Zerostic designs, builds, and operates AI automations end to end — from mapping the messy manual process to shipping a monitored, human-supervised system that runs in production. This deck shows how we work and walks through one real build.
Empowering Your Business — idea to production, one partner.
Most "AI automation" breaks because it stops at a clever prompt. We treat every automation as production software — with integrations, guardrails, a human in the loop where it matters, and monitoring so you can trust it to run unattended.
LLM agents (Claude, Gemini) that read context, decide, call tools, and hand off to a human when confidence is low.
Grounding on your own documents, catalogues, and data so answers and actions reflect your business, not the open web.
Connect CRMs, WhatsApp/Meta, email, payment gateways, databases and internal tools into one flow.
Real-time pipelines and event streaming (Kafka) so automations react the moment something happens.
Approval gates and review dashboards so people stay in control of anything sensitive or irreversible.
Cloud deployment (Docker/Kubernetes, CI/CD), logging, cost guardrails and uptime — we run it, not just build it.
Predictable delivery matters more than a flashy demo. Each phase has a clear deliverable, so you always know what you're getting and when.
We sit with the people doing the work today and map the real process — triggers, decisions, exceptions, and the outcome that actually matters. We define success metrics before writing any code.
Architecture for the automation: data model, where AI reasons vs. where rules run, integrations, and guardrails. We agree on what stays human-approved.
We build the agents, wire up every system, and stand up the review dashboard. Delivered in working increments you can see, not a black box at the end.
Evaluations on real cases, edge-case handling, security review, and cost guardrails so the system is dependable and predictable under load.
Ship to production on managed cloud, with monitoring and alerting. We keep improving it as your volume and needs grow.
This is our reference architecture. Whatever the use case — support, sales, operations, document processing — the shape stays the same: something triggers it, the AI layer reasons over grounded context, actions fire across your systems, and a human supervises the parts that matter.
A concrete build to make the above real: an automation that takes a raw lead and carries it all the way to a booked meeting — qualifying, enriching, reaching out, replying, and scheduling, with a human able to step in at any point.
Leads arrive from forms, ads, and lists. A person manually checks each one, guesses if it's worth chasing, writes an outreach message, follows up, and tries to book a call. It's slow, inconsistent, and most leads go cold before anyone replies.
Leads land from a form, an uploaded list, or a discovery scrape, and are cleaned and de-duplicated into one place.
An LLM reads each lead and assigns a confidence score against your ideal-customer profile. Low scores are archived (never deleted), high scores move on.
Search-grounded lookup adds context — company, role, signals — so outreach is specific, not generic.
A tailored message goes out over WhatsApp or email. High-value accounts pause for a human to approve first.
When the lead responds, an AI agent answers their questions and offers real calendar slots; silence triggers a timed, polite follow-up.
The meeting is booked on zerostic.link/meeting, and everything — score, messages, outcome — is logged to a dashboard your team fully controls.
We build on proven, scalable technology rather than fragile no-code chains, so your automation holds up as volume grows.
Share one process that eats your team's time — leads, support, onboarding, reporting, document handling, anything repetitive. We'll map it and come back with a concrete automation plan.
Which manual process should the AI take over first?
What tools does it touch — CRM, WhatsApp, email, sheets, your app?
What does "working" look like — time saved, faster replies, fewer errors?