDF or an email. The system reads each one, codes it to the right job, cost code, and entity, checks it, and hands back a balanced journal entry ready to post. It is the fastest part of the back office to prove, which is why most engagements start here.
- A vision-capable language model extracts the vendor, amounts, dates, and line items as structured data, with a confidence score on every field.
- Each line is matched against your chart of accounts, open jobs, and prior entries.
- Validation runs duplicate detection, math checks, and a debit-equals-credit reconciliation.
- Anything low-confidence or failing a check is held back and routed to a person. Nothing posts without sign-off.
Built to take manual work off your team.
The invoice pipeline is the flagship. The same method applies to any manual, repetitive workflow in your operation: map the work, build on your real data, prove it against criteria agreed up front, then keep it running.
See services & how delivery works →Purpose-built Claude agents for document processing, report generation, data extraction, and the workflow tasks that have no off-the-shelf tool. Scope can be a single task or a multi-step workflow across several systems.
AI-assisted internal tools (dashboards, approval queues, review interfaces) built on top of your existing data and the agents already running.
For teams not ready to build yet. A clear roadmap of where AI fits your operations and what to prioritize. No commitment to a build required.
Four rungs. Each one de-risks the next.
Enter at any rung. The default path runs top to bottom, and you decide what happens at every step. Fees are fixed and quoted before anything starts.
A 30-minute call and a written assessment afterward: which manual processes are worth automating first, roughly what a build would take, and what it would save. No obligation either way.
One workflow, your real data, a fixed price and timeline, and acceptance criteria in writing before the build starts. The deliverable is a measured run on held-back work, not a demo.
The proven pilot hardened into a running service: wired into your actual stack, an exception queue with human sign-off, monitoring, an evaluation suite, and a runbook.
Keeps the system true as reality drifts: new vendors, new edge cases, source-system changes, model upgrades, and a monthly report of volume and accuracy.
The clean exit: if the pilot's proving run misses the acceptance criteria, you keep the written report and owe nothing further. The measured run is the deliverable, not a polished demo.
Questions worth asking first.
How accurate is it?
Modern language models are very good at reading messy, unstructured documents and turning them into clean structured data, which is the hard part. Accuracy comes from the system built around the model, not the model alone. Every field gets a confidence score, every output is checked against your data and a set of validation rules, and anything uncertain or failing a check is held back for a person instead of guessed at. The system is designed to catch its own errors before they reach you.
Which AI models does it use?
Trod builds primarily with Claude, with the specific model chosen per task to balance accuracy, speed, and cost, and it is not locked to one vendor as the field moves. The model is one component. The agents, validation rules, and integrations built around it are what turn a capable model into a system you can run in production.
Does this replace your team?
No. The agents handle the repetitive keying and matching. Your team reviews, approves, and makes the judgment calls. People spend their time on the work that actually needs a person, not on data entry.
What does a custom project look like?
It starts the same way as the invoice pipeline: a fixed-scope pilot on your real data, with acceptance criteria agreed in writing before the build starts, so you see measured results before committing to anything ongoing. Scope can be a single task or a multi-step workflow. If it is manual and repetitive, it is worth a look.
What happens to your data?
Your data is used to run your automations and nothing else. It is never used to train public models. How it is stored and who can access it is agreed during scoping, in writing.
Which tools does it work with?
The agents wire into the systems already in use rather than replacing them. The invoice pipeline runs against common accounting stacks today, and for custom work the integration is part of the scoped build.
How fast can it be running?
The pilot is kept small so it proves out fast, usually weeks rather than quarters. Once proven on your data, it moves to a maintenance retainer and keeps running.
Tell us what's eating your team's time.
Send a few details about the manual work you want off your plate. Trod will follow up to set up a walkthrough on your own data.
Not sure where to start? Ask about a free AI Systems Audit. Either way, start by telling us what takes the most time.