AI Integrations
Wiring AI into the systems you already run — document processing, drafting, data extraction, and automation, built with guardrails, logging, and human review where it counts.
[ 01 ] Overview
There's a wide gap between a chatbot demo and AI that's actually wired into how your business runs. Crossing it is software work — connecting models to your documents, your inbox, your database, and your workflows, with the guardrails that make the output trustworthy enough to act on. That's the work we do here, and it's engineering first, AI second.
The practical wins are rarely glamorous. A pipeline that reads incoming PDFs and files the data into your system instead of a person retyping it. Search that answers questions from your own manuals, contracts, and records instead of hoping someone remembers which folder to look in. Draft responses that start eighty percent done and get a human review before anything leaves the building. Automations that summarize, sort, and route the text work that used to eat entire afternoons.
Every integration ships with the boring parts that make it dependable: logging of what the model was asked and what it produced, human review steps wherever a wrong answer has a cost, evaluation against your real data before go-live, and a kill switch. You own the accounts, the API keys, and the code — if we disappear tomorrow, your integration doesn't.
What you'll get
- Document processing pipelines. PDFs, scans, and forms read, extracted, and filed into your systems automatically
- Search over your own files. ask questions in plain English, get answers drawn from your manuals, contracts, and records
- Drafting automation. first drafts of replies, quotes, summaries, and reports, queued for human review before anything sends
- API integration. Claude, OpenAI, or local models connected to the software you already run
- Guardrails and logging. every request and response recorded, review steps where errors would cost you, and a kill switch
- Evaluation before go-live. tested against your real documents and edge cases, not a polished demo set
- Full ownership. your accounts, your API keys, your code, documented so any developer can maintain it
Our process
- Scoping. the one or two workflows where automation pays for itself first
- Prototype. a working version against your real data within weeks, not quarters
- Evaluation. measured accuracy on your documents and edge cases, reviewed together
- Hardening. logging, review steps, error handling, and access controls for production
- Care. monitoring, model updates, and tuning as your documents and needs change
[ 02 ] Common questions
Which AI providers do you build on?
Claude and OpenAI cover most work, chosen per task — and where data can't leave your premises, local open-source models are a real option at a real capability cost we'll be upfront about. The integration is built so the model behind it can be swapped as the market shifts, because it will.
Will our data end up training someone's model?
Not on the setups we build. Business and API tiers from the major providers carry no-training terms — different from consumer apps, where policies vary. We configure the right tier, put the data-handling terms in writing, and where documents are especially sensitive we add redaction before anything leaves your systems, or keep the whole pipeline local.
What happens when the AI gets something wrong?
It will, occasionally — anyone who says otherwise is selling something. The design accounts for it: human review on anything with a cost to being wrong, confidence thresholds that route uncertain cases to a person, and logging so mistakes are caught and measured rather than discovered by a customer. Fully automated no-review flows are reserved for the tasks where an error is cheap and reversible.
What does an integration cost to run?
Usually less than people expect — API pricing is per use, and most small-business workloads run tens of dollars a month, not thousands. We'll estimate the monthly run cost during scoping and build in usage caps and alerts so there are no surprise bills. The build itself is quoted fixed-fee once the scope is agreed.
Can you work with the software we already use?
Usually yes — anything with an API, an export, or even a watched folder can be integrated. QuickBooks, Microsoft 365, Google Workspace, most CRMs, and plain old network shares are all workable sources. The scoping step confirms what your specific systems expose before you commit to anything.
Want a quote on this?
Send a paragraph about what you're trying to do, we'll come back inside 24 hours.