Automate
We wire AI into the pipeline you already run: intake, triage, data movement, document handling, reporting. The goal is a working system in production.
Automate · Research · Prototype · repeat
AI in your operations, built as a loop.
ARP17 is a consulting and engineering practice that integrates AI into automation pipelines. Every system we ship keeps running the cycle: automating the work, researching what the results say, and prototyping the next improvement.
the method
The cycle is the deliverable.
We sell the loop: a system that ships early, gets measured in production, and improves with every pass. Each cycle leaves your pipeline measurably better than the last.
Research
Once it runs, we measure it: where the model is wrong, where humans still step in, where the process itself is the bottleneck. Evidence, straight from your data.
Prototype
Findings become the next prototype: a better prompt, a new tool in the chain, a changed workflow. Small, testable, and reversible by design.
Repeat
The prototype that survives testing goes back into the automated pipeline, and the loop starts again. Never at v1. Always in an iterative adaptation process.
what we do
Consulting and engineering, in the same pair of hands.
We advise on where AI belongs in your process, then build and operate it. One team owns the recommendation and its consequences.
- Pipeline and process audits
- We map how work actually flows through your team and identify the steps where automation and AI pay for themselves first.
- LLM and API integration
- Model selection, prompt and tool design, and the plumbing that connects models to your systems of record.
- Workflow automation
- Queues, schedulers, webhooks, and orchestration that replace copy-paste work and brittle spreadsheet handoffs.
- Evaluation and monitoring
- Test sets, quality metrics, and alerting, so you always know how the system performs this week.
- Prototype sprints
- A focused build that answers one question: is this worth automating? You get a working prototype and a measured answer.
- Team enablement
- Documentation, handover sessions, and playbooks so your own people can run and extend what we build together.
how we work
Two-week cycles. Stop whenever you want.
Every engagement is a sequence of fixed-price cycles. Each one ends with something running and a decision about the next loop. You choose, cycle by cycle, how far to go.
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Days 1 to 2
Scope the cycle
We pick one process, agree on what "better" means in numbers, and define the smallest change worth shipping.
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Days 3 to 9
Build and measure
We automate, integrate, and instrument. You see progress in your own tools, on your own data, during the cycle.
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Day 10
Review and decide
We walk through what the data says, what we would loop on next, and you decide: another cycle, a pause, or done for now.
your data, your call
Privacy-aware processing, when you want it.
Some pipelines handle data that should stay close to home. For those, we run the same loop with a privacy-aware setup: models and data flows chosen so information stays exactly where you decide.
Optional
Local models
Open-weight models running on your own infrastructure. Prompts, documents, and outputs stay inside your network.
Optional
EU-hosted models
European model providers and EU-region endpoints of the major platforms, so processing stays within EU data boundaries.
Optional
GDPR-aligned pipelines
Data minimization, clear processing records, and DPA-friendly architecture designed in from the first cycle.
Same cycle, same pace. Just a tighter data boundary, whenever you choose it.
start the loop
Tell us about one process that eats your team's time.
Describe it in a few sentences. We will reply within two working days with a first-cycle proposal: what we would automate, what we would measure, and what it costs.
[email protected]common questions
Questions people ask before the first cycle.
Short answers to what teams usually want to know about working with ARP17.
Who can help us integrate AI into our processes?
ARP17 is a consulting and engineering practice that does exactly this. It finds where AI fits the workflows you already run, then builds and operates it, so one team owns the recommendation and its consequences. Tell us about one process at [email protected].
What does ARP17 actually do?
ARP17 advises where AI belongs in your process, then builds and runs it: pipeline and process audits, LLM and API integration, workflow automation, evaluation and monitoring, focused prototype sprints, and team enablement.
How are engagements structured, and what does it cost?
Every engagement is a sequence of fixed-price cycles of about two weeks. The price for each cycle is agreed up front when it is scoped, so there are no open-ended bills. You decide, cycle by cycle, how far to go, and can stop after any cycle.
How fast can we start seeing results?
A first cycle ships a working system in production in about two weeks. Describe one process that eats your team's time and we reply within two working days with a first-cycle proposal: what we would automate, what we would measure, and what it costs.
Can our sensitive data stay private?
Yes. For sensitive pipelines, ARP17 runs the same loop with an optional privacy-aware setup: local open-weight models on your own infrastructure, EU-hosted models and EU-region endpoints, and GDPR-aligned pipelines with data minimization and DPA-friendly architecture.
What kinds of processes are the best fit for automation?
Repetitive, high-volume steps with clear inputs and outputs are the strongest fit: intake, triage, data movement, document handling, and reporting. We start by mapping how work actually flows through your team and finding the steps where automation and AI pay for themselves first.