Build AI into what you already ship.

We help your team adopt AI where it pays off. Train your engineers to build with it, and build it into your product and workflows, guided by a team that runs its own AI infrastructure in production.

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carpathian.ai / ai / triage
support/triage.py
from carpathian import Carpathian

client = Carpathian()

def summarize(thread: list[str]) -> str:
    reply = client.chat(
        model="osprey",
        prompt="\n".join(thread),
    )
    return reply.text
Train your team
Working sessions on the systems your engineers already maintain, so the skills land on work that was going to happen anyway
Build AI into your product
Features your customers use, designed and built into the application you already ship
Automate the workflow
Support triage, data entry, reporting, and the handoffs between systems that quietly eat the week
Host the models
Open models running on our own US infrastructure, for the work whose data cannot leave your control

Two ways in

Bring your engineers up to speed, ship AI features in your product, or both at once.

Train your team
  • Working sessions on the code your team already maintains
  • Patterns for using AI in code review, testing, and documentation
  • Guardrails that keep the speed from turning into defects
  • Playbooks your engineers keep using after we step back
Build AI into your product
  • Features like search, summarization, classification, and assistants
  • Automation that clears repetitive work out of your team's day
  • Private model hosting when your data needs to stay in-house
  • Integration with the tools and data you already run
Your codebase
Sessions run on the systems your team already maintains, so the work counts twice
Your data
Private model hosting for the work that cannot leave your infrastructure
Your playbooks
Written down and handed over, so the gains outlast the engagement
Your call
We rank the uses by the time they give back and you decide what ships
Book a Working Session

What the engagement covers

We start where the friction is, prove it on your own work, then make it something your team can run without us. You keep the code, the playbooks, and the judgment about where AI belongs.

Most engagements open with a short discovery pass over the work your team repeats every week, because that is where automation pays back soonest and where a wrong guess costs the least to correct. From there we pick a single workflow, build it properly, and put it in front of the people who have to live with it.

Training runs alongside the build rather than after it. Your engineers watch the decisions being made, take the second workflow themselves with us reviewing, and keep the patterns once we step back.

  • The source, the prompts, and the evaluation set, all of it in your repositories
  • Playbooks written against your stack and your review process
  • Accounts and API keys in your own name from the first day
  • A ranked read on which workflows are worth automating next
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Included
  • Team training and upskilling
    We embed with your engineers and get them productive with AI on the work they already do, so the fluency stays with the team after we leave
  • Workflow integration
    We wire AI into the places it saves the most time: support triage, data entry, reporting, and the handoffs between your systems
  • AI product features
    We design and build AI features into your application, from a first prototype to a production feature your customers rely on
  • Model strategy and privacy
    We help you choose between commercial and open models, and can host them on our own infrastructure so your data stays private

How we work

Three steps, each one producing something you can judge before the next begins.

The process
  • Find the highest-value uses
    We look at how your team works and where AI removes the most friction, then rank the candidates by the time each one gives back
  • Pilot on your work
    We run a focused pilot on your workflows and codebase, so you see results before committing to anything larger
  • Train and roll out
    We upskill your team, put guardrails in place, and hand over playbooks so the gains keep compounding after we step back

Need the software built too?

We build the application as well as the AI inside it, and we can improve the system you already run before adding anything to it.

Custom SoftwareSoftware Improvement

Common questions

Straight answers to what teams ask before bringing AI into their stack.

What does AI application development include?
Two things. We train your engineers to use AI tools well in their day-to-day work, and we build AI into your applications and workflows. You can start with either and grow into both.
Do you train our team on our own codebase?
Yes. Training is hands-on and embedded. We pair with your engineers on the work they are already doing, so the skills stick to your stack and your workflow rather than a generic slide deck.
Which AI models and tools do you work with?
We work with the leading commercial models and with open-source models we host ourselves. We help you pick the right one for each use case based on cost, privacy, and quality, rather than pushing a single vendor.
Is our code and data kept private?
It can be. We can run models on infrastructure we host so your prompts and data are not shipped to a third-party API. You decide what stays in-house and what does not.
How is this different from just buying AI tools?
Tools on their own tend to stall. Adoption comes from tying AI to how your team ships, with the training and guardrails to use it safely. That is the part we focus on.
What can you build with AI?
Product features like search, summarization, classification, and assistants, plus internal automation that removes repetitive work. We scope it to what moves the needle for your business.

Put AI where it earns its place.

One call is enough to tell you which parts of your workflow are worth automating and which are not.

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