Four ways the work gets automated.
Trod is an AI automation consultancy, building custom AI agents, workflow automation, and internal tools that take repetitive, manual work off your team, in any industry. The flagship is an AP-to-journal-entry pipeline that takes invoices in and returns balanced journal entries, proven on your real invoices in a fixed-scope pilot before any retainer. It is the clearest proof of the method, and the same method applies to whatever your team still does by hand: reading documents, moving data between systems, or rebuilding the same report every month.
One thing is different from a typical consultancy: there is no handoff between strategy and execution. The person in the discovery call is the person writing the code, presenting the results, and answering for the system once it is running. There is no deck. The first real deliverable is a working system running on a sample of your actual work, measured against acceptance criteria you agreed to before the build started.
AP & Invoice Automation
Start here. Invoices arrive as a PDF or email, get read and coded to the right job, cost code, and entity, checked against the chart of accounts, and handed back as a balanced journal entry ready to post. Low-confidence lines get flagged for a person instead of guessed at. Nothing posts without sign-off.
Engagement: a fixed-scope pilot on a month of real invoices, proving the pipeline on the actual stack before any ongoing commitment. Once proven, it moves to a maintenance retainer.
Custom Agents & Automations
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 that spans several systems. Each agent is built for a specific job, reading a defined kind of document, producing a defined kind of output, following your rules, and wired into the tools already in use rather than replacing them.
Engagement: scoped to the specific automation. A project fee covers build and rollout; a retainer covers upkeep as source systems and edge cases change.
Internal Portal Layer
Internal tools (dashboards, approval queues, review interfaces) built on top of existing data and the agents already running. Designed for whoever is doing the work day to day: the person reviewing a flagged invoice, approving a batch, or checking a report before it goes out.
Engagement: scoped to the workflow that needs an interface. Usually follows once an automation or agent is already producing data worth a proper front end.
AI Strategy & Advisory
For teams not ready to build yet. A scoped review of where AI actually fits the operation: which manual processes are worth automating first, what a realistic build would take, and what to prioritize, sized to the scope of the review. No commitment to a build required.
How delivery works: Map, Build, Prove, Run
Every engagement follows the same four phases, whatever the service. The point of the method is that you see measured results on your own data before committing to anything ongoing.
A discovery session walks the actual workflow: what comes in, what goes out, which systems it touches, how many hours it eats. One workflow is picked, and the scope goes in writing: inputs, outputs, data-handling terms, a fixed price, and acceptance criteria that define what working means. Not ready to scope a build? Start with a free AI Systems Audit and get a written assessment instead.
The pipeline is built against your sample data, with part of it held back and left untouched for the proving run. Every extracted field carries a confidence score, every validation rule is written down, and exceptions route to a person by design. A plain-language ship note lands every week: what runs now, what is next, what is blocked.
The held-back data, which the system has never seen, runs through with no fixes along the way. Results are scored against the acceptance criteria and delivered as a written report and a live walkthrough: accuracy by field, what was flagged, what was caught, what was missed. Hit the bar and the pilot moves to production.
The proven pilot becomes a monitored service: wired into your real intake, deployed securely, logged on every run, and alarmed when something drifts. The evaluation set becomes a regression suite that every change must pass. A maintenance retainer covers monitoring, new edge cases, and a monthly report of volume, accuracy, and changes.
The clean exit: if the proving run misses the acceptance criteria, you keep the report and owe nothing further. A polished demo is not the deliverable; the measured run is.
What every build includes
These are not add-ons. A system without them is a demo, and demos are not what Trod delivers.
- Acceptance criteria agreed in writing before the build starts.
- Confidence scores on every extracted field, and validation rules you can read.
- Human sign-off built in: uncertain items route to your team, and nothing posts without a person approving it.
- Evaluation & regression suite delivered to you, so the accuracy numbers are reproducible and future changes are tested.
- Monitoring & alerting that catch failures and drift before you do.
- A runbook covering what the system does, how to handle each exception, and what unusual looks like.
The stack
Where to start
Many engagements start with AP & Invoice Automation because it is the fastest to prove on a month of real invoices. But the right starting point is whatever has the most manual work in it today, whether that is an invoice stack or something specific to your operation.
Start with a free AI Systems Audit.
Tell Trod what eats your team's time and get a written assessment back: whether it's automatable, what it would take, and what it would save. No obligation either way.
Request an audit →