From complex problems to production software.
TechnoConception brings senior product judgment, architecture, and hands-on engineering together to ship dependable AI-enabled software.
6 signals → 3 senior decisions → 1 accountable outcome
- Business value
- People and workflow
- Existing systems
- Data and access
- AI suitability
- Operating constraints
Signals to resolve
- Business value
- People and workflow
- Existing systems
- Data and access
- AI suitability
- Operating constraints
Senior decisions
- Clarify value
- Architect boundaries
- Validate production
Production ready
One accountable path to production software.
Product · Architecture · Build · Operations
For ambitious teams whose product, architecture, and operations must work as one.
The work is strongest when a difficult initiative needs senior judgment, hands-on implementation, and production accountability in one relationship.
01
An AI opportunity has no production architecture.
The prototype is promising, but data access, evaluation, privacy, reliability, and integration are unresolved.
02
The product roadmap has outgrown the team’s capacity.
A portal, SaaS platform, or internal product needs senior engineering without fragmenting ownership.
03
Software is harder to change than the business.
Architecture, delivery, or technical debt is blocking product progress and increasing operational risk.
14+ years
Product, architecture, cloud, SaaS, and applied AI under direct founder leadership.
CetaSpace
A first-party product that exposes the same product and system decisions we make with clients.
EN · FR
Bilingual commercial discovery and delivery from the first decision through production.
One engineering partner. Every critical workflow connected through production.
Move from an unresolved business problem to a dependable system without fragmenting product judgment, architecture, implementation, and operations.
Applied AI & Intelligent Workflows
Integrate AI where it makes a workflow or product measurably more useful, not where ordinary software is more dependable.
Custom Software & SaaS Engineering
Design and build the software your organization actually needs, from internal platforms to customer-facing products.
Architecture, Modernization & Technical Leadership
Make difficult technical decisions with senior engineering at the table and a viable path to delivery.
AI & Product Opportunity Sprint
Turn an ambiguous initiative into a decision-ready plan before committing to a larger build.
The interface is only the visible edge of the system.
Dependable products require the experience, permissions, data, integrations, AI behavior, delivery, and operations to agree. TechnoConception keeps those decisions in one architecture.
Product experience
Interfaces · workflows · identity
Product platform
Domain services · data · integrations
Production control
AI evaluation · security · observability
Boundaries before components.
Users, decisions, permissions, data flows, failure modes, and operating responsibility define the system before a stack is selected.
Operations are part of the product.
Monitoring, fallback, security, documentation, and ownership are designed with the experience, not appended after launch.
First-party engineering case
See the decisions behind a product we build ourselves.
CetaSpace is TechnoConception’s first-party engineering case. The dedicated case study shows real product surfaces in context, alongside the system boundaries, trade-offs, and operating lessons they support.
- Workspace boundariesIdentity, permissions, and tenant isolation remain shared concerns rather than duplicated application logic.
- Platform boundariesShared services support independently evolving product surfaces through explicit interfaces.
- Human controlAI-assisted operations keep approval, monitoring, fallback, and ownership inside the product model.
Production quality is designed in, not added at the end.
The product promise includes the conditions under which the system remains understandable, secure, testable, and operable.
Security and privacy boundaries
Identity, permissions, data handling, tenant boundaries, and external integrations are treated as architecture, not configuration cleanup.
Testable AI behavior
Evaluation cases, citations, human review, fallback, cost, latency, and unacceptable failures become explicit parts of the product.
Operational ownership
Monitoring, release controls, incident paths, documentation, and the responsibility to improve are planned before production.
Built on senior judgment. One relationship from discovery to operations.
The work stays connected from the first decision to the operating product. Each phase produces evidence for the next.
01
Discover
Define the problem, users, current workflow, constraints, and the decision that must be made.
02
Architect
Set system boundaries, data flows, trade-offs, risks, and a delivery sequence.
03
Build
Implement the complete product with testing, integration, security, and direct technical ownership.
04
Operate
Ship with monitoring, documentation, ownership, and a deliberate path to improve.
Not ready to commit to a build? Start by making the problem clear.
The AI & Product Opportunity Sprint turns an ambiguous software, workflow, or AI initiative into a decision-ready plan. It may conclude that buying, pausing, or not building is the stronger choice.
- Problem and workflow definition
- Systems, data, privacy, and security inventory
- AI-suitability and build-versus-buy analysis
- Target architecture and prioritized scope
- Delivery roadmap with explicit assumptions and recommendation
Senior judgment stays close to the work.
TechnoConception is led by Ludovic Chenneberg, a Montréal-based product engineer and technical leader with more than fourteen years of experience across software architecture, full-stack products, cloud delivery, SaaS, and applied AI.
14+ yearsProduct, architecture, cloud, SaaS, and AI
Direct relationshipThe person responsible for architecture remains involved through implementation and production decisions.
EN / FRCommercial discovery and delivery in English and French.
Technical reading index.
Practical writing on applied AI, product architecture, and the decisions required to move from prototype to production.
From AI prototype to production
A checklist for Québec teams moving beyond a promising demonstration.
RAG, agents, or ordinary software?
Choose architecture from the workflow and failure modes, not from the latest label.
What an Opportunity Sprint should deliver
The decisions and artifacts that should exist before implementation begins.
Tell us what is difficult right now.
Whether the answer is AI, custom software, modernization, or not building anything yet, we will begin by clarifying the decision.