AI Automation Architecture · Macon, GA · working nationwide

AI that does the work.
Guardrails that keep you in control.

Simplicate maps how your business actually runs, designs the agent systems that take the repetitive work off your team, and proves every automation in a digital twin of your operation before it touches a single real record.

Request a feasibility audit → Get the free playbook

Architect-led, network-deliveredAutomation roots that predate LLMsOrganizer, The AI Collective Macon

NO HUMAN CHECKPOINT

Air Canada · chatbot invented a bereavement refund policy · tribunal held the airline to it · no validation layer SaaS founder's agent · coding agent deleted a production database during a code freeze · no action gate Mata v. Avianca · lawyers filed six AI-fabricated case citations · sanctioned · no source check Chevrolet dealer · chatbot agreed to sell a Tahoe for $1, "legally binding" · no deterministic filter DPD · support bot swore at a customer and wrote a poem about how bad DPD was · no output guardrail McDonald's · drive-thru voice AI pilot ended after viral mis-orders · no twin testing

Most AI pilots stall.

Not because the models are weak, but because nobody mapped the work first. The bottleneck was never the AI.

Map before you build.

Every engagement starts with a decomposition of the real workflow: which steps are rules, which need judgment, which need a person.

Prove before you deploy.

Automations run against a digital twin of your data and edge cases first. You see it work before it is live.

What an AI Automation Architect does

Three disciplines. One outcome: automation your business can trust.

Simplicate is not a chatbot vendor. The work is architecture: understanding your operation, designing the system that runs parts of it, and installing the controls that keep it safe.

01 / AUDIT

Workflow Decomposition & Feasibility Audits

A fixed-scope look at one or more of your manual workflows, ending in a clear verdict on what to automate and how.

Process mapping. Onboarding, inventory, reporting, intake: where the bottlenecks and hand-offs really are.
AI feasibility assessment. Each step classified: deterministic logic, LLM intelligence, or human oversight.
Data layout evaluation. Are your documents and pipelines clean enough for autonomous context-matching without hallucinated errors?

RESULTA written verdict per workflow: automate now, fix the data first, or keep it human.

02 / ARCHITECT

System Architecture & Multi-Agent Design

Networks of specialized agents that collaborate, wired into the software you already run.

Agent orchestration. One agent pulls raw data, another analyzes it against your metrics, a third drafts the deliverable.
Tool & API integration. Middleware and orchestration frameworks that give models "hands" to read and write across your stack, securely.
Guardrails & validation. Deterministic filters and human-in-the-loop checkpoints so no high-risk action happens without explicit sign-off.

RESULTAgents with hands in your systems, and a sign-off gate in front of anything that moves money or touches a customer.

03 / PROVE

Digital Twins & Decision Simulation

A simulation workspace that mirrors your real data, workflows, and edge cases. Test an automation before it is deployed, or test a business decision before you commit to it.

Mirror the operation. Your actual record shapes, volumes, and exception patterns, without touching production.
Break it on purpose. Malformed inputs, missing fields, conflicting records, and the weird Tuesday cases.
Rehearse the decision. Pricing, staffing, suppliers, marketing: see how a change in one part of the business moves the rest. Try it below.

RESULTA measured pass rate before go-live, and a written explanation of why each outcome happened.

See the method in 20 seconds

Watch a feasibility audit classify a real workflow.

This is the core of the first engagement. Every step of a workflow gets a verdict, and the data layout gets a readiness score. Below is a customer-onboarding example. Run it.

Deterministic: rules, no AI neededLLM: reads, extracts, draftsHuman: judgment and sign-off

Audit my workflow →

feasibility-audit · customer-onboarding.wf idle

01Receive signed agreementemail attachmentDeterministic

02Extract client details from the contractnames · terms · addresses · SKUsLLM agent

03Create the account record in the CRMschema-validated writeDeterministic

04Approve credit terms above thresholdfinancial exposureHuman sign-off

05Draft the welcome and kickoff emailtone-matched · personalizedLLM agent

06Schedule kickoff and notify the account ownercalendar · SlackDeterministic

0Automate now

0LLM-assisted

0Keep human

DATA LAYOUT READINESS —

Operational digital twins

Nothing goes live until it has survived a copy of your business.

A digital twin is a simulation workspace built from your actual data shapes, workflows, and edge cases. Agents run against it first. You watch them succeed, and more importantly, you watch where they fail, before a customer ever does.

01
MirrorReplicate the record structures, volumes, and integrations your team actually uses.
02
StressReplay historical edge cases and inject synthetic failures: missing fields, duplicates, bad formats.
03
CertifySign off on a measured pass rate and a documented list of what the human checkpoint catches.

Decision simulation · demonstration project · live model output

Test your next business move before you make it.

