Onboarding, churn, and embedded AI that compounds with every customer.
We build production AI for SaaS teams across the EU and Greece — onboarding automation, churn prediction, customer success copilots, and embedded AI features that ship inside your product. Every system we build is wired to your usage data and instrumented against activation, retention, and expansion — not just model output.
Every SaaS team in 2026 is being asked to ship AI features. The question is no longer whether to ship them — it's which ones actually compound: which ones increase activation, which ones reduce churn, and which ones can be charged for.
The teams we work with usually have a list of AI experiments and a half-built copilot. They're past the demo and starting to feel the operational drag: features that look good in the changelog but don't move retention, model spend that grows faster than ARR, and a customer success team running on screenshots instead of signal.
What they need isn't another model — it's the engineering layer that turns usage data into product decisions: instrumentation, evaluation harnesses, embedded features wired to billing, and copilots that actually shorten the path to value. That's what we build.
Activation flows that adapt to the user's role, data, and intent — not the same scripted tour for everyone.
Risk scores tied to the playbook your CSM team actually runs, with the next best action included.
Account briefs, expansion signals, and renewal prep delivered before the QBR — not during it.
Features your customers see and pay for, wired to usage limits, billing, and your existing UI components.
Cohort views, leading indicators, and explanations of why activation or retention moved.
AI answers that actually unblock users in-product, with full handoff context when they need a human.
Representative engagement
Product roadmap had an AI-powered "summarize meetings" feature that internal engineering had estimated at 6 months. CEO needed it in time for the next investor update. Existing team had React + Node + Postgres + AWS competence but no LLM-orchestration experience.
We built the feature on Claude Haiku (cost-tuned for the per-tenant unit economics they needed) with a persistent memory layer in Postgres, usage analytics, and per-tenant rate limiting. Integrated into the existing React SPA in two-week increments with weekly demos. Their engineers paired with ours throughout — they own and maintain the feature now.
Feature shipped in 9 weeks with full multi-tenancy and observability from day one. Tracking 40% MAU adoption in the first 60 days post-launch. Client added a new pricing tier monetizing the feature; payback on the engagement happened within the first quarter. Their team now ships LLM features without us.
On attribution · Client and exact metrics anonymized at the client's request. Engagement details (timeline, model choice, integration approach, knowledge transfer) are accurate.
Two to three weeks. We map the buyer question, the data, the regulatory shape, and what shipping looks like. Output is a written brief with a fixed-scope first phase.
A working slice end to end — the model, the integration, the UI, and the observability. Built to be evaluated, not to demo.
Production engineering: data contracts, decision logs, deployment, monitoring, runbooks. The thing your team can own after we leave.
Cutover, training, and a handover that includes the parts most teams skip — change-management notes, audit-ready docs, and a 30-day support window.
Mental Bound