Service
AI automation services for production workflows
As an AI systems engineering company, we design and build production-grade automation platforms, AI agents, and revenue-ops pipelines. Every system is structured for measurable cycle-time reduction, reliable execution, and long-term architectural stability.
Products & platforms engineered across 12+ industries
Lead-cycle time reduction on GetBoardWise (3 days → 16 hours)
AI runs delivered at 99.9% uptime across production deployments
Production automation systems we have shipped
From AI-driven outbound prospecting to end-to-end revenue automation, these are real systems running in production for our partners — built around hard cycle-time, uptime, and reliability targets.
GetBoardWise
A monorepo with 5 apps (API, admin portal, client portal, jobs worker, sales service) orchestrating end-to-end LinkedIn outreach for board-level prospecting. AI QC pipeline, personalized messaging via Claude Sonnet + GPT-4o + Firecrawl, campaign execution through Phantombuster + AdsPower anti-detect profiles + Oxylabs residential proxies, and a separate pre-sales pipeline (Typeform → Zapier → HubSpot → AWS Lambda CV parsing).
Cycle time reduced 78% (3 days → 16 hours)
+19 NPS across 12K+ runs at 99.9% uptime
Zero double-bookings via real-time calendar sync
Multi-channel alerting with auto-escalation
Smart Lead Automation (n8n)
A self-hosted n8n workflow on AWS EC2 that handles the full lead lifecycle in under two minutes: Google Form → Google Sheets → GPT-4o-mini classification (Hot/Warm/Cold with reasoning) → Slack notification → HubSpot contact + deal creation → QuickBooks customer record. Zero manual intervention from capture to CRM.
End-to-end execution in under 2 minutes
AI scoring with explanatory reasoning per lead
4-system sync (Sheets, Slack, HubSpot, QuickBooks)
Polling cadence tunable per business need
Stellar Cleaners
Lead-to-revenue automation for a multi-location residential cleaning company. GoHighLevel core + BookingKoala + Make.com + Gmail + Google Local Services Ads + Squarespace. Make.com polls Gmail every 15 minutes, parses LSA emails with regex, and auto-creates tagged contacts and opportunities. A 7-stage pipeline and six automated workflows run capture-to-rebooking with stop-on-response logic.
6 automated workflows from lead capture to win-back
Location-based auto-tagging across 10 service areas
7-stage pipeline with timed escalation
Real-time owner + office push via Lead Connector
Let's take your systems and automation to the next level
Discover how intelligent automation transforms business performance
Compressed cycle times
Multi-day manual workflows collapse into minutes or hours when AI agents handle classification, enrichment, and routing.
Reliable execution at scale
Production-grade pipelines run with monitoring, retries, and audit trails — not brittle scripts that break under load.
Cross-tool orchestration
CRMs, billing, calendars, messaging, and AI providers stitched into one coherent system with stop-on-response and escalation logic.
Operator-grade observability
Dashboards, Slack alerts, and weekly AI insight crons keep humans informed and in control of the automated layer.
Real outcomes from automation systems we have shipped
Lead-cycle time
on GetBoardWise outbound (3 days → 16 hours)
End-to-end lead handling
from form submission to CRM + accounting record on Smart Lead Automation
Uptime across 12K+ AI runs
on GetBoardWise multi-app production deployment
Our partners find numerous reasons to love us
Tinkerbyte rebuilt our outbound around a real AI pipeline — campaign execution, QC, and personalization that finally moves at the speed our team needed.
Client
CEO, GetBoardWise
Well-structured AI & automation solutions built for your needs
AI automation — common questions
What is AI automation, and how is it different from traditional automation?
Traditional automation runs deterministic rules ("if X then Y"). AI automation uses large language models and AI agents to handle the parts of a workflow that need judgment — classifying ambiguous leads, drafting personalized messages, parsing unstructured documents — alongside rule-based steps for the deterministic parts. The result is a pipeline that handles a much wider range of real-world inputs than rule-only automation.
How long does it take to build a production AI automation system?
For a focused use case (one workflow, one team), we typically ship a working pipeline in 4–8 weeks. For a multi-app platform like GetBoardWise (5 apps, multi-channel alerting, AI QC, anti-detect campaign execution), the build runs 3–6 months with continuous improvements after launch. We scope per project after a free tech audit.
Do you build custom AI agents, or use off-the-shelf platforms like n8n and GoHighLevel?
Both — chosen per project. Off-the-shelf tools (n8n, Make.com, GoHighLevel, Zapier) are right when you need a fast, maintainable revops pipeline with mostly standard integrations — see our Stellar Cleaners GoHighLevel build and the Smart Lead Automation n8n pipeline. Custom code is right when you need proprietary orchestration, anti-detect execution, or LLM cost optimization beyond what no-code tools allow — see GetBoardWise.
Which LLMs do you work with — OpenAI, Anthropic, Google, others?
All of them, often in parallel. We pick providers per task based on cost, latency, and quality. On Callan Hawkins we run OpenAI, Gemini, and Claude in parallel for CV parsing — each provider scores different documents better, and routing intelligently keeps costs down. On GetBoardWise we use Claude Sonnet for message generation and GPT-4o for QC. We are not locked into any single vendor.
How do you handle reliability — what happens when an LLM API goes down or returns garbage?
Production AI systems need real engineering, not just prompts. We build with retries, fallback providers, response validation, and observability baked in. GetBoardWise runs at 99.9% uptime across 12K+ AI runs because the AI calls are wrapped in a real job queue (Bull/Redis), with multi-channel alerting and auto-escalation when things go wrong. We treat AI APIs the same way we treat any external dependency: with assumed failure.
What does an AI automation project cost?
Costs depend on scope. Revops pipelines built on existing platforms (n8n, GoHighLevel, Make.com) typically run lower because the platform handles infrastructure. Custom multi-app builds with proprietary orchestration cost more but produce systems that compound (GetBoardWise saw a −78% cycle-time reduction). The honest answer is: we scope after a free tech audit so you get a real estimate instead of a number we guessed.
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