A business is a connected system. Cut staff and costs drop, but service pressure rises and this month's walk-aways become next month's missing regulars. This is Vector, a Simplicate-built simulator of a fictional restaurant in a competitive market. It shows what a decision model does, not what your restaurant will do. Pick a move and read what the model says happened and why.

vector · demonstration · twelve-month season vs. baseline mean of 8 seeds

Raise every menu price 5%

Scenario profit by monthBaseline monthly mean

WHY IT HAPPENED

Same inputs, same results, every time: the engine is deterministic and replays byte-for-byte. Figures are season means over 8 random seeds from the calibration pack, config . The engine writes a causal breakdown for every metric, every month; the explanation above is the plain-English version. Vector is a demonstration of modeling capability, not a validated model of any real restaurant and not a forecast. A model for your business would be scoped to your decisions, calibrated to your available data, and delivered with its assumptions written down.

What would a model of your business help you decide? → Pricing · staffing · inventory & suppliers · marketing & retention · resilience under rising costs

How an engagement runs

Audit. Architect. Prove. Deploy.

Each phase ends in a deliverable you own, whether or not you continue. No black boxes, no retainer you can't explain to your CFO.

PHASE 1 · AUDIT

Feasibility audit

We map the workflow with the people who do it, classify every step, and grade your data layout.

Deliverable: Automation Blueprint with a go / fix-first / no-go verdict

PHASE 2 · ARCHITECT

System design

Agent roles, tool and API integrations, and the exact guardrails and human checkpoints, specified before code.

Deliverable: Architecture spec and risk register

PHASE 3 · PROVE

Digital twin trial

Build and run the system against a twin of your operation. Measure pass rates. Break it on purpose.

Deliverable: Certified test report

PHASE 4 · DEPLOY

Go-live and handover

Controlled rollout with monitoring, sign-off gates live, and your team trained to own it.

Deliverable: Runbook and 30-day tuning

Free playbook

Run your own first-pass feasibility audit this week.

The same decomposition method used in paid engagements, written for owners and operations leads. A 25-minute read. Score any workflow in under an hour, and use it tomorrow.

→The Deterministic / LLM / Human decision tree, with 10 classified examples
→The 12-point Data Readiness checklist: the signs your data will hallucinate
→The 5 guardrails every automation needs before it touches money or customers
→A printable one-page Workflow Scoring Worksheet

Playbook on its way.Check your inbox in the next few minutes. (Prototype: nothing was actually sent.)

FIG. 03 — DELIVERY MODEL

How Simplicate is built

Architect-led. Delivered by a vetted network.

Every engagement has one accountable architect who maps the work, designs the system, and signs the test report. Build capacity scales behind that: integration developers, data specialists, and QA from a network Simplicate has worked with before, pulled in by the scope of the job, never by the sales cycle.

The lean core is a design choice. You get senior judgment on every decision and the capacity of a larger shop when the build calls for it, without paying for a bench. Simplicate's founder brings a background in RPA and business process automation that predates the current wave of AI, and organizes the Macon chapter of The AI Collective, where Middle Georgia operators and builders compare notes on what actually works.

One accountable architect per engagementRPA & process automation rootsVetted specialist networkMacon, GA · The AI Collective

Before you ask

Straight answers.

The questions owners ask before a first audit. If yours isn't here, it goes in the form below.

Do we have to replace our software?

No. The architecture starts from the tools you already run. Agents get read and write access through your existing systems' APIs or middleware. If something has to change, it shows up in the audit as a cost, not as a surprise mid-build.

What does a feasibility audit cost, and how long does it take?

Fixed scope and a fixed price, agreed before anything starts. A single-workflow audit is measured in days, not months. You get the written verdict whether or not we build anything together.

Our data is a mess. Is that a dealbreaker?

It is the most common finding, and it is exactly what the data layout evaluation is for. The usual verdict is "fix these three things first," with the fixes listed. Automating on top of messy data is how hallucinated records happen, so we don't.

What stops the AI from doing something expensive or embarrassing?

Two layers. Deterministic filters catch anything that violates a rule you set (amounts, fields, formats, recipients). A human sign-off gate sits in front of any action that moves money, changes a customer record, or sends something outside the company. Nothing high-risk runs without a person approving it.

Is a digital twin the same as a business simulator?

Same discipline, two time horizons. The twin asks whether an automation behaves correctly before it goes live. A decision simulator asks whether a pricing, staffing, or supplier change pays off before you commit. Both model your business as a connected system and run it against scenarios first.

Who actually does the work?

One accountable architect owns the engagement end to end. Build capacity comes from a vetted network of integration developers, data specialists, and QA, scaled to the scope of the job. You always know who is responsible.

Start here

Request a feasibility audit.

Tell me about one workflow that eats your team's week. Within two business days you'll get a short written read on whether it is worth automating, and what it would take.

No pitch deck, no "AI transformation" roadmap
You get a usable answer even if we never work together
Fixed-scope audits, priced before anything starts

Prefer email? dave@simplicate.ai

Describe the workflow

Request received.You'll hear back within two business days with a written first read. (Prototype: nothing was actually sent.)

AI that does the work.Guardrails that keep you in control.