Business automation systems
Practical automation for intake, follow-up, reporting, documents, and back-office workflows.
Custom AI automation means software that handles a recurring workflow end to end, using a model for the parts that need language or judgment and ordinary code for everything else. Typical targets are intake, quoting, customer follow-up, document processing, and email triage. It is for teams where the same multi-step task is done by hand many times a week and the volume has outgrown the person doing it.
The problem
Most automation projects fail in one of two directions. Either the workflow is too varied for a rules engine, or it gets handed entirely to a model that is confidently wrong a few percent of the time on work that cannot tolerate it.
- Quotes are assembled by copying specs out of PDFs and emails into a template by hand.
- Inbound leads sit unanswered over a weekend and go cold before anyone replies.
- Every invoice, application, or form gets read by a person to pull out six fields.
- A no-code flow handles the common case and silently mangles the rest.
- The team does not trust the automated version, so they check every output anyway and save nothing.
What we build
Document and email extraction
Pulling structured fields out of PDFs, forms, and message threads into validated records, with confidence thresholds that route uncertain cases to a person.
Classification and routing
Inbound work sorted by type, urgency, or owner, so the right person sees it quickly without someone triaging an inbox all morning.
Drafting with human review
Follow-ups, quotes, and summaries drafted from real context and queued for approval. The model proposes, a person sends.
Deterministic guardrails
Pricing rules, validation, and business logic in ordinary code, not in a prompt. The model never gets to invent a number that matters.
Evaluation before rollout
A test set from your real historical cases, scored before the system touches live work, so accuracy is measured rather than assumed.
Cost and failure monitoring
Per-run token cost, latency, error rates, and a log of what the model saw and produced, so quality drift is visible.
What you get at handoff
- Code in your repository, calling your own model accounts.
- The evaluation set and scoring script, so you can re-test after any change.
- A runbook covering the review queue, failure modes, and the manual fallback.
- Training for the people who will review the output day to day.
- A written note on what we deliberately left manual, and why.
Stack
Models
Claude, OpenAI, Structured outputs, Open-weight models where privacy requires it
Language
Python, TypeScript
Documents
PDF extraction, OCR, Email parsing
Business systems
HubSpot, Salesforce, Stripe, QuickBooks, Slack, Microsoft Teams
Operations
Evaluation sets, Review queues, Cost tracking, Alerting
Related services
FAQ
How do you keep the model from getting things wrong?
By giving it less to be wrong about. Anything with a correct answer, such as pricing, tax, eligibility, or thresholds, lives in ordinary code. The model handles language: reading messy input, classifying it, drafting text. Then the output is validated before it reaches a system of record, and uncertain cases go to a person.
Do we have to replace staff for this to pay off?
No, and most of these projects are not framed that way. The usual outcome is that the same team handles more volume, or stops doing the part of the job everyone dislikes. Removing three hours of copy-paste per person per week is a real return without touching headcount.
How do we know it is accurate before we trust it?
We build an evaluation set from your own historical cases, with known correct answers, and score the system against it before it touches live work. You get the score, the failure examples, and the script, so the same test can be run after any prompt or model change.
What does it cost to run each month?
That depends on volume and how much text each run processes, and we estimate it during scoping rather than after. Most document and follow-up workflows at small-business volume land in the tens of dollars per month, not the thousands. Cost tracking is built in so it stays visible.
What if our process is not consistent enough to automate?
Then that is the finding, and automating it would just produce inconsistent results faster. Sometimes the fix is to tighten the process first. We would rather say that during scoping than build something on top of a workflow nobody agrees on.
Can this connect to the tools we already use?
Yes, and it should. These systems usually read from and write to a CRM, an inbox, a payment tool, or an internal database. If a tool has an API, it can be part of the workflow.
Ready to scope Automation?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.