Engineering execution platform

Engineering work at AI speed. Assessment-ready by default.

Regulations, standards, platform information, legacy code, models and datasheets feed into agentic engineering. Engineers review the resulting requirements, architecture, tests, impact chains, assessments and defect findings. IDE context and traceable evidence are also outputs.

AI agents assess readiness, find gaps and generate assessment reports.

Explore AI assessment All standards

For engineering teams in

Coding became faster.
Engineering coordination, traceability and assurance did not.

  • Engineering knowledge remains fragmented across documents and tools.
  • Requirements, architecture and tests evolve at different speeds.
  • Evidence and traceability are reconstructed late.
  • Coding agents need engineering context and clear review rules.
Six AI suites. One connected project.

AI for every part of
your engineering work.

Too much time spent searching standards, writing documents or preparing evidence? Choose the AI suite that helps with that work.

AI for Regulatory

Find the rules that apply to your product.

Spend less time searching standards. Get answers with references and link the relevant rules to your work.

Explore Regulatory Suite

AI for Assessment

Find the gaps before the assessment.

See missing evidence early and give engineers a clear list of improvements before the assessment.

Explore Assessment Suite

AI for Requirements

Write better requirements with less effort.

Turn customer needs into draft requirements. Spot unclear wording and conflicting needs before they create more work.

Explore Requirements Suite

AI for Testing

Know what to test when things change.

Draft tests from requirements, find missing coverage and see which tests a change affects.

Explore Testing Suite

AI for Architecture

Make design decisions easier to review.

Draft designs and check how components fit together. Keep the reasons behind decisions easy to find.

Explore Architecture Suite

AI for Engineering Context

Less searching. More time to engineer.

Find requirements, past decisions and project knowledge in one connected view, with links to the sources.

Explore Context Suite
The difference with KlugSpice

Faster engineering.
Measure the impact.

See where AI can reduce engineering effort, speed up change reviews and simplify assessment preparation.

Engineering workflows before and with KlugSpice, with a KPI for each comparison.
Engineering workBeforeWith KlugSpice
Engineering draftsMeasure: hours per approved taskWrite requirements, designs and tests by hand.Start with an AI draft.Requirements, architecture options and tests, ready for review.
Change analysisMeasure: hours per change reviewSearch documents and tools to find what a change affects.Find the impact faster.Review affected requirements, designs and tests together.
TraceabilityMeasure: requirements with expected links (%)Maintain links manually and check records for missing coverage.Spot missing links sooner.Follow connected requirements, designs, tests and evidence.
Assessment preparationMeasure: hours preparing evidence and reportsGather evidence, find gaps and write reports before the assessment.Build readiness alongside the work.AI checks evidence, flags gaps and drafts assessment reports.

Measure the improvement in a pilot, using the same scope and quality criteria.

Explore KPIs and estimate the value
How it works

Faster engineering.
Evidence at every step.

AI drafts requirements, explores architecture and prepares tests. Coding agents receive the context. Approved work returns to the tools.

Explore the platform
Engineering in motion
Project knowledgeStandards · Decisions · Tool data
Specialist AI agentsDrafts for review
Draft
requirementsExplore
architecturePrepare
tests
Task contextRequirements, interfaces
and test criteria
IDE coding agents
CursorClaude CodeGitHub Copilot
ReviewAI checks + engineer approval
Sync approved workBack to existing tools
Assessment throughoutTraceability · Evidence · Gaps & reports
An Automotive SPICE example

See KlugSpice in action.

Choose an industry and assessment framework. Then follow a battery warning task from agent execution to IDE handoff and reviewable evidence.

