Complex Systems • Software Engineering • Formal Methods • AI-Assisted Engineering
Formal Harness for AI-Assisted Systems & Software Engineering
AI-Assisted Systems & Software Engineering
A 3× improvement in speed and quality creates more value than a 20× improvement in speed alone.
⚠️ YOUR CONSTRAINTS
LLMs transform natural language into structured information and code. Agentic AI orchestrates prompts, tools, and actions. But systems and software quality requires more: a reliable design process that formally links needs, constraints, architecture, implementation, and validation.
✓ OUR SOLUTION
The Formal Harness provides the continuity, traceability, and consistency required for reliable and accelerated engineering execution. AI becomes part of a controlled design process, not an isolated assistant.
THE CRITICAL CHALLENGES FOR SUCCESSFUL AI-ASSISTED ENGINEERING
COST & RESILIENCE
- Control AI operating costs by reducing unnecessary context, prompts, and model calls
- Avoid vendor lock-in and pricing shocks with a resilient, explainable, and model-independent architecture
TIME-TO-MARKET
- Releases, delivery, and market adoption are the real objectives — not producing code 20× faster in isolation
- Enable fast setup, progressive onboarding, and easy take-off for engineering teams
QUALITY
- Keep engineering teams in control of product design, architecture, and implementation decisions
- Ensure every generated artifact, design decision, or piece of code can be justified against initial needs and constraints
SCALING
- Integrate new processes, viewpoints, and modules progressively as needs evolve
- Capitalize engineering knowledge so AI assistance becomes reusable, traceable, and adapted to engineering teams
IMMEDIATE BENEFITS FOR ENGINEERING TEAMS
Our approach combines the speed of generative AI with the rigor of formal engineering models.
Teams can start quickly and obtain immediate gains in quality, productivity and traceability.
Teams can start quickly and obtain immediate gains in quality, productivity and traceability.
Quality & Trust
- Insert AI into a formally controlled design process linking needs, constraints,architecture, implementation, and validation
- This is not a new experimental system: it accelerates a field-proven modeling platform that has been supporting complex systems design across automotive, aerospace, railway, energy, and other demanding industries for more than 20 years
Immediate Productivity
- Accelerate model construction through a step-by-step approach guided by the design process. Navigate and visualize the model efficiently to review, check, and refine engineering artifacts
- Experience shows that LLMs are highly effective when working with the Formal Harness model. AI can focus on the user’s engineering objective instead of spending effort understanding or producing complex tool-specific syntax
Formal Foundation
- Rely on single source of truth, data-centric modeling, consistent viewpoints, formal semantics, and programmable engineering rules
- Unlike purely agentic approaches, the Formal Harness is grounded in a formal model where engineering properties can be specified, checked, and preserved throughout the design process
Lower AI Operating Costs
- Reduce prompt complexity and context size by using the model as structured project memory. LLMs work on focused subsets of information, allowing intermediate open-source models to handle many tasks efficiently
- Frontier LLMs remain valuable for broad, up-to-date general knowledge. But most operations on the formal model can be performed efficiently by intermediate models, often at 20–30× lower cost
Sovereignty
- Treat the LLM as a commodity. Project memory, engineering knowledge, and reusable skills are managed by the fully open Formal Harness, not locked inside a specific model provider. All information can be retrieved and exported in the format you need
- Because intermediate LLMs are efficient for many model manipulation tasks, everyday engineering activities can run on a private, affordable AI stack deployed in your own cloud environment
Human Control & Ethics
- Engineers remain responsible at every stage: idea, specification, architecture, definition, implementation, and validation
- The result is not less human control, but more engineering leverage: teams can explore more solutions, build stronger rationales, and maintain traceability from needs to code or detailed design faster with a level of accuracy that was previously unaffordable
20 YEARS OF STRUCTURED ENGINEERING TECHNOLOGY NOW UNFOLDED WITH GENERATIVE AI
For more than 20 years, Knowledge Inside has developed a deeptech foundation combining flexible data modeling, single source of truth, data-centric engineering, viewpoint consistency, and clean formal semantics.
Historically, such models were built by trained systems engineers to progressively secure complex designs with accuracy and rigor.
Experience now shows that this foundation is particularly effective for LLM interaction. It allows generative AI to support reliable design processes in complex systems and software engineering.
By combining AI with KI models, engineers can produce in hours or days what formerly required months from experts: structured models, traceability, design rationale, architecture exploration, documentation, and links from design to implementation.
This still requires creativity, control, organization, guidance, and engineering experience. Humans remain the bottleneck and this is precisely why the process must maximize their leverage. Many activities that were previously too costly or cumbersome can now become business as usual.
This works because the underlying theory is clean, compact, and operational.
Historically, such models were built by trained systems engineers to progressively secure complex designs with accuracy and rigor.
Experience now shows that this foundation is particularly effective for LLM interaction. It allows generative AI to support reliable design processes in complex systems and software engineering.
By combining AI with KI models, engineers can produce in hours or days what formerly required months from experts: structured models, traceability, design rationale, architecture exploration, documentation, and links from design to implementation.
This still requires creativity, control, organization, guidance, and engineering experience. Humans remain the bottleneck and this is precisely why the process must maximize their leverage. Many activities that were previously too costly or cumbersome can now become business as usual.
This works because the underlying theory is clean, compact, and operational.
SINGLE SOURCE OF TRUTH
Requirements, constraints, architecture, definition, code, tests, remain connected inside a consistent formal model of the whole system
CONSISTENT VIEWPOINTS
Different teams and AI systems work on synchronized and consistent viewpoints of the same underlying holistic formal model
DATA-CENTRIC
Any document, result, artefact generated from the model is consistent by construction with the underlying formal model through a 360° API
RELIABLE AI EXECUTION
The Formal Harness provides the formal constraints required to safely use statistical generative AI in systems and software engineering
BUILT FOR TEAMS DEVELOPING COMPLEX SYSTEMS AND SOFTWARE
Our technology comes from two decades of model-based engineering, covering systems and software lifecycle processes, including configuration management, baselining, and ad hoc functional chains
Initial operational domains
Our technologies have been developed and validated in demanding complex systems engineering environments. Approaches formerly reserved for critical systems design can now benefit a much wider range of engineering and software applications
Fast setup
Teams can adopt these capabilities rapidly and obtain immediate gains in quality, consistency, traceability, maintainability, and productivity.
OUR EXPERIENCE IN BREAKTHROUGH PROJECTS
AUTOMOTIVE
First MBSE deployment on an EV program
RAILWAY
Digital transformation for SIL4 certification
SPACE
Modeling of complex tender responses
NUCLEAR
Digitalization of ESPN safety analyses
CONSTRUCTION
MBSE + BIM integration for nuclear facilities
AI is entering its infrastructure phase
As LLMs become easier to access and replace, strategic value is moving to the layer that makes them reliable in engineering and business operations.
The Formal Harness is our implementation of this layer, built on field-proven model-based engineering technology.
Let’s identify where the Formal Harness can bring immediate value to your engineering process.
On request — free of charge — no commitment
Samuel Boutin — Founder & CEO
20 years of expertise in technical information systems | Automotive • Rail • Construction • Nuclear • Space