Quality Controls

AI in quality control for oil & gas construction in Alberta — a development blog by AIMQC

---

We are launching Quality Controls, a development blog about AIMQC — AI-powered mobile quality control for pipeline and construction QA/QC in Alberta.

This space is intentionally technical. We will write about how the AIMQC platform is built and evolved: structured workflows (ITP / IR / ITR / NCR), multi-tenant office operations, evidence and audit trails, APIs and integration, and the path from fragmented data to centralized, verifiable quality. Deeper product context lives at aimqc.com; here we unpack the engineering and design choices behind it.

If you are a QC lead, a builder, or an engineer curious about compliance-as-code, field-first systems, and API-first platforms in regulated construction — this blog is for you.

---

What we will cover

We have four articles planned to situate the software, the role of AI, the data and platform design, and the timing of AIMQC in the market:

1. The context we are building in Alberta O&G construction QC: fragmentation, turnover, and why the office cannot carry the load alone

Grounding the product in real constraints: scattered inspection data, reporting and audit burden, visibility gaps through project close-out, and APESA / AB-83 turnover requirements.

2. From ~15% coverage to ~99%: why AI changes the ceiling Why manual QC verification caps out — and how AI-assisted workflows raise coverage without multiplying headcount

The economics and physics of spot-check, admin-heavy models. Why high inspection coverage requires machine-scale triage — not to replace judgment, but to focus human competence where it matters.

3. Data-first, AI-ready: maps of data, APIs, and integration Under the hood: canonical QC objects, APIs, and object graphs that make AI integration reliable on purpose

The intentional foundation: a PostgreSQL-backed domain model (ITP, IR, ITR, NCR, turnover artifacts), 100+ REST surfaces, schema-driven contracts, and an API-first integration story that lets AI agents read context, propose actions, and stay bounded by the same compliance gates as humans.

4. Why AIMQC, why now, why this design Riding the wave: AI engineering, structural quality, and building at the apex of a platform shift

Timing (mature cloud, LLMs, computer vision, regulated demand for evidence), design (state machines, templates, multi-tenant isolation, runtime governance), and strategy. AIMQC bets on intelligence in structure — code, APIs, and invariants — augmented by AI.

---

About AIMQC

AIMQC is an AI-powered quality control platform for oil and gas pipeline and construction projects in Alberta. The platform covers the full QA/QC lifecycle — from field inspection on mobile through to office review, compliance reporting, and system turnover — built on a compliance-first, field-first architecture.

Learn more at aimqc.com.

---

David Olsson — CTO, AIMQC dolsson@aimqc.com