Contact
512-521-6791
1205 BMC Drive
Suite #106
Cedar Park, TX 78613
The Automation Library
What’s Actually Possible
A tour of what data-grounded automation can do, anchored in our own builds and illustrated with published results from across the market. Find the layer where your time is leaking.
A note on the numbers below. The results attributed to named companies are published case studies from those companies and their vendors. They are not JLytics engagements. We include them to show what each class of automation can do when it is built well, not to claim them as ours. Where the work is ours, we say so plainly.
The point is the pattern, not the logo. The same mechanism that earned those results is the mechanism we build, with the data preparation and quality control that decide whether you land in the success column or the 95% that does not.
1
Sales and marketing
What it looks like for you
Enrichment and outreach that runs without an SDR doing busywork. A CRM that stays clean instead of decaying. Marketing reporting that builds itself. Local presence (Google Business Profiles, reviews) managed at scale from one place.
What JLytics builds here
Lead pipelines that scrape target companies, enrich each with AI research, qualify and stage them for human review, then launch tailored outreach. Google Business Profile management that drafts ready-to-publish posts and gathers reviews across many locations. Persona engines that rank your customers by conversion, revenue, and value so the targeting is grounded, not guessed.
Where DIY goes wrong: over-automation that tanks your sender reputation; personalization tokens that render blank because the CRM field was dirty; a funnel that was broken before you made it run faster. We fix and instrument the process first.
2
Data and analytics pipelines
What it looks like for you
Raw exports turn into trustworthy numbers on a schedule, with no one manually exporting and reformatting. Metrics mean the same thing across every report. You find out the moment a number goes wrong, instead of after a decision is made on it.
What JLytics builds here
Practice and performance dashboards wired to your source systems, with the dirty-data reconciliation done up front. Analytics reporting that ingests raw exports and computes change over time on a steady cadence. The persona and revenue-indexing models that sit underneath our audience work and run identically every time.
Where DIY goes wrong: silent failures, where the pipeline reports success while delivering stale or wrong numbers. Garbage in, garbage out. Brittle pipelines that break on every upstream schema change. We assert expected outputs, monitor from outside the pipeline, and decouple from fragile source schemas.
3
Back office and operations
What it looks like for you
Invoicing, accounts payable, and document handling that stop eating staff hours. Repetitive operational work that runs without a person babysitting it. Headcount you do not have to add as volume grows.
What JLytics builds here
Daily cash-flow summaries that pull billing data and flag work delivered but not yet invoiced. Self-maintaining trackers for leads, transactions, and promotions that keep themselves tidy on schedule. Document organization for inbound and outbound flows.
Where DIY goes wrong: automating a bad process instead of fixing it first (roughly 40% of back-office automation failures trace to this); screen-scraping bots that break on every vendor UI update; maintenance costs that quietly exceed the labor saved. We standardize the process first, prefer APIs over scraping, and budget maintenance as a permanent line.
4
The engine layer: AI agents and LLM workflows
What it looks like for you
Customer-facing assistants that are actually safe to deploy. Employee copilots that speed real work. Agents embedded in a specific domain (your marketing, your operations) that do bounded, verifiable jobs rather than vague everything-bots.
What JLytics builds here
A daily executive briefing that reads across your sources overnight and lands one oriented summary before your first meeting. An offer and audience designer that runs structured research with a human approval gate at every step. Retrieval-grounded systems (RAG) built on your own documents and data, with the guardrails that keep them honest.
Where DIY goes wrong: this is the most dangerous layer. Hallucination reaching users as confident fact. Scope creep, where a multi-step agent compounds small errors into a high failure rate. No human in the loop on irreversible actions (agents have deleted production databases with no confirmation). Cost and latency blowups from agentic loops. We narrow scope to bounded, verifiable tasks, ground agents in trusted data, gate risky actions behind human approval, and cap cost and iterations before anything ships.
The common thread
It was never about the tool
Notice what does not appear in any of those results: the name of a tool. The wins came from pointing the right automation at a real, well-scoped process, feeding it clean data, and building in the quality control that keeps it accurate. That is the work. The tool is the easy part everyone copies.
If you can see your own time leaking in one of these four layers, that is the conversation to have.