Explore the interactive examples
Read the walkthrough
  1. Create the project: A user opens Create project and explores industry-specific frameworks: Medical Devices with IEC 62304 for software lifecycle processes and ISO 13485 for quality management. The user then selects Automotive and Automotive SPICE, names the battery temperature warning project and creates it. The following engineering example uses the selected Automotive SPICE scope.
  2. Set the task: A battery temperature warning task starts in the KlugSpice task interface. The Project Lead assigns work and the Context agent attaches project sources. SYS.1 captures the stakeholder need.
  3. System engineering: Across SYS.1, SYS.2 and SYS.3, agents capture the need, draft system requirements and compare system architecture options. An independent check requires a measurable timing limit. The illustrative project source specifies 100 milliseconds; Automotive SPICE does not prescribe this value.
  4. Software engineering: SWE.1, SWE.2 and SWE.3 connect software requirements, software architecture and detailed design. The software draft links back to SYS-119 and its sources. An engineer approves the illustrative baseline B1 before it is selected for the coding handoff.
  5. Context to IDE: The approved baseline B1 is visible from the start: SYS-119 and SWE-042, interface IF-12 v3, the 100 millisecond project constraint, verification criteria and source links. The context package moves into an IDE coding task. Cursor, Claude Code and GitHub Copilot illustrate destinations. A proposed warning-logic change returns for independent checks. Engineer review is still required; no agent approves its own output.
  6. Software verification: SWE.4, SWE.5 and SWE.6 cover unit verification, software component and integration verification, and software verification. Agents prepare criteria and cases, then connect illustrative execution results from existing test tools. Preparing a test is shown separately from executing it.
  7. System verification: SYS.4 and SYS.5 connect system integration and system verification to architecture and system requirements. An illustrative bench result of 112 milliseconds exceeds the 100 millisecond project limit. The failed result remains connected to SYS-119 and its verification case.
  8. Defect & impact: Problem resolution and change analysis connect the late warning to requirements, the scheduling design and affected tests. A scheduler delay is a hypothesis requiring confirmation. Agents prepare investigation and retest tasks; proposed revisions still need review.
  9. Readiness report: The Assessment agent prepares a readiness report for the selected scope. Source links, work products, traceability and review records are assembled alongside open findings. Retest evidence and a change review remain outstanding. The illustration is not a formal capability rating or a guarantee of assessment success.

Process labels are shortened for this illustrative V-model mapping. The example uses selected system and software processes from the VDA QMC Automotive SPICE 4.0 model. Automotive SPICE does not prescribe this development sequence. All task records and results shown are sample data.

Medical framework examples: IEC 62304: medical device software lifecycle processes · ISO 13485: medical device quality management.

Inside KlugSpice

Less searching. Clearer reviews. Familiar tools.

See how KlugSpice helps engineers find the right information, check AI drafts and keep approved work in your existing tools.

Context

Find what you need without rebuilding the picture.

Bring project information together, keep the sources visible and give each task the information it needs.

How we build context
Execution

Give reviewers the information they need to decide.

See how 21 specialists prepare and check the work, while engineers decide what gets approved.

How work gets approved
Your tools

Keep existing tools as the source of record.

Use the requirements, models, code and tests you already have. Agree the connections and return approved work to your tools.

Explore integrations
One platform, many standards

Built for products where
getting it right matters.

Every industry needs clear requirements, tested designs and supporting evidence. KlugSpice connects the work to the applicable standards.

Automotive

Connect vehicle requirements, tests and assessment evidence.

Physical AI

Keep robot requirements, safety decisions and tests connected.

Aerospace

Keep design decisions and verification evidence together.

Rail

Follow changes from system requirements through testing.

Space

Keep mission requirements linked to designs and evidence.

Useful answers

Questions engineering teams ask.

What is KlugSpice?

KlugSpice is an engineering execution platform for regulated product development. Specialist AI agents draft requirements, explore architecture options, prepare verification assets and connect traceable evidence. Engineers review the work, and approved updates return to existing engineering tools.

How is KlugSpice different from a coding copilot?

KlugSpice coordinates engineering work around the coding task: requirements, architecture, verification, traceability and assessment preparation. It passes approved requirements, interfaces and constraints to IDE coding agents such as Cursor, Claude Code and GitHub Copilot. Proposed code changes return for checks and engineering review.

What does assessment-ready by default mean?

It means building traceability, evidence and review records alongside engineering work. KlugSpice checks the selected assessment scope, highlights missing or weak evidence and prepares findings and reports. Readiness support helps teams prepare; it does not guarantee an assessment result.

Which engineering teams can use KlugSpice?

KlugSpice supports automotive, medical device, Physical AI and robotics, aerospace and defence, rail, industrial and space engineering teams. The industry, standards, project sources and review process define the workflow. Automotive SPICE and IEC 62304 are examples of the frameworks teams can work around.

Understand agentic engineering Compare the plans
Learn and apply

Engineering guides and insights

Practical reading for connected engineering work, from the first requirement to reviewable evidence.

All resources

Engineering guides

Work through system and software engineering examples, with practical review checklists.

What is an AI-native engineering execution platform?

Follow a changed requirement through project context, agent work and engineer approval.

Explore engineering standards

Find the frameworks relevant to your product and see how engineering work supports readiness.

Start with your work

See KlugSpice in action.

Tell us which task takes too much time. See how KlugSpice can help engineers prepare the work and review the result.

Request a trial licenseOne project. One workflow. Clear success criteria.
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