AI integration & automation
AI that does a defined job, inside guardrails you can audit.
We add language models where they save real hours, in drafting, triage, extraction and search, with human approval where it matters, hard spending caps and a log of every action.
In short
AI integration means connecting a language model to your own systems and data so it performs a specific task, such as drafting a reply, classifying an inquiry, extracting fields from a document or answering questions from your records, inside software you control. Unlike handing staff a chatbot, the model's inputs, permissions, costs and outputs are defined in code and can be audited.
Patterns we build
Drafts with approval
The model writes the first draft of a reply, follow-up or summary. A person approves or edits it before anything leaves the building.
Triage and routing
Inbound leads, applications and messages are classified, prioritized and routed to the right person, with the reasoning stored next to each decision.
Extraction
Structured fields pulled from résumés, PDFs, emails and forms into your database, validated against a schema before they are saved.
Role-scoped assistants
An assistant inside your application that can do what the signed-in user is allowed to do and nothing more, because it acts through the same permission checks.
Operations automation
Scheduled agents that do repetitive checks and submissions. We run our own operations this way: daily indexing submissions, live structured-data checks and lead counting from delivery logs.
Measured cost
Per-task model routing and monthly spending caps enforced in code, so a bad loop or traffic spike can't run up an open-ended bill.
How we work
From idea to a workflow you trust
Pick one job
We look for a task that is frequent, text-heavy and easy to check, such as drafting replies or sorting inquiries, and define what a correct output looks like.
Test on real examples
Before any build, we run the task on a set of real, anonymized past cases and measure how often the output is right, so the decision to proceed rests on evidence.
Build the guardrails first
Permissions, approval steps, spending caps, rate limits and logging go in before the model is connected to anything that touches customers.
Ship in draft mode
The first release suggests rather than acts. Staff approve each output, and every approval or edit becomes evidence for widening its autonomy.
Widen with evidence
Only tasks whose approval rate holds up get more autonomy. Everything else stays human-in-the-loop, and that is a perfectly good outcome.
Guardrails
How we keep AI features safe to ship
For a skilled-trades staffing portal we built, the model drafts applicant outreach. Routine drafts go to a small, fast model, harder cases escalate to a larger one, a hard monthly spending cap is enforced in code, and every message remains a draft until a person approves it. The same principles apply to every AI feature we build:
| Guardrail | What it prevents |
|---|---|
| Model routing by task difficulty | Paying large-model prices for routine work, or small-model quality on hard cases |
| Monthly spending cap enforced in code | An open-ended bill from a loop, a bug or a traffic spike |
| Human approval for anything outbound | A model sending something to a customer that no one has read |
| Role-scoped tool access | An assistant reaching data the signed-in user could not see |
| Evaluation on real cases before launch | Shipping a feature on the strength of a good demo |
| A log of every input, output and action | Not being able to answer why a decision was made |
Proof
Where this work lives
- The staffing portal aboveThe careers funnel behind the outreach-drafting work: 40 distinct skilled-trades applicants in five weeks, and seven hires so far by the client's report.See the details
- Our own operationsEvery retainer includes same-day indexing automation and a monthly report assembled from delivery logs, run by the same agent tooling we use internally.See the details
Tools we build with
- Anthropic Claude API
- TypeScript
- Next.js
- Postgres
- Structured outputs
- Scheduled jobs
Frequently asked questions
Which AI models do you use?
We choose per task. Most of our production work currently runs on Anthropic's Claude models, with a small model for routine work and a larger one for hard cases. The integration is written so the model can be swapped when a better option appears.
Will our data be used to train AI models?
We use providers' commercial APIs, whose terms exclude API customer data from model training by default, and we send only the fields a task needs. Sensitive records stay in your database; the model sees the minimum required.
How much does it cost to run?
For most business tasks, model usage costs far less than the build. We set a hard monthly cap in code and route routine work to smaller, cheaper models, so the running cost is known in advance.
Can AI replace our staff?
Not in the way it is often sold. What it does well is take the first pass at repetitive, text-heavy work so your people spend their time on judgment and relationships. We design for that, with people approving the output.
What is a good first AI project?
Something frequent, text-heavy and easy to check: drafting routine replies, sorting inbound inquiries, or pulling fields out of documents. A narrow first project shows its value quickly and builds the evaluation habits the next one needs.
Do you build chatbots for websites?
Only when they can answer from verified information and hand off cleanly to a person. A chatbot that guesses about your prices or availability does more harm than a good contact form.
Related services and reading
- Custom Software & Web AppsBooking engines, portals, dashboards and integrations built around your real workflow, with correctness enforced in the database.
- Local SEOLocation pages, schema, Google Business Profile, content and same-day indexing, with a monthly report of verified leads.
- Web Design & DevelopmentCustom websites in Next.js: fast, accessible, structured for search, and migrated without losing the rankings you already have.
Talk it through with the people who'd build it.
Twenty minutes on Google Meet. Tell us what you need; we'll tell you plainly what we'd do and whether we're the right fit.