How Altara Builds Trustworthy AI for Scientists and Engineers

The first generation of AI proved value in lower-stakes, digital workflows – drafting emails, writing code, and answering simple questions. But in the physical world, the bar is fundamentally higher. When an AI system suggests the wrong experiment, misinterprets inspection data, or incorrectly flags a failure, the consequences aren’t a bad paragraph or a broken UI that can be fixed with another quick prompt. The cost is wasted months of R&D, halted production lines, and multimillion dollar losses.

For scientists and engineers, accuracy isn’t a “nice to have.” It’s a fundamental constraint that determines whether AI can be used at all.

At Altara, we’ve built our platform around this core principle: AI for the physical sciences must be trustworthy by design, and we need to partner closely with our customers to ensure we’re delivering trusted results.

From Black Boxes to Glass Boxes

A common failure mode of modern AI systems is lack of transparency. Models produce output, but the reasoning behind that output is inaccessible or too long to easily interpret. That paradigm doesn’t work for scientific workflows.

Altara is designed as a glass-box system where every output is fully inspectable. For example:

• The exact SQL queries executed across underlying data sources
• The agent’s intermediate reasoning steps
• The transformations applied to raw data
• The analysis pipeline used to produce the final result

Scientists and engineers can trace conclusions back to first principles – verifying not just what the system said, but how it got there. They can also directly correct and modify analyses that occurred along this reasoning chain.

Citations as a First-Class Primitive

In scientific work, claims without sources are unusable.

Altara treats citations as a core system primitive, not a post-processing feature. Every insight is grounded in underlying data:

• Links to raw information (Excel sheets, Powerpoints, PDFs, etc.)
• Exact queries run and results pulled

This allows users to immediately validate outputs, cross-check assumptions, and build confidence in the system’s recommendations.

Deterministic Workflows Where it Matters

AI systems based on large language models are inherently probabilistic. The problem is, in scientific workflows, many components require determinism and repeatability.

Altara integrates these deterministic components as central building blocks within its agents, for specific scientific and engineering workflows. This hybrid architecture ensures the value of probabilistic AI for ambiguous tasks can be combined with the robustness required for core analysis pipelines.

Measuring Accuracy, Not Assuming It

General-purpose AI benchmarks are insufficient for domain-specific scientific work. At Altara, we therefore have invested in evaluation frameworks tailored to real-world use cases in science and engineering:

• Task-specific evals based on domain-relevant data
• Continuous measurement across new tasks and use cases

This allows us to quantify performance in the contexts that actually matter and systematically improve over time.

Code Generation for Reliable Execution

Models have been extensively used for writing code, and they’re often better at producing answers through code than through direct prediction.

For example, asking a model to multiply two large numbers with next token prediction may fail. But if it writes a function to multiply two numbers and then calls that function, the result is more reliably correct.

Altara leverages code execution as a mechanism for increasing accuracy:

• Models generate executable analysis pipelines
• Computations are carried out in controlled environments
• Results are derived from verifiable execution, not raw prediction

This is particularly important for improving accuracy in numerically intensive workflows.

Beyond Language Models: A Full Scientific Stack

Large language models are powerful, but they are not sufficient for the physical sciences. True scientific intelligence requires the ability to work across:

• Large scale time series data coming from sensors and instruments
• Images like SEM and inspection data
• Structured and unstructured experimental datasets
• Domain-specific simulation and modeling tools
• Messy spreadsheets missing units and column names
• Long-tail research documents, operator logs, and more

Altara combines LLMs with traditional machine learning models and domain-specific models, to ultimately unlock real results for complex scientific and engineering challenges.

Learning from Users in Real Time

Trustworthy systems need to do more than just perform well. They also need to improve quickly when they fail.

That’s why we work closely with our customers to ensure accuracy for their specific use cases. At Altara, we focus on closing the loop between user feedback and system performance:

• Users can flag incorrect or incomplete results, helping to encode tribal knowledge
• Feedback is incorporated into our evaluation methods
• Workflows are adapted to specific customer environments

Over time, Altara becomes increasingly aligned with the unique data, processes, and standards of each organization. This is critical in scientific domains, where complex edge cases are a day-to-day norm.

Building AI Scientists and Engineers Can Rely On

The promise of AI in the physical world is acceleration. Faster experiment cycles. Faster failure diagnosis. Faster paths from discovery to commercialization.

But none of that happens without trust.

At Altara, we believe trust is a property you must engineer into every layer of the system:

• Transparent reasoning
• Verifiable sources
• Deterministic execution where it matters
• Rigorous, domain-specific evaluation
• Multimodal intelligence beyond language models
• Continuous improvement through feedback

Ultimately, that’s what will enable scientists and engineers to bring the next generation of physical technologies into the world – faster than ever before.