Drafting and writing
Quotes, reports, follow-up emails, and updates drafted from your data in your voice — reviewed by a person, sent in seconds instead of written in twenty minutes.
01What you get
Generative AI Integration, concretely
Reading and extracting
PDFs, emails, and scans turned into structured data in your systems — validated against your rules, with the exceptions flagged for review.
Summarizing and briefing
The hour-long call, the fifty-message thread, the month of tickets — condensed into the brief your team actually reads before the meeting.
Guardrails and evals
What the model may do alone, what it must escalate, and how its quality is measured — defined before launch, tested continuously, logged permanently.
02In practice
The same service, your industry’s shape
Industry
Legal & Professional Services
First drafts from matter data in firm-approved language.
Explore → IndustryLogistics & Transportation
Rate cons, BOLs, and invoices read into the TMS untouched.
Explore → IndustryFinance & Insurance
Applications and statements extracted, checked, and filed same-day.
Explore →03Built on
Tools we reach for
Built on
- Claude / GPT-class models
- RAG on your data
- Structured output
- Evals & monitoring
- Fine-tuning where it earns it
- Your compliance rules
04How we work
Product speed, engineering quality
- 01
Learn the operation
We sit inside the process and watch how it runs today — the steps, the exceptions, the judgment calls, and every tool it passes through.
- 02
Show a working version
Not a mockup and not a deck. The first version you can actually use, in weeks rather than quarters.
- 03
Put it into real work
The system goes into your environment and connects to what you already run. It takes the routine path and hands the rest to a person.
- 04
Measure, then extend
We agree what better means before we start — time, errors, throughput — and report against it. The next process begins once the first one has earned it.
05Questions we hear
Fair questions
Which model do you use?
The one that wins on your task in our evals — we're not tied to a vendor. Model choice is an engineering decision we revisit as the market moves, not a religion.
Does our data train someone's model?
No. Data stays in your environment, API calls are configured for zero retention, and nothing you own becomes anyone else's training set.
How do we know it's accurate?
We build an eval set from your real cases before launch and measure against it continuously. You see the accuracy number — and what happens to the cases it gets wrong.
Start with the process that costs you most
Tell us about the work your team dreads. We'll come back with what a working system would look like — and what it would take to prove it.