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From model to product — systems rigor scaled to real-world complexity
Cross-domain expertise for end-to-end systems
Layered and component-based architectures for systems spanning user interfaces, real-time control loops, and hardware drivers. Includes latency budgeting, interface contracts between layers, and selection of communication protocols (DDS, CAN, UART) at the system model level before a line of code is written.
Design and validation of acquisition chains, filtering pipelines, and detection algorithms for radiation detection (Medscint), photonics (Bliq), and battery management (Volthium). Algorithm correctness is verified against system-level requirements before hardware integration.
Hardware description, timing closure, and co-design decisions between FPGA logic and host software. System models capture the latency, throughput, and resource constraints that drive HDL architecture choices.
Bare-metal and RTOS-based firmware for STM32, PIC, and similar targets. System-level requirements drive interrupt priority schemes, memory layout, and safety partitioning — not the other way around.
Schematic capture, power budgets, signal integrity analysis, and PCB layout review. Electronic design constraints are modeled as parametric requirements (voltage rails, thermal limits, EMI margins) visible across the full system model.
A single architect across software, firmware, FPGA, and electronics means your system model is the source of truth — not the gap between your contractors' deliverables.
Complex systems can't be held in your head
A system with 5 components and 3 interfaces per component has 225 possible interaction pairs. At 10 components, that's 4,050. Mental models break down well before that scale — and that's where undocumented assumptions become integration defects.
Rushing into implementation without system-level architecture leads to rework, integration failures, and cost overruns. The classic cost-of-change curve shows defects found at integration cost 10-100x more than defects caught at the requirements stage.
Systems engineering is proportional: a 4-person startup building a v1 sensor prototype needs a 1-page interface control document and a 20-block SysML structure diagram, not a 300-page MIL-STD-810 test plan. Paul calibrates the rigor to the real risk.
A radiation detector has an FPGA acquisition chain, a real-time firmware layer, a host-side signal processing pipeline, and a calibration database. Without a system model, the team discovers at integration that the FPGA's timestamp resolution does not match the host's expected timing protocol. Fixing this requires reflashing the FPGA bitstream, updating the firmware driver, and revalidating the signal processing pipeline — a two-week slip caused by an assumption that was never written down.
SysML v2, digital twins, and model-based verification
A textual, version-controllable modeling language replacing the graphical-only SysML v1. Requirements, structure, behavior, and parametric constraints live in a single model. Eliminates the 'PowerPoint architecture' problem where system designs exist only as slides that go stale the moment implementation begins.
Executable system models that simulate physical behavior (thermal, optical, electromagnetic, mechanical) before hardware exists. A battery management system's thermal model can validate cooling requirements against a cell chemistry model in simulation, catching design violations before the first PCB spin.
Requirements and test cases are linked in the model. When a requirement changes, the affected tests are identified automatically. Eliminates the manual traceability matrices that teams maintain in spreadsheets and inevitably let go stale.
Specialized agents parse stakeholder inputs and decompose them into structured SysML v2 requirement elements with measurable acceptance criteria. Gaps, ambiguities, and conflicting requirements are flagged before the architecture phase begins, when they cost the least to resolve.
Every requirement is linked from its source stakeholder need, through the architecture decision that satisfies it, down to the test result that verifies it. Regulatory audits (FDA, ISO 26262, DO-178C) become a report export, not a weeks-long documentation scramble.
These tools are not academic exercises — they reduce delivery risk and protect your budget on complex projects.
70+ specialized agents. PhD-level rigor. Startup delivery speed.
Specialized agents parse stakeholder inputs (interviews, RFPs, regulatory standards) and decompose them into structured SysML v2 requirement elements with measurable acceptance criteria. Gaps, ambiguities, and conflicting requirements are flagged before the architecture phase begins.
A requirements set that would take a senior SE two weeks to structure and validate is produced in two days.
An orchestration pipeline maintains live traceability links from every SysML requirement element through its satisfying architecture block, down to the source code module and the automated test that verifies it. When a requirement changes, the pipeline identifies every downstream artifact that is now stale.
Eliminates the spreadsheet-based traceability matrices that compliance teams dread maintaining.
Before a design decision is committed, an agent checks it against the full requirement set for violations: does this interface data rate satisfy the latency requirement? does this power budget satisfy the thermal envelope? does this state machine cover all specified failure modes?
