AI Automation
AI agents that finish the task, not just answer a question
An AI agent is software that works toward a goal in several steps: it looks things up, uses your tools, checks its results and hands off to a person when it should. We build agents for specific, bounded jobs, with clear limits on what they can do.
What we build
What's included
Back-office agents
Agents that gather information from several systems, prepare a record or a draft, and route it for approval.
Research and enrichment
Agents that look up companies, contacts or products and fill in what your team would otherwise search for by hand.
Follow-up and scheduling
Agents that keep track of open items and send the next message or reminder at the right time.
Tool-using assistants
Assistants connected to your data and actions, so a request like "draft a reply to this lead" is done end to end.
How we keep agents safe
- Each agent gets the narrowest permissions that do the job
- Actions that matter wait for a person to approve them
- Every step is logged, so you can see what the agent did and why
- Spending limits on AI usage, per agent and per month
How an agent works
What is inside an AI agent
An agent is a loop: it reads a goal, decides the next step, uses a tool, checks the result, and repeats until the goal is met or it needs a person. The quality comes from the tools and limits around the model, not from the model alone.
A clear goal and a stop rule
Agents work best on bounded jobs: "prepare the renewal file for this client", not "run the business". Every agent has an explicit end state.
Tools, not free access
Each agent gets specific tools: look up a record, draft an email, create a task. It cannot do anything we did not give it a tool for.
Memory that is yours
Agents read from your systems and a knowledge base you control, so they work with facts on record rather than guesses.
A human handoff
When an agent reaches a step that needs judgment, it stops, summarizes what it found, and hands over.
Where agents earn their keep
Agents we build for small and mid-sized businesses
These are the agent types that have paid off in practice. Each one is a bounded job with a clear output.
A good first agent
- Has one output a person already produces by hand
- Needs information from two or more systems
- Runs on a trigger or a schedule, not on a conversation
- Can be checked by a person in under a minute
Lead research agent
Takes a new enquiry, looks up the company and contact, drafts a reply in your voice, and queues it for approval.
Back-office preparer
Collects what a renewal, onboarding or claim needs from several systems and assembles the file.
Follow-up agent
Tracks open items, sends the next reminder at the right time, and escalates when something stalls.
Data enrichment agent
Fills gaps in your CRM or catalog from public sources, with a source noted on every value.
Honest limits
What agents are not good at yet
We tell clients where the technology falls short before they commit. Agents are not a fit for everything.
- 01Open-ended goals without a clear done state
- 02Tasks where a single wrong action is expensive and hard to undo, unless a person approves each one
- 03Work that needs context only in someone's head, until that context is written down
- 04Anything that must be right every time without review
How it works
From first conversation to running system
You only commit to the next phase once the last one has paid off.
- 1
Audit
FreeA short written review of where your time goes and what to automate first.
- 2
Pilot
Fixed scopeOne workflow, built on your real data, running in production. You see results before committing to more.
- 3
Build
Per phaseWe extend what worked across the process, with logging and a person in the loop.
- 4
Run
MonthlyWe monitor, fix and improve what we built, and report what it handled.
Start here
Find out what to automate first, for free
You tell us
- What your business does
- Which tasks take the most time
- The tools you use today
You get back
- 3 to 5 concrete automation opportunities
- A rough effort and payoff for each
- Which one to pilot first, and why
Free. Reply within one business day. No obligation.
Tools
What we work with
We choose tools to fit your systems and security needs, not the other way around.
- Claude
- OpenAI
- Azure AI Foundry
- Model Context Protocol
- n8n
- Power Automate
Work
Related work
FAQ
AI Agents: common questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent completes a task in steps, using your systems, and can run without anyone chatting with it.
Can an agent make mistakes?
Yes, which is why we design them with limits: narrow permissions, approval for actions that matter, and logs of every step.
Where does the agent run?
In your cloud account or in ours, depending on your data and security needs. Microsoft-centered businesses often keep it inside Azure.
Find out what you could automate
Tell us about the work that takes your team the most time. We come back with concrete opportunities and a rough effort for each. No cost, no obligation.