Accelerating physical product development.

Designing a physical product is fundamentally a multi-objective optimization problem. Engineers must balance competing objectives such as performance, reliability, weight, cost, and manufacturability, all while satisfying technical and physical constraints. Improving one objective often comes at the expense of another. The goal is to explore the design space and move toward the Pareto frontier, where improving one objective requires compromising another.

In practice, this optimization happens through continuous feedback loops. Design engineers revise geometry based on CAE results. Manufacturing engineers identify production constraints. Suppliers introduce new information about materials, costs, and lead times. Even after a product enters production, quality issues, field failures, and cost-reduction opportunities continue to drive improvements.

Physical product development is therefore not a linear process from requirements to finished products. It is a continuous cycle of design, analysis, experimentation, feedback, and refinement.

The speed and quality of this optimization depend heavily on how quickly engineers can obtain and act on useful information.

Yet this process faces several bottlenecks. Engineers spend significant time coordinating across design, simulation, manufacturing, and supply-chain teams. Specialists have limited bandwidth and must decide which analyses to run, which assumptions and boundary conditions to use, and which experiments will provide the most useful information. These decisions often require reviewing previous designs, simulation results, technical documentation, and lessons from past projects.

Much of this information already exists, but it is scattered across different systems and teams. CAD and PLM systems contain designs and revision histories. Simulation tools hold performance predictions. Manufacturing and ERP systems contain production processes, material costs, and supplier data. Test reports, internal discussions, and engineering logs capture years of experience that may never become easily accessible to future engineers.

As a result, engineers repeatedly spend time searching for information, reconstructing previous decisions, and consulting specialists across departments. Important constraints often become apparent only after a design has progressed downstream, when changes are more expensive and require additional iterations.

There is a useful way to think about this problem through information theory: not all information is equally valuable. Its value depends on how much uncertainty it removes from a decision.

An engineer doesn't necessarily need hundreds of historical simulation reports. They may need one report explaining why a similar design failed, or one manufacturing record showing why a particular tolerance was prohibitively expensive.

Likewise, an engineering iteration is valuable not simply because another simulation was run or another design was tested, but because it provides information that helps determine what to do next.

This suggests two ways to accelerate physical product development: reduce the time required for each iteration, and increase the useful information gained from it.

We believe improving the flow of engineering knowledge can help achieve both, allowing engineers to explore the design space faster and make better-informed trade-offs at every stage.

Every product an organization designs, simulates, manufactures, and tests generates valuable knowledge. A failed prototype reveals a design constraint. A manufacturing issue exposes a costly feature. A supplier suggests a better material. A successful design establishes an approach that can be reused.

This knowledge should not remain buried in project folders, isolated systems, or the experience of individual engineers. It should inform future decisions.

When an engineer considers changing a component, they should be able to understand how similar designs performed in the past, what manufacturing problems were encountered, which alternatives were explored, and why earlier decisions were made. When evaluating a new design, they should be able to draw on relevant test results, manufacturing experience, material costs, and lessons from previous products.

Engineering knowledge generated anywhere in the product lifecycle should be available wherever it can improve an engineering decision.

The goal isn't to overwhelm engineers with more information. It's to make the most relevant information available at the right time, helping them reduce uncertainty, evaluate trade-offs earlier, and avoid repeating work that their organization has already learned from.

Engineers already work with an extensive set of specialized software. These tools are powerful, and organizations have invested years building workflows around them. We don't want to replace these systems or introduce another complicated platform engineers must learn to use.

We're building Adjoint to work alongside existing engineering tools, with minimal setup and almost no change to how engineers work.

Our approach focuses on three core capabilities: connectivity, contextual understanding, and engineering intelligence.

Connectivity is the foundation. Adjoint is being designed to integrate with existing CAD, PDM, PLM, simulation, ERP, manufacturing, testing, and other systems, making engineering knowledge accessible without requiring organizations to move their data into an entirely new workflow.

But connecting information is only the first step. Contextual understanding means recognizing how designs, components, revisions, requirements, analyses, manufacturing processes, and past decisions relate to one another. By connecting these relationships, Adjoint aims to understand which information is relevant to the specific product and engineering problem at hand.

This enables contextual engineering intelligence: bringing relevant historical examples, technical constraints, cost considerations, and potential downstream effects directly into an engineer's existing workflow. Rather than searching across multiple systems, engineers should be able to access the context they need as they make decisions.

We're also exploring specialized and fine-tuned models across engineering data, from CAD geometry and simulation results to manufacturing records and tabular data, to maximize the accuracy, relevance, and quality of the insights Adjoint delivers.

Trust, transparency, and simplicity are central to our approach. Insights should be traceable to their sources, uncertainties should be clear, and organizations should retain control over how their engineering data is accessed and used. We're designing for flexibility in AI model selection, including cost-efficient open-source and specialized models, to accommodate different requirements for privacy, cost, and technical performance.

The goal isn't to replace engineering judgment. It's to give engineers better information with which to exercise it.

Consider an engineer developing a new mechanical assembly. Adjoint could identify similar past designs, relevant engineering changes, previous failures, and the decisions that resolved them. Instead of repeating an old investigation, the engineer starts with the organization's accumulated experience.

Or consider a CAE engineer evaluating a revised design. Adjoint could bring together previous simulations, physical test outcomes, model assumptions, and known failure modes. This context could help the engineer decide which analyses are worth running, which boundary conditions deserve attention, and what additional evidence would be most useful.

Similarly, when a design engineer modifies a component, Adjoint could surface manufacturing constraints, material costs, tooling considerations, or supplier information that might otherwise only become apparent later in development. The engineer can evaluate these implications while alternative designs are still easy to explore.

The same approach could extend throughout the product lifecycle, helping engineers understand how design changes affect manufacturing processes, inspection requirements, supply-chain commitments, and costs, or how production experience should inform the next revision.

These are some of the workflows we're exploring as we develop Adjoint. They illustrate how information generated in one part of the product lifecycle could improve decisions made elsewhere.

We envision a future where engineers can draw on the full experience of their organizations without spending hours finding and connecting information. Where manufacturing experience informs design, physical testing informs simulation, and lessons from previous products become useful inputs to new engineering decisions.

That's what we're building toward at Adjoint: making every engineering iteration more informed, every decision better supported, and physical product development faster.