We set up agents for real engineering work, train teams and build our own tools. Then we check the quality of what agents produce and calculate what that work costs.
Three ways to start. Pick by role: engineering, people development or budget.
For CTOs and tech leads
For developers and QA · system analysts · tech leads and managers
For owners and CFOs
A practical course for developers: project context, MCP, agent skills and verifying the result on a task of your own.
Every number says where it comes from: our own project, a cost model or a review of a real deployment.
≈$220
pipeline cost per month
The agents collect a competitor’s changes and map them against the EasyP backlog. Product decisions stay with a human.
≈$750
per month instead of ≈$17.5k (estimate)
The roughly 23× gap is the ratio of two estimates, not savings reported by a specific client.
4 h → 5 min
time to first reply
This measures the first reply, not full resolution. The review shaped our rules: no expensive model by default, and cache hit rate under monitoring.
Ruble figures converted at roughly 90 ₽ per dollar.
Token economics: what makes up your agent bill and how to cut it
This is the pilot path. The workshop and the LLM cost audit can be booked on their own.
1–2 weeks
We map your processes and pick one where an agent helps: with a clear definition of done and a measurable result.
2–4 weeks
We build the pipeline on the team’s real tasks, agree the perimeter with security and measure the share of accepted results.
after the pilot
We lock the standards into your repositories, train the team and leave cost monitoring in place.
Tell us what you want to try: deploying an agent, training your team or reviewing your LLM spend. We’ll set up a short call to go over your stack, security requirements and what is worth trying first.
Led by Edgar Sipki · sipki.online
