Calnetic Process
From operational workflow to production software and AI.
Calnetic Process
How we scope, architect, and deliver.
Step 01
PLAN
Discovery & System Audit
We start with how your operation actually runs today. We map the workflow, audit existing databases and legacy systems, and identify exactly where deterministic software rules belong versus where AI models will measurably improve speed or accuracy.
- 01.1
Operational Discovery
Walk the floor with operators and subject-matter experts to map real workflows, exceptions, and bottlenecks.
- 01.2
Workflow & Data Audit
Inspect existing databases, ERPs, spreadsheets, and APIs to see how data is captured, moved, and reconciled.
- 01.3
AI vs. Rules Assessment
Separate strict deterministic logic (calculations, compliance, permissions) from tasks suited for private AI models.
- 01.4
Scope & ROI Roadmap
Deliver a phased implementation plan with clear technical boundaries, milestones, and measurable operational ROI.
Step 02
DEFINE
Architecture & Governance
Before writing production code, we lock down the architecture. We specify data models, API contracts, private cloud security boundaries, human-in-the-loop review gates, and interactive prototypes so stakeholders can validate the system upfront.
- 02.1
Solution Architecture
Design the database schema, application services, cloud topology, and model orchestration layer.
- 02.2
System Integration Specs
Define API contracts, webhooks, and sync pipelines across your ERP, CRM, accounting, and legacy databases.
- 02.3
Security & AI Governance
Lock down role-based access, private model endpoints, audit logging, and human-in-the-loop approval gates.
- 02.4
Interactive Prototyping
Validate clickable interfaces and workflow screens with end users before full engineering build begins.
Step 03
EXECUTE
Build, Cutover & Support
We build and ship in iterative milestones with automated testing, CI/CD pipelines, and zero-disruption cutovers alongside your active systems—followed by ongoing operational monitoring, model evaluation, and long-term support.
- 03.1
Iterative Engineering
Ship working software in short, reviewable milestones with direct access to the senior engineers building it.
- 03.2
Automated QA & Evals
Run unit/integration test suites alongside structured AI accuracy benchmarks against real operational edge cases.
- 03.3
Production Cloud Deployment
Execute zero-disruption cutovers into isolated AWS, Azure, or GCP infrastructure with full observability.
- 03.4
Long-Term Support
Provide ongoing SLA support, security patching, model tuning, and feature evolution as your operations scale.
On site in Calgary and Alberta
For local clients, we do this work in the room.
Discovery walk-throughs, stakeholder interviews, requirements workshops, prototype reviews, and go-live training all happen at your office, plant, or job site. We have run on-site sessions many times, for organizations from large enterprises to early-stage startups.
FAQs
Common questions
What is Calnetic’s software and AI development process?
Our delivery runs in three engineering stages: Plan (operational discovery, workflow mapping, and deciding where deterministic rules vs. AI models belong), Define (solution architecture, integration specs, private data governance, and interactive prototyping), and Execute (iterative build, automated testing, cloud deployment, and long-term support).
How do you evaluate whether a process should use AI or deterministic code?
During the Plan stage, we audit each step of the workflow. Steps requiring strict math, compliance enforcement, or exact state rules are written as deterministic code. Steps involving unstructured documents, natural-language intake, classification, or cross-document synthesis are engineered with private AI models and human-in-the-loop verification.
How do you protect proprietary data during AI and software projects?
All client systems and AI integrations run inside isolated cloud environments (AWS, Azure, or GCP) using enterprise model endpoints that never train on your data. Role-based permissions, encryption, and audit trails are specified in the Define phase before any production data is connected.
How do you ensure project transparency and communication?
You work directly with senior architects and engineers. We validate interactive prototypes before build commitment, ship working software in short iterations, and review progress against clear milestones.
What quality assurance measures do you have in place?
Every release passes automated test suites, integration checks, and—where AI models are involved—structured evaluation benchmarks against real edge-case documents and prompts prior to production deployment.
What kind of support do you provide after deployment?
We stay with the system after launch—handling cloud operations, security patching, model updates, and new workflow features as your business grows. Many of our client engagements span more than ten years.
Do you run discovery and workshops on site?
Yes. For Calgary and Alberta clients, we travel to your office, plant, or job site for walk-throughs, stakeholder interviews, requirements workshops, prototype reviews, and go-live training. We have done this many times, for organizations from large enterprises to startups. Build work in between runs remotely, with regular demos of working software.
Talk with us about a project.
Describe what you need built, integrated, or supported. We will reply with a practical next step.