Why do we need Spark when we already have access to Microsoft Copilot and other general-purpose AI tools?

Executive summary

• It's about fit, not "better vs worse." Copilot = general-interest work; Spark = tuned for deep-tech R&D (advanced engineering, aerospace, electronics, energy, medical devices, and materials).
• Specialized data. Spark works with patents, publications, and company datasets, reformulating prompts per source to get relevant results.
• Engineer-first workflows. Agents like Research Matrix (comparison tables), Patent Analysis (Boolean queries + relevance checks), and Value Chain Analysis (supplier/application flowcharts) automate work that engineers usually do by hand.
• Different outputs. Copilot gives essays; Spark gives spreadsheets that engineers can use directly.
• Looking ahead. Spark will only diverge further, with topic spaces and more domain-specific agents in the pipeline.

In detail

On the surface, Spark and Copilot look alike: both search, summarize, and generate text. But their foundations and audiences are different:

• Copilot and similar tools serve general-interest use cases, where the deliverable typically is a report or slide deck.
• Spark is tuned for scientists and engineers in deep-tech R&D - advanced engineering, aerospace, electronics, energy, medical devices, and materials. It is not aimed at strategy teams, sales, or finance - and not at domains like pure software development, where the requirements differ entirely.

This is not about “better vs worse.” It’s about fit.

Data sources: Built for technical depth

Copilot's foundation is broad web search and productivity data. That's useful for general topics but too shallow for technical research.

Spark connects to specialized data sources that R&D teams rely on. For example:

• Scientific publications - latest research from preprints, journals, and conferences.
• Worldwide patents - essential for IP and competitive intelligence.
• Company databases - company websites (more than 20 million) and other dedicated business and market information that complements technical data.

But this is not just about access: Spark is designed to handle the nuances of each source: it reformulates prompts into multiple source-specific queries and aggregates the results in ways tuned for R&D. A patent engine, for example, requires different operators than a publication index; researchers phrase concepts differently than company websites. Spark accounts for these differences so the output is both broader in coverage and sharper in relevance.

Audience: Designed for scientists and engineers

Copilot is optimized for general-purpose speed and convenience. By contrast, Spark is purpose-built for R&D, where the demands are different:

• Relevance over recall. Engineers and scientists quickly rule out "near misses." That’s why Spark leans toward stricter filters.
• Different time tradeoffs. Waiting a few extra seconds per query is fine if it eliminates hours of manual review.
• Context assumptions. Spark assumes technical users. This changes what sources are prioritized and how results are ranked.

Agents

Spark's agents are purpose-built to mirror how R&D teams actually work. Instead of generic chatbots, they embed into engineering workflows - structuring data, applying domain-specific logic, and delivering outputs in the formats that scientists and engineers use to make decisions.

The Research Matrix

Most research tools output essays. But engineers don't make technical decisions by reading essays - they build comparison tables. Until now, those tables were built manually: defining rows and columns, collecting data for every cell, filtering for relevance, and summarizing results. It was slow, error-prone work.

The Research Matrix agent automates that process. It takes a broad research question and structures it into rows and columns. Each cell is handled by a dedicated AI research agent that pulls targeted results, complete with source snippets and links back to the original material.

With the research matrix, you can:

• Break down complex technologies into systematic sub-problems.
• Compare competing materials or methods side-by-side.
• Evaluate suppliers against custom criteria.
• Build company lists on a topic and score them across the dimensions that matter.

Unlike chat transcripts, matrices are Excel-ready outputs. You can export them directly into your existing project spreadsheets, or share them with colleagues without reformatting. And when you add or change a row or column, you don't have to start over - Spark updates just what's needed.

This shift from unstructured text to structured, analyzable data highlights Spark's focus on R&D workflows, compared to more general-purpose tools.

Example:

One of our customers was asked to analyze a technology field for a business unit in their company. Using Spark’s research matrix, she produced a complete overview in just 10–15 minutes.

They were blown away with the output… 'How did you get all this information? How much time did it take?' And she said, 'Minimal time.'

Patent Analysis

Patent data is notoriously difficult to work with: queries require careful Boolean logic, relevance is highly context-specific, and the text itself is dense and legalistic.

