Accounts payable
Invoices read from email, matched to orders and drafted as entries for approval.
AI agents & automation
Hand off the busywork.
Keep the final say.
AI agents read, decide and act across your tools to finish multi-step tasks, such as taking an invoice from an email all the way to your accounting system. We build them with clear limits, a person’s approval where it matters, and a log of every step.
- Invoice arrivesBy email, as a PDF
- ReadSupplier, amounts, PO number
- CheckMatched against the order in your ERP
- PrepareAccounting entry drafted
- ApproveA person confirms in one click
- DonePosted, filed and logged
What an agent is
A chatbot answers.
An agent gets it done.
An agent is a language model given a goal, a set of tools and rules. It works out the steps, uses your systems to carry them out and hands anything uncertain to a person.
Works across tools
Email, documents, CRM, ERP and spreadsheets, connected through MCP and APIs.
People stay in charge
You decide what it may do alone and what needs approval.
Every step on record
What it read, what it decided and why, kept for review.
Use cases
Good candidates
are everywhere.
High-volume work that needs a little judgment and arrives as email, PDFs or forms. These are examples, not client results.
Inbox triage
Incoming email classified, routed to the right person and answered in draft.
Sales operations
Inbound leads qualified, enriched, added to the CRM and followed up in draft.
Weekly reporting
Figures pulled from several systems and written up as the report your managers read.
Customer onboarding
Documents collected, checked for completeness and accounts created.
IT operations
Alerts triaged, diagnostics gathered and tickets opened with the context attached.
How it works
Autonomy is earned, step by step.
An agent starts by proposing, not acting. It gets more freedom only where its record shows it deserves it.
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Pick the process
Frequent, rule-based with some judgment, and digital from the start. We measure the time it takes today, so the gain can be measured later.
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Set the guardrails
What the agent may do alone, what needs a person’s approval, what it must never do, and spending and rate limits.
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Build and test on past cases
We replay real historical cases and measure how often the agent gets each step right before it touches live work.
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Run in shadow mode
The agent proposes, people decide. Each type of action is switched to automatic only once its accuracy is proven.
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Monitor and improve
Logs, exceptions, cost per task and accuracy over time, reviewed with you.
Agents or classic automation
Use each
where it’s strongest.
Where every step is predictable, plain automation is cheaper and more reliable. We use both, often in the same workflow.
| Criterion | AI agent | Classic automation |
|---|---|---|
| Inputs | Unstructured: emails, PDFs, free text | Fixed formats and fields |
| When things vary | Adapts, or asks a person | Breaks until someone updates the rules |
| Predictability | Needs guardrails, testing and monitoring | Same input, same result |
| Cost per run | Model usage on each run | Close to zero |
What you get
An agent your team can trust.
- The agentConnected to your email, documents and business systems.
- Approval inboxWhere people review and confirm proposed actions.
- Test resultsAccuracy per step, measured on your past cases.
- Logs & dashboardEvery run, decision and exception, searchable.
- Guardrail settingsWhat runs automatically, what waits for approval, and the limits.
- RunbookHow to handle exceptions, change rules and pause the agent.
Technology
Chosen per workflow.
Agent frameworks
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- CrewAI
Workflow & scheduling
- n8n
- Temporal
Tools
- MCP servers
- REST APIs
- Email & document parsing
Models
- Open-source on your hardware
- Claude, GPT or Gemini via API
Observability
- Langfuse
- OpenTelemetry
When an agent isn’t
the right tool.
- Every step is predictable. Classic automation is cheaper and never improvises.
- The task happens a few times a month. The build cost won’t pay back.
- A mistake would be costly and hard to undo, and there is no room for a review step. Keep a person doing it, with AI assisting.
How we work together
Start small.
Decide on facts.
Every project goes through the same three stages. You decide at the end of each one whether to continue, and you can stop after any stage and keep everything built so far.
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Stage 1
Assessment
We look at your process, your data and your systems, then tell you plainly whether AI is worth it, which approach fits, and what it would cost to build and run.
You get a written recommendation with scope, timeline and cost.
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Stage 2
Pilot
A working version on your real data, used by a few of your people, with results measured against questions and cases from your own business.
You get a working pilot and an evaluation report.
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Stage 3
Production & support
We secure it, connect it to your systems, roll it out to your team and keep it running: monitoring, model updates and improvements.
You get a production system, documentation and ongoing support.
Yours to keep
- Source code, in your own repository
- Prompts, configuration and infrastructure setup
- Test sets and evaluation reports
- Fine-tuned model weights, if any
- Documentation and runbooks
Not included in our fees
- Hardware, if you buy or rent GPUs
- Cloud hosting
- AI model usage, if you use a cloud API
- Third-party software licences
We list them in the assessment, so the full cost is clear before you commit.
Questions
Will the agent act without anyone checking?
Only where you allow it. By default, anything that sends, pays, deletes or changes records waits for a person. Actions move to automatic one type at a time, once their measured accuracy justifies it.
What happens when it gets something wrong?
Uncertain cases go to a person instead of being guessed. Every step is logged, so mistakes can be traced and corrected, and the case is added to the test set so it doesn’t recur.
Which processes make good first agents?
Ones that are frequent, take real staff time, arrive as email or documents and follow rules with some judgment, such as invoices, inbound requests, lead handling and recurring reports.
Can agents run on a private, local model?
Yes. Agents can use an open-source model on your hardware, a cloud model, or both, with sensitive steps kept local.
What does an agent cost to run?
Mostly model usage per task, plus hosting. We measure the cost per task during the pilot, so you can compare it with the time it saves before deciding to continue.
Other AI solutions
Start with
a conversation.
No call needed to start. Send a short brief and we’ll reply in writing with what we’d do and what it would take.
Houston, Texas · Working with clients worldwide