Read incoming documents
Purchase orders, bills of lading, and invoices arrive as PDFs and scans. AI reads the fields into your system and marks the ones it can't read with certainty. Your team checks those, then creates the record.
Services / AI Integration
AI built into the work your team actually does.
AI pilots often stall between the demo and daily work. We find the tasks where AI clearly removes work — documents, customer questions, quotes, internal knowledge — build it into the systems your team already uses, and keep a person accountable for every result that matters.
Sound familiar?
Signs AI could take work off your team
TodayWith AI built into your tools
Your team retypes data from PDFs and emails into your systems.
Fields are read from the document, and your team checks the ones marked as uncertain.
The same customer questions get answered by hand, every day.
Replies are drafted from your own records and policies, with sources, for your team to send.
Answers live in a few people's heads or in scattered files.
Anyone on the team can ask in plain language and see which document the answer comes from.
An AI pilot impressed in a demo but never made it into daily work.
AI runs inside the tools your team already opens every morning.
No one can say where your company data would go.
Every connected system, permission, and retention period is written down.
Where AI helps established businesses today
Four kinds of work where AI can take real effort off a team. The screens are example designs for fictional client companies, each in its own existing software — not client projects.
Draft replies with sources
When a customer asks about a delivery date or a fee, AI drafts the reply from your order data, agreements, and policies and lists every source. Someone on your team reads it, edits it if needed, and clicks Send.
Turn requests into quote drafts
A quote request in an email becomes a draft with the details from the email and prices from your own price lists. Anything the email doesn't say is flagged as a question. A person approves the quote before it goes out.
Answer questions from your own documents
Your team asks in plain language and gets an answer that cites the documents it comes from. Answers only draw on documents the person asking is allowed to open.
Review and approval
Nothing goes out without approval.
AI prepares the work. A person decides what goes out — you set which actions need approval, and drafts wait until someone on your team approves them.
A review queue
Every draft waits in one place, with what it's based on, until someone approves, edits, or rejects it.
Approval rules per action
You decide which actions need a person's approval, and whose — sending an email, changing a price, or sending a quote.
An audit log
Who prepared, changed, and approved each result, and when.
A working example
Try one we built: talk to Thally.
Thally, the voice assistant on this website, is an AI integration we built. It is set up to answer only from this website's content and to say so when something isn't covered here. Before a contact request goes out, it shows your details on screen, and nothing is sent until you confirm.
In the custom ERP systems we build, Thally works as a chat panel that prepares quotes, replies, and follow-ups for a person to confirm.
Your data
Clear data boundaries
Before anything is built, we write down which systems the AI connects to, what it may do there, and how long anything is kept.
Connected sources
The AI connects only to the systems a task needs, with read or draft access set per system.
No training on your data
Your data is not used to train the AI provider's models.
Models chosen per use case
We pick the model for each task. You're not locked in to one AI vendor.
Retention, written down
How long drafts, logs, and requests are kept — in your systems and at the AI provider — is documented before go-live.
How we approach AI projects
We leave AI out where conventional software does the job better, and say so.
Start with the work, not the model
We look at where your team's time actually goes before we suggest any AI. The first assessment gives you an honest list of where AI is worth it in your business — and where it isn't.
Test on your real cases
Each use case is tested on examples from your own business before it goes live, and you see the results.
Build it into your systems
AI goes into the tools your team already uses, including software from other vendors after a technical review — not into a separate app no one opens.
Hand over or operate on a retainer
You get documented code and a clean handover, or we keep running and improving it with you as your retained engineering partner.
What you can count on
Three commitments that apply to every engagement, whatever its size.
You own what we build.
The custom code, documentation, and intellectual property we create for you are yours. Open-source components stay under their original licenses.
Direct access, no middlemen.
No account managers, no hand-offs. You talk directly to the people who design and build your system.
A clean handover. No lock-in.
Every project ends with documented code, deployment, and operations — so your team, or any other vendor, can take it from there.
Is AI integration right for you?
A good fit if
Your team spends a lot of time on repetitive, text-heavy work — documents, emails, quotes, or questions — and you want people to stay in the loop on the results that matter.
Probably not a fit if
The task follows fixed rules. Conventional, rules-based software will usually do it better and more predictably, and we'll tell you so.
Questions about AI integration
Do we need our own data science team?
No. We handle strategy, architecture, and implementation end to end. Your team brings what no model has: how the work is actually done.
Which AI models do you use?
We choose the model for each use case, based on the task, its data, and its cost. You're not locked in to one AI vendor, and a use case can move to another model later.
Is our data used to train AI models?
No. Your data is not used to train the AI provider's models. Before anything is built, we document which systems the AI may access and how long data is kept.
Can you add AI to software we already have?
Yes, including software from other vendors. After a technical review, we tell you where AI can connect — and where it can't.
What happens when the AI is unsure?
We build it to show uncertainty instead of hiding it: unclear fields are marked for a person to check, drafts list their sources, and anything that goes to a customer waits for approval.
Who owns what you build?
You do. The custom code, documentation, and intellectual property we create for you are yours. Open-source components stay under their original licenses.