Share how the work happens
Bring an automation request, a bottleneck, or simply walk me through your current operation. You do not need to know what to automate yet.
You bring the contextWe build practical AI automation to handle manual, repetitive work, so your team can focus on higher-quality work.
Start with one repetitive task. AI can understand the request, take the next action, and hand it to a person when judgment is needed.
Bring an automation request, a bottleneck, or simply walk me through your current operation. You do not need to know what to automate yet.
You bring the contextI map the handoffs, repeated decisions, and data involved, then tell you honestly where AI can help and where it should not.
We find the useful first stepYou get a clear proposal for what we will build, what it will cost, and which metrics will prove that the workflow is getting better.
Scope, cost, and success metricsWe put the workflow into the team's hands, watch the results, and keep improving it until the efficiency gain is visible in the work.
Measure the gainWhy choose us
We start with the tools and processes your team already knows. No unnecessary migration, no extra admin, and no new system to babysit.
Every workflow is shaped around your goals, rules, and edge cases. You get a practical solution built for how your business actually operates.
Before we build, we agree on what improvement means: hours saved, faster response, fewer errors, or cleaner records. Then we track it.
Automation handles repeatable work while people keep control of exceptions and decisions that need judgment. We keep iterating as your business changes.
Bring one repetitive process. We'll map what happens today and find the first useful thing to automate.
Insights
We benchmarked Jev, TypeSafe’s first System One Model, against GPT-4o-mini for classifying whether social media posts were relevant to the Glints brand. Across 80 posts, both models agreed on 96.25% of classifications, while Jev was 42.6% cheaper and 1.72× faster on average. The results suggest Jev is a promising candidate for relevance filtering, though a human-labeled evaluation is still needed before considering production replacement.