Architectural errors that typically surface at integration review surface instead at the model commit stage.
Multi-domain system models accumulate inconsistencies as each domain evolves. An orchestration agent runs cross-model consistency checks: interface port types match between the block diagram and the firmware driver definition; timing assumptions in the sequence diagram match the FPGA timing model.
Inconsistencies are caught before they propagate to hardware.
For exploring architectural trade-offs, agents generate candidate SysML v2 model fragments for review. A team evaluating three interface architectures can have all three modeled, parametrically analyzed, and compared against requirements within a single working day.
Traditional manual modeling would require a week per candidate architecture.
Agents derive test cases directly from SysML requirement elements, covering nominal paths, boundary conditions, and specified failure modes. Test vectors are generated for both software unit tests and hardware-in-the-loop scenarios.
Verification coverage gaps (requirements with no associated test) are reported automatically.
AI agents amplify throughput and catch mechanical errors — they do not replace engineering judgment. Architectural decisions and domain expertise remain Paul's responsibility.
Four SysML/UML diagram types that structure design, architecture, and verification
LLM + diagram-as-code: SysML diagram generation and iteration drops from days to seconds — up to 1,000× faster.
Maps external actors (operators, third-party systems, automated processes) and their interactions with the system — capturing the functional scope before any architecture decision is made.
Aligns all stakeholders on scope in Sprint 1, eliminating the misunderstandings that generate costly rework during integration.
Concrete example
Multi-sensor acquisition system: actors Operator, Maintenance Technician, and Automated Calibration interact with use cases Acquire Data, Trigger Alarm, and Calibrate Sensor.
Models the structural architecture: component blocks, their typed properties, interface ports, and composition associations between subsystems.
Makes interface incompatibilities (voltage levels, protocols, timing constraints) explicit — left implicit, these become defects discovered at the worst moment: hardware integration.
Concrete example
Embedded measurement platform: IMUSensor block with SPI 10 MHz port, ProcessingUnit block with latency ≤ 2 ms constraint, and DMA bus connecting both subsystems.
Illustrates processing flows with decision points, parallel paths, and synchronization bars — ideal for FPGA state machines, firmware pipelines, and startup sequences.
Validates complex control logic before RTL or firmware implementation, catching race conditions and missing states without hardware synthesis.
Concrete example
FPGA pipeline: Data Acquisition → FIR Filtering (parallel) → Threshold Detection → [Alarm | DMA Transfer] → Status Register Update.
Shows time-ordered interactions between hardware and software components, including synchronous calls, asynchronous responses, and protocol timing constraints.
Validates communication protocol design, critical timing windows, and detects race conditions before any development begins.
Concrete example
Sensor polling: MCU → [SPI CS low] → ADC → [2 µs conversion] → DMA → [Memory transfer] → Interrupt trigger → Sample processing.
Scientific rigor. Agile delivery. Concrete results.
Ph.D. in Physics (nanophotonics)
Université Laval
15+ years cross-domain experience
Bliq, Volthium, ABB, LR Tech, Medscint
Agile-compatible
Iterative models, not waterfall
Single point of accountability
From model to firmware to PCB
70+ specialized AI agents
SE rigor at startup speed
Software development and systems engineering are inseparable in multi-domain projects. Complex software built without a system model produces integration failures discovered at the worst possible moment — when hardware exists, deadlines are tight, and the cost of change is at its peak. This is not a preference; it is a structural fact about how multi-domain system complexity scales.
Paul's approach is agile-compatible. Model artifacts are sized to the sprint and the risk: a critical hardware interface gets a detailed parametric model in Sprint 1; a lower-risk software module gets a block-level description in Sprint 3. Stakeholders review model increments in sprint reviews, not in waterfall gate meetings.
Paul's agentic AI methodology — 70+ specialized agents, 20+ orchestration prompts — compresses what traditionally takes months of manual SE work into weeks, without reducing traceability, verification coverage, or model quality. This synthesis of PhD-level discipline and AI-augmented tooling is what distinguishes Innovation Codotek from generalist SE consultants.
Software development and systems engineering go together — one without the other is optimism with a price tag.
— Paul Brûlé Bareil, Ph.D.
Let's talk about your system's complexity and the best way to approach it.