The Patent Analysis agent addresses this directly:

• Search query generation - Spark converts a natural language topic into a structured patent search query, complete with Boolean operators, bracketing, wildcards, and relevant patent classes.
• Relevance assessment - Each result is evaluated for whether a PHOSITA (person having ordinary skill in the art) would consider it relevant.
• Technology and application mapping - From claims, abstracts, and descriptions, Spark highlights the key technologies and potential applications described in each patent.
• Continuity and sharing - Results are stored in your private data vault for reuse, and can be exported as Word documents for collaboration.

This makes patent analysis more systematic and far less time-consuming. Instead of hours spent learning query syntax, screening irrelevant hits, and parsing dense text, researchers can move quickly to identifying meaningful prior art and application areas.

Value Chain Analysis

Understanding technology isn't just about the invention itself - it's about how that invention fits into the broader ecosystem of suppliers, processes, and applications. Mapping that value chain has traditionally been a manual, time-intensive task, often pieced together from scattered sources.

The Value Chain Analysis agent streamlines this process:

• AI-generated flowcharts - For any technology or material, Spark generates a map of the value chain, from source through to end-use applications.
• Deeper exploration - Click on a node in the chain to explore suppliers, discover related technologies, and see current developments.
• Market alignment - By showing how a technology is used across applications, Spark helps teams identify high-impact opportunities and ensure technical development aligns with market needs.

This makes it easier to see not just what a technology is, but where it fits - who the suppliers are, which applications are emerging, and how upstream and downstream developments might shape future opportunities.

What about Copilot's Researcher Agent?

"I already use Copilot's Researcher (or Analyst) agent. Isn't that the same?"

The Researcher agent is useful: it can plan queries, pull from web and enterprise data, and produce narrative summaries. For many knowledge work tasks - trend spotting, policy analysis, internal strategy - that's enough.

But for deep-tech R&D, the differences matter:

• Specialized sources. Copilot Researcher leans on web and enterprise data. Spark integrates structured patent databases, scientific publications, and company datasets - tuned specifically for technical prompts and questions.
• Query structure and formulation. Researcher agents retrieve iteratively. But they don't automatically generate Boolean patent-class queries or parse patent claims - or translate your prompt into something that a scientific publications or a company database can understand. Spark does.
• Stricter relevance filters. Copilot Researcher and similar tools are optimized to generate long-form essays. Spark takes a different approach: its research matrix favors precision over filler. It will deliberately leave a cell blank rather than populate it with marginal or irrelevant results.
• Output format. Copilot Researcher tends to return essays. Spark delivers structured outputs - matrices, flowcharts, relevance tables - aligned with how engineers actually make decisions.

This is not a matter of "better" vs "worse." It's about fit: Copilot Researcher is great for strategy consultants, for example, where the deliverable typically is a report or a slide deck. By contrast, Spark is designed for deep-tech R&D - what PowerPoint is to the consultant, Excel is to the engineer.

Under development: Differentiation will grow

What Spark delivers today is only the beginning. The current state is the least differentiated Spark will ever be. With every release, the gap widens, because AI makes it possible to build tools that adapt more and more specifically to a user's world.

Upcoming capabilities include:

• Topic spaces - save and organize high-value answers under project labels.
• More targeted agents - expanding beyond research matrices, patent analysis, and value chain maps into other deep-tech workflows.

The direction is clear: Spark will not try to be everything for everyone. It will become more tailored to R&D, more context-aware, and more aligned with engineering workflows.

Looking ahead

Today Spark and Copilot may appear similar on the surface. But the trajectories are different. Spark is built around one principle:

Don't build one tool for everyone. Build the right tool for the right group.

• Lawyers will need AI tuned to legislation, codes, and case law.
• Finance professionals will need AI tuned to balance sheets, transactions, and market dynamics.
• Scientists and engineers in deep tech need Spark.

Over time, the differences will only grow. AI makes it possible to design tools that don't force the user into one-size-fits-all workflows, but instead adapt to the user's specific context, standards, and way of working - moving us away from the "cogwheel" world of Charlie Chaplin’s Modern Times.