Our Methodology

Built to reach production. Fast.

OrwyTech's AI automation methodology is designed from first principles to deliver a working production win within 45 days — not a sandbox demo, not a pilot that stalls. We start with the highest-leverage process in scope, build it against real data, measure it against the ROI model, and only expand from there.

Three principles define every engagement: scope is kept small enough to validate early, AI is built on redesigned processes rather than existing ones, and every deployed module has a measurable accountability loop from day one.

"OrwyTech builds event-driven operating systems that react — agent networks that run workflows end-to-end, without waiting for a human to notice something needs to happen."
70–90%
of enterprise AI pilots fail to reach production
// industry consensus across major analyst firms
~45
days to first production win with our methodology
// high-leverage module, real data, measurable outcome
0
human bottlenecks in the workflows we build
// the goal is full end-to-end execution
Foundation First

Phase 0: Classify before you build.

Before any agent is built, we classify the work. Most business operations are triggered by events or reminders — a form submitted, a deadline crossed, a message received, a record updated. The first question is: which of those triggers is deterministic, and which requires an LLM to route?

Deterministic triggers go straight to workflow engines. Only the ambiguous, language-dependent, or edge-case triggers need an LLM. This decision gate prevents unnecessary inference cost and builds a system that's fast, auditable, and reliable — not a black box.

DETERMINISTIC
Rule-Based Events
Triggered by structured data conditions — if X then Y. Fast, auditable, no inference cost. Routed directly to workflow execution.
LLM-ROUTED
Ambiguous Events
Requires language understanding, intent classification, or nuanced judgment. Routed through a model with explicit confidence thresholds and fallback paths.
FOUNDATION
Phase 1: Infrastructure
Before any automation: databases, webhooks, API scaffolding, data pipelines. The plumbing that everything else depends on. Built once. Runs forever.
Discovery Process

Six questions. Every task.

Before we automate anything, we shadow it. We sit with your team, observe the actual work, and ask six questions about every repeating task in scope. The answers build the AI Opportunity Matrix — the backbone of every engagement.

Q1
Who does this?
Role, seniority, loaded labor cost. Determines the baseline value of automation and which tasks are strategically critical versus purely administrative.
Q2
How long does it take?
Per-instance time measured precisely, not estimated. Validated against time-and-motion observation, not self-reporting (which is notoriously inflated).
Q3
How often does it happen?
Daily, weekly, triggered by volume events? Frequency × time × cost = the raw savings number. This is where most ROI estimates go wrong — they don't count the real volume.
Q4
What tools does it touch?
CRM, ERP, email, spreadsheet, internal dashboard. Integration surface determines build complexity and timeline. No surprises at the halfway point.
Q5
What's the error rate?
Human error on this task — rework cost, downstream impact, escalation frequency. Automation that eliminates high-error tasks compounds its value beyond the raw time savings.
Q6
What if it's wrong?
Consequence severity. A mis-routed invoice versus a mis-sent legal notice are different risk profiles. Determines whether confidence gates are soft (flag for review) or hard (human approval required).
AI Opportunity Matrix

Score every task. Prioritize ruthlessly.

Every task in scope gets scored across four dimensions. The matrix becomes the roadmap: which modules to build first, in what order, and why. Stakeholders can see the logic. There are no arbitrary prioritization decisions.

DIMENSION 01
% Automatable
What fraction of this task can be handled without human intervention under normal conditions? Accounts for exception handling, edge cases, and confidence thresholds.
DIMENSION 02
AI-Native Fit Score (0–10)
How well does this task match AI's actual capabilities — language, pattern recognition, structured extraction? A low fit score means rule-based automation, not LLM. We use the right tool for the job.
DIMENSION 03
Potential ROI
Annual savings = time_saved × loaded_labor_cost × frequency × automation_%. Three scenarios: conservative, medium, optimistic — with explicit assumptions. Conservative must impress on its own.
DIMENSION 04
Measurement Confidence
How reliably can we measure whether automation is working? Tasks with low observability get instrumented before automation. You can't improve what you can't see.
Delivery Model

Progressive deployment. Trust flywheel.

We don't build everything at once and flip a switch. We identify the highest-leverage module in the AI Opportunity Matrix, build it first, test it on real data in production, measure it against the ROI model, and only then unlock the next module. Each win builds trust. Each win funds the next.

Your team sees results within weeks, not quarters. Skeptics become champions. Champions become advocates. The organization's appetite for automation grows with its confidence in it.

PHASE 0
Event Classification & Foundation
Classify all work triggers. Build the infrastructure layer — databases, APIs, data pipelines. No automation yet. Just a solid foundation that won't need to be rebuilt.
PHASE 1
First Win (~45 days)
Highest-leverage module from the AI Opportunity Matrix. Built, tested on real data, deployed to production. Measured against the conservative ROI scenario. The trust anchor.
PHASE N
Expand & Compound
Each subsequent module builds on shared infrastructure from Phase 0. Marginal cost drops. Value compounds. The end state is an event-driven OS — not a collection of standalone tools.
Agent Networks Process Mining Event-Driven OS ROI Modeling Progressive Deployment Workflow AI
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Tell us about your operations. We'll identify the highest-leverage automation opportunity and show you what the ROI model looks like before you commit to anything.

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