Applied-AI operations for growth-stage teams

AI agents that take real hours off your team.

For teams where execution quality is the competitive edge, and routine judgment work is eating the week.

ALIPAP designs and ships production AI agents that make decisions inside guardrails, not just move data, backed by the cross-functional operations experience to know which work is worth automating. Outcomes first, engineered for trust: confidence thresholds, human-in-the-loop review, and evaluators calibrated against real human judgment.

Start with an AI & Operations Assessment Get the Launch Readiness Checklist

No prep needed. You’ll leave with 2-3 concrete next steps.

40% team review time cut by a deployed agent

100+ hrs monthly ops time saved per client

50+ production automations shipped

Selected work

Shipped systems with measured outcomes. Every one ran with guardrails and a human in the loop.

Grant-Evaluation Agent

Challenge: Two reviewers spent about 25 hours a week processing inbound submissions by hand, plus 4 hours of weekly decision calls, with scoring that varied reviewer to reviewer.

What ALIPAP built: An n8n + Claude review pipeline. On submission it scores each application against an engineered rubric, generates a summary doc with images extracted in parallel, and routes APPROVE / REVIEW / REJECT down separate paths by confidence level, posting to the right Slack channel with a human in the loop for edge cases. The evaluator was backtested against real human reviews and tuned until its judgments matched the team’s.

Impact: Review time cut from 25 to 15 hours a week and decision calls from 4 to 2, with consistent, rubric-aligned scoring the team trusts.

Partner-Lifecycle CRM, Built In-House

Challenge: Partner intake, scoring, and the handoffs across BD, Legal, and Finance ran on manual coordination. A sister organization’s external vendor quoted about $30K just to auto-populate partner legal documents.

What ALIPAP built: A homegrown ClickUp + Zapier partner-lifecycle CRM. One centralized intake feeds a single pipeline; each stage change auto-creates the next team’s task across BD, Legal, and Finance with a human approval at every gate; partner legal documents auto-generate (the exact capability quoted at $30K); and activity-based scoring surfaces which partners need attention. Delivered alongside a company-wide Basecamp to ClickUp migration across a 50-plus person team with zero disruption.

Impact: Replaced the $30K vendor quote with a build delivered in under a week, saved 5-plus hours a week of manual ops, and cut engineering coordination time about 20% after the migration.

Multi-Agent Outreach Engine

Challenge: Lead research and personalized outreach were entirely manual.

What ALIPAP built: An n8n system that scrapes target accounts, qualifies leads with an AI agent, enriches contacts from a data API, and drafts personalized outreach, running the campaign only after human approval.

Impact: A repeatable lead engine that runs without manual research, with a human gate before anything sends.

Production AI Coaching App

Challenge: Behavior-change apps are usually built one vertical at a time (addiction, anxiety, phobias), so each new launch means rebuilding the product, and putting an AI coach in front of vulnerable users raises the bar on safety and cost control.

What ALIPAP built: A live, multi-tenant AI product. A mobile coaching app (Expo / React Native) with an in-app AI coach served through a live Cloudflare Worker that brokers model calls with primary and secondary key rotation, health checks, per-user and per-tenant daily cost caps, rate limiting, JWT auth, and RevenueCat billing. An operator console turns an intake into a new branded app, and the template ships a new vertical by swapping two JSON config files. Architected and shipped end to end through AI-assisted development (Claude Code) against locked PRDs, two dozen-plus verification gates, a written threat model, and crisis-keyword safety guardrails, with 198 passing backend tests.

Impact: A production backend and a TestFlight build, App Store compliant, that spins up new branded coaching apps from config, with per-tenant cost isolation and user-safety guardrails in place from day one.

Where the hours go

Your team burns hours on routine review, data hygiene, and reporting a system could handle.
You want to deploy AI agents but aren’t sure which work is safe to automate.
Off-the-shelf automations move data but can’t make a judgment call you’d trust.
Priorities are clear, but delivery still drifts and handoffs break down.
Leaders spend too much time coordinating work manually.
Execution depends on heroics instead of repeatable systems.

What we help fix

AI Agent Deployment

Best for: Teams ready to take routine, judgment-heavy work off human plates: submission review, CRM hygiene, lead research, reporting, internal workflows.

Deliverables: Agent design with decision routing and confidence thresholds, evaluator calibration against human ground truth, integration and build (n8n, Claude, Make, Zapier), human-in-the-loop review, monitoring, and team training.

Outcome: A production agent your team actually trusts and uses, with measured manual-hour reduction.

Operations & Automation Sprint

Best for: Teams where delivery drifts across functions and routine work eats the week.

Deliverables: Dependency and owner model, weekly execution cadence, KPI dashboard pack, plus a scoped automation build that retires the first manual workflows.

Outcome: Clearer ownership and the first routine work automated, in weeks, not quarters.

Ongoing Fractional Ops Leadership

Best for: Teams needing consistent cross-functional execution ownership, with an agentic edge, without a full-time hire.

Deliverables: Weekly operating cadence, roadmap governance, stakeholder alignment, escalation management, and a standing automation backlog.

Outcome: Predictable execution and a steadily shrinking pile of manual work.

How we work

Diagnose

We map where the hours go and what is slowing progress, then pick the work that is both high-cost and safe to automate.

Build & Deploy

A focused 2-4 week sprint: design the agent or workflow, calibrate it against real judgment, and ship it with a human in the loop.

Stabilize & Scale

Monitoring, training, and operating rhythms so the system keeps working and the automation backlog keeps shrinking after handoff.

Engineered for trust

Every system ships with the controls a skeptical team needs before it goes live. This is what separates a deployed agent from a demo.

Confidence thresholds Work is routed by certainty. Low-confidence cases escalate instead of guessing.
Human-in-the-loop review A person approves edge cases and anything consequential before it acts.
Evaluators calibrated to humans Agent judgments are backtested against real reviews until they converge.
Cost caps and rate limits Per-user and per-tenant spend ceilings so a runaway job cannot blow the budget.
Written threat model Failure modes and safety guardrails documented before launch, not after.
Audit logs and rollback Every decision is traceable, and any system can be reverted cleanly.

Start with an AI & Operations Assessment

Share one routine workflow or active initiative. We’ll tell you what is worth automating, what to fix first, and return a practical action plan.

Aliaksei Papou, senior operations and applied-AI operator. Naples, FL and remote. Senior operator-led and small by design: you work directly with the person who builds your systems, not an account manager.

Contact

Email: alipapllc@pm.me

LinkedIn: ALIPAP

Location: Naples, FL | Remote: Yes

Engagement Models

AI agent deployment | Fixed-scope sprint | Monthly fractional retainer

Limited concurrent engagements | Response time: within 24 hours