A chatbot tells you. An agent does it.
We build agents that read your documents, update your systems and carry work through multi-step processes. And that stop and ask whenever a real judgment call comes up.
Which job in your operation eats the most time and needs the least judgment?
Thirty minutes with the engineer who builds these agents. No slide deck.
Six jobs that do not need a person.
They still get one, because there has never been anything else to give them to.
- 01 Reading the same document type for the hundredth time this month
- 02 Copying what it said into the system that needs it
- 03 Writing the follow-up that always says roughly the same thing
- 04 Checking whether the thing you asked for has arrived yet
- 05 Moving a task to its next stage because nothing else will
- 06 Reconstructing what happened on a file after the fact
What goes into an XtractSol agent
Every agent starts from a proven pattern and gets shaped to your workflow, your systems and your rules. Start with one agent where the pain is sharpest, or build up to several working together.
Built for one real job
We learn how the work actually flows today, then design an agent that handles it end to end and knows when to ask a person for input. Not a generic template.
Reads like a careful colleague
Contracts, forms, statements and emails read for the details that matter, with anything odd flagged. Every field links back to its source, so checking the work takes seconds.
Carries the chain forward
Some jobs are a run of small steps that each depend on the last. The agent moves the task through its stages without somebody having to nudge it along.
You decide where it stops
The agent respects your sign-off points every time. It does the legwork and brings a clear recommendation, so you keep authority over the calls that carry weight.
Works in the tools you run
Connected to your systems of record, communication channels and everyday apps, so it can read and update information right where your team already works.
Limits, and a record of everything
Every agent ships with hard limits on what it can touch and a full log of what it did, so it stays inside the boundaries you set even as volume grows.
What an agent is allowed to touch
Most of the anxiety about agents is not about whether they can do the work. It is about what happens on the day one of them is confidently wrong.
So scope is a configuration, not a promise. Each agent gets three bands, agreed in writing before the build starts. The inner band is where it works unsupervised. The middle band is work it prepares and a person releases. The outer band it cannot reach at all, whatever it concludes it should do.
A wrong answer inside the inner band is a small correction. That is the whole point of drawing the line there.
- Authorise a payment or release funds
- Override a rule you set
- Reach a system it was not given
- Anything that leaves your organisation
- Any value carrying money or liability
- Moving a file to its next stage
- Read documents and extract fields
- Update records inside its scope
- Draft messages and summaries
The agent does the legwork. You keep the decisions that carry weight.
Automation should support your team's judgment, not sideline it. You set the boundaries and the agent respects them every run.
Read
The incoming document or request, with every field linked to source.
AgentCheck
What it found, measured against the rules you set.
AgentAct
Systems updated and follow-ups drafted, inside its permitted scope.
AgentStop
At a boundary or an edge case, it halts rather than guessing.
AgentApprove
The judgment call comes to a person, with the context attached.
Your teamRecord
Every action logged and tied back to what prompted it.
AgentThe three named causes are all management problems, not model problems
Gartner's forecast that more than 40% of agentic AI projects will be cancelled by the end of 2027 puts the blame in three places: escalating costs, unclear business value and inadequate risk controls. Model capability is not on that list. Neither is technical difficulty. What kills these projects is starting too broad, never defining what success looks like in numbers, and building something nobody can supervise.
So we do the opposite of a platform pitch. We pick one workflow, agree up front what it should save and how we will know, put hard limits on what the agent can touch, and log everything it does. If the audit says the workflow is not worth automating, we say so then, before you have committed a budget to it.
An agent that acts confidently on something it misread has not saved you work. It has moved the error somewhere harder to find.
An agent is only useful if it can reach your tools
We connect to your systems of record, your communication channels and the everyday apps your team already lives in, including in house software.
Integration depth varies by system and version. We confirm exactly what is supported for your setup during the workflow audit, including anything that cannot be connected.
Live in weeks, in clear stages
We do not drop a mystery box into your operation and hope it works. We learn how your team works first, then build where you can see it.
Discovery call
How the work flows today and where an agent would save the most time. A written summary follows.
Workflow audit
Real examples reviewed, and an honest estimate of what the agent should read, do and hand off.
Build and train
Tuned on your actual documents and cases, including the awkward ones that need a person.
Deploy and refine
Connected with your review checkpoints built in, then tracked against real workloads.
What is the difference between an AI agent and a chatbot?
A chatbot mostly responds to questions. An agent takes action: it reads documents, updates systems, completes multi-step tasks and knows when to bring in a person. A chatbot is something you talk to. An agent is something that gets work done for you.
Do the agents act on their own without any oversight?
Only as far as you want them to. You decide which steps an agent handles on its own and which ones need a human sign-off. For anything sensitive the agent prepares the work and a person makes the final call.
What happens when the agent gets something wrong?
Every action is logged and tied back to its source, so a mistake is findable rather than buried. Agents are built to stop and flag rather than guess, and the boundaries you set limit what a wrong answer can actually touch. During the build we tune against your real cases, including the ones that went badly.
Which models do you use, and will our data train them?
We select the model per workload rather than committing you to one provider, and we confirm the specifics during the workflow audit. Your data is never used to train any model. Where the data is sensitive we agree the handling and retention rules in writing before the build starts.
How long does it take to get an agent running?
After the free discovery call the workflow audit takes a couple of weeks, and a first working agent is usually live within a few weeks after that. You see progress at each stage rather than waiting on one big reveal.
What does it cost?
A build fee for the first agent plus a monthly fee to run and maintain it. You get a firm number after the workflow audit, before you commit to anything, and if the workflow is not worth automating we will tell you then.
Ready to get started?
Book a free discovery call and we will map how agentic AI can fit your workflows.