Services / AI agents and internal tools
Language models wired into your real systems, with guardrails.
A chat window on the side of your business does not change much. An assistant connected to your ticketing system, your documents and your database does.
We build these with clear boundaries on what the model may do, a log of every action it takes, and a person in the loop wherever the cost of a mistake is high. Before you commit, we run a short feasibility test on your real data.
What this looks like in practice
Representative builds for this service. Yours will be scoped around your tools and your process.
Support triage
Incoming tickets are classified, tagged with the likely product area, and given a drafted reply that a person approves in one click.
Built with Claude, Zendesk, n8n.
Document extraction
Contracts, invoices and forms become structured records with a confidence score, flagged for review when the model is unsure.
Built with Claude, Python, Postgres.
Internal knowledge assistant
Staff ask questions in Slack and get answers grounded in your handbook, wiki and past tickets, with sources cited.
Built with Claude, pgvector, Slack.
How an engagement works
- 1
Discovery call
30 minutes
You describe the problem. We ask questions and tell you honestly whether it is worth automating or building. No slides.
- 2
Scope and fixed quote
Within a few days
A short written proposal: what we will build, what we will not, the price and the timeline. Fixed, not hourly.
- 3
Build in weekly demos
Weeks one onward
You see working software on a preview link every week and steer it. Nothing is a surprise at the end.
- 4
Test and hand over
Final week
We test it properly, deploy to your accounts, write the runbook and walk your team through it on a recorded call.
- 5
30 days of support
After launch
Anything that breaks or was not as agreed, we fix at no charge. After that, a small retainer or you are self-sufficient. Your call.
Questions about ai agents and internal tools
Which models do you use?
Usually Claude through the Anthropic API. Sometimes OpenAI or an open model if the task or your data policy calls for it. We choose per task, and the code is not tied to one vendor.
Is our data used to train anything?
No. We use APIs that do not train on your data, and we can run open models inside your own infrastructure if data cannot leave it.
How do you stop it making things up?
Ground answers in retrieved documents, cite sources, restrict which tools it can call, and measure accuracy against a test set before launch. Where a wrong answer is expensive, a person approves before anything is sent.
Also see: Workflow automation, App development, QA and test automation.
Have something that should run itself?
Tell us what keeps eating your team’s week. We’ll say honestly whether it’s worth fixing and what it would take.
Or email hello@zerotouchlabs.com. We reply within one business day.