From Messy Processes to Measurable Automation

IAnversic Process transforms manual, disorganized business processes into evaluated, optimized, automated and measurable workflows. Assess before automating. Evidence over assumptions. Deterministic execution with human accountability.

Process Lifecycle

8 Phases

Discovery → Validation → Assessment → Design → Build → Deploy → Monitor → Improve

Assessment Engine

6 Dimensions

Business Value, Process Characteristics, Data Readiness, Technical Feasibility, Risk & Governance, Economics

Decision Authority

Human-in-the-loop

Approvals required for external communication, criteria changes, financial actions, workflow publication

Reference Use Case

Vendor Selection

End-to-end demo: RFP intake → proposal extraction → scoring → comparison → approval → report

Process Intelligence in Action

Three core capabilities that demonstrate how IAnversic Process turns messy processes into measurable automation — from discovery to deployment.

Simulated input: Vendor proposals (PDF, DOCX, XLSX), email threads, questionnaires

AI-extracted, pending validation6 of 9 items human-validated

Process

Vendor engagement and selection — from RFP intake to contract decision

Discovered Current State

Proposals arrive in 3 formats; analyst manually copies prices into a comparison spreadsheet. . Mandatory EU data residency requirement disqualifies vendors without written confirmation. .

RFP email thread, message 3, §4.2

Click to see evidence linkage

“Mandatory: the vendor must host all customer data inside the EU. Proposals that cannot confirm EU data residency in writing will not be evaluated.”

Process Intelligence Principles

Seven principles that distinguish IAnversic Process from generic automation tools. Every design decision traces back to these.

Assess Before Automating

Never invest in automation without evaluating technical, operational and economic viability first. The assessment engine produces explained scores, not opaque ratings.

Evidence Over Assumptions

Every metric is tagged: observed, provided, estimated, or unknown. Estimates never count as facts. Missing data blocks approval until explicitly acknowledged.

Deterministic Execution

Calculations, state transitions, approvals, retries, and versioning run as controlled code. AI recommends; the runtime decides what executes.

Human Accountability

Sensitive decisions (vendor communication, criteria changes, financial actions, workflow publication) require authorized human approval with full evidence traceability.

Process Lifecycle & Architecture

Eight phases from discovery to continuous improvement. Each phase produces auditable artifacts and feeds the next. Built on modular monolith with durable Postgres runtime.

home.arch_ingestion_pipeline

1. Discovery

Upload documents, emails, spreadsheets, diagrams. AI extracts activities, actors, decisions, rules, exceptions, systems, metrics gaps.

2. Validation

Human review of AI draft: confirm, edit, add missing steps, identify responsible, link evidence, mark unknowns, record disagreements.

3. Assessment

Six-dimension evaluation (Business Value, Process Characteristics, Data Readiness, Technical Feasibility, Risk & Governance, Economics) with deterministic scoring.

4. Target Design

Propose improved process: eliminate redundancy, simplify approvals, standardize inputs, add validations, integrations, AI-assisted tasks, deterministic workflows.

5. Build & Validate

Translate to workflow definition with supported nodes. Validate structure, permissions, rules, error handling, idempotency. Run test cases.

6. Deploy & Operate

Publish immutable version. Durable execution with checkpoints, retries, timeouts, human tasks, approvals, idempotency keys. Full audit trail.

7. Monitor & Measure

Track cycle time, human hours, error rate, volume, cost per case, rework. Compare against baseline. Distinguish observed from estimated outcomes.

Technology Stack

Modular monolith with durable Postgres runtime. AI provider layer with adapter pattern. No external runtime dependencies.

Backend

Python 3.12+, FastAPI, Pydantic v2, SQLAlchemy 2, Alembic. Async workers for durable execution.

AI Provider Layer

Adapter pattern for model providers. Structured outputs, timeouts, retries, token limits, cost tracking, prompt versioning, validation.

Database

PostgreSQL with SKIP LOCKED for durable queues. Checkpoints, idempotency keys, audit events. No Redis/Temporal required.

Frontend

Next.js, TypeScript, Tailwind CSS, shadcn/ui. Bilingual (en/es) + light/dark from day one. OpenAPI-generated API client.

Infrastructure

Docker, Docker Compose. Env-only config (PROCESS_*). Health checks, migrations, backups. Self-hosted and cloud ready.

Quality & Evaluation

ruff, mypy, pytest. IAnversic EVAL integration for AI task evaluation. Deterministic calculation services tested separately.

Vendor Selection Demo

Explore the complete end-to-end demo: 3 fictional vendors (Alder & Finch, Brightpath Systems, Corvane Labs), weighted criteria, mandatory requirements, missing data handling, and traceable comparison report.