Service 01 · AI & document intelligence
Practical AI that reads, routes and answers, inside your systems
Most business information still arrives as text. AI can read it, pull out what matters, draft the next step and answer questions about your own data. We use it where it does real work, and connect it to the systems you already run.
- Email, PDF or form arrives
- AI extracts the fields: customer, items, dates, amountsException: Low confidence: a person checks it first
- Rules route it to the right queue or person
- Record created or updated in your system
- Reply drafted for a person to approve
Use cases
What AI can actually do in your business.
Concrete jobs, not a chatbot on the side. Each one connects to the systems your team already works in.
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01
Turn emails into structured work
Requests, orders and supplier messages read, the key fields extracted, and a record or task created in your system.
Inbox → AI extraction → record and task
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02
Extract data from documents
Invoices, purchase orders, inspection sheets and forms in different layouts, read into clean fields, with uncertain values sent for review.
PDF → fields → review queue → system
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03
Route requests by what they say
Messages and tickets sorted by topic, urgency and customer, and sent to the right person or queue.
Message → classification → the right queue
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04
Draft replies and documents
First drafts of quotes, follow-ups and summaries, written from your records for a person to check and send.
Record → draft → human approval
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05
Answer questions about your own knowledge
A secure assistant that answers from your procedures, specifications and past jobs, and shows where each answer came from.
Question → your documents → sourced answer
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06
Summarize large volumes
Long threads, reports and notes condensed into what changed, what is late and what needs a decision.
Many documents → one briefing
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07
Generate reports from operational data
Plain-language summaries of the week’s numbers, written from the dashboard data itself.
Database → narrative report
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08
Agents for defined workflows
Software that carries out a clearly bounded multi-step task (gather, check, file, notify) and hands anything unusual to a person.
Trigger → steps with checks → hand-off
An example
From an email to updated records, in one pass.
The kind of document every business receives. AI reads it, your rules decide what happens, and a person approves anything customer-facing.
Arrives as text
Supplier · Order update
Hi, confirming your order of 3 rolls of the charcoal performance fabric (54").
Two rolls ship Thursday; the third is backordered about two weeks.
Invoice 4471 to follow. Please quote the job reference on payment.
Becomes structured work
- Type
- Order confirmation
- Item
- Charcoal performance fabric, 54"
- Quantity
- 3 rolls
- Shipping
- 2 rolls Thursday, 1 backordered (~2 weeks)
- Invoice
- 4471 (to follow)
- AutomaticMaterial record updated
- AutomaticJob flagged: waiting for material
- A person approvesCustomer delay message drafted
How we use AI
A powerful tool, used with engineering discipline.
AI is the right answer less often than the hype suggests, and more often than most businesses assume. These are the rules we build to.
- Rules first
- If a printed code, a layout or a keyword decides it, plain rules are cheaper and fully predictable. AI is for what rules can’t handle.
- A person checks what matters
- Low-confidence results go to a review queue. Nothing important is posted or sent unchecked.
- Your data stays bounded
- Which data may be processed, where and by which service is agreed in writing before anything is built.
- Measured on your documents
- Accuracy is tested on your own documents before go-live, and reported, not assumed.
- Connected, not bolted on
- AI is useful when it reads from and writes to your systems. A chatbot on the side rarely changes the work.
Why the system matters more
AI is only as useful as what it connects to.
An assistant that can read an email is interesting. One that creates the record, flags the job and drafts the customer update in the system your team already uses changes the work.
The work listed here is not AI. It is the kind of system AI connects to: structured records, automated flows and rule-based document handling.
- Case studySofa Art UpholsteryOne record from website inquiry to paid invoice: the kind of system AI plugs into
- Field note 04Payroll & document automation (rule-based)2 h → ~5 min payroll file sorting, every two days
- Field note 01Digital timesheets60,000+ timesheets processed, over more than two years
Questions
Straight answers about AI.
Do we need AI?
Maybe not. Many of the biggest wins are plain automation and a better process. We recommend AI where it does something rules can’t, and say so when it doesn’t.
Is our data used to train someone else’s model?
We use business accounts of approved services that do not train on your data, and which data may be processed is agreed in writing first.
What happens when the AI is wrong?
The design assumes it sometimes will be. Confidence thresholds send uncertain results to a person, and every automated action is logged.
Can AI work with our existing systems?
Yes, and it should. The value comes from reading your documents and writing structured results into the systems you already run.
Have you built AI systems for clients?
RootN’s published work so far is automation, business systems and analytics, shown on the Work page. AI projects are scoped the same way: a measured baseline, a fixed price and a review step.
Next step
Bring one document-heavy process.
Show us the emails, PDFs or forms your team reads and re-types. In 30 minutes we’ll tell you whether AI, plain automation or neither is the right fix.
With the engineer who would do the work. Reply within one business day.