Enterprise AI for Aerospace & Defense Engineering
An interface is only the visible layer. Behind it is the real challenge: indexing decades of engineering data across systems: secure, traceable, and governed. That’s where Sinequa fits: the knowledge layer empowering your AI, not another tool competing with it.
Wanting control over your AI, the IP, the roadmap, what it can reach, is exactly right for an environment like yours. But the interface was never the hard part. The hard part is everything behind it: connecting to PLM, ERP, requirements repositories, test systems, supplier documentation, and decades of program history, then indexing all of it at depth, security-trimmed, and traceable to source. Without that layer, even the best-built AI answers from whatever it can reach, which is usually the collaboration layer and not the engineering core.
That retrieval layer is a multi-year build on its own, connectors, indexing pipelines, permission enforcement, deployment inside classified and export-controlled environments. It’s exactly what Sinequa already does, in production, in environments as controlled as yours. So the question isn’t build vs. buy. It’s whether your team spends the next two years rebuilding the knowledge layer, or grounds what you’re building on one that already works.
Your AI is only as good as what it can retrieve. Sinequa makes your systems of record retrievable, so whatever you build on top of it can be trusted.
The interface is the easy part. These are the capabilities that decide whether what you build can be trusted.
Drawings, requirements, test and verification records, program documentation, and supplier data live in PLM, ERP, and document repositories, not the collaboration layer. Most AI can’t reach them.
A connector says “supported.” Real indexing means reading the engineering artifact itself including 2D/3D CAD and scanned technical documents, so an engineer can trust the answer, trace it to source, and act on it.
Document-level, attribute-based access controls inherited from your source systems and enforced on every query, across billions of documents. An answer never exposes what a user isn’t cleared to see. Full audit trail of any action.
On-premise, hybrid, or fully air-gapped. ITAR-ready. For classified and export-controlled programs, this is the requirement most platforms, and most internal builds, stall on.
Part numbers, requirement IDs, program nomenclature, MIL-SPEC and AS9100 terminology. Generic relevance and engineering relevance are not the same thing.
The knowledge layer feeds whatever sits on top, your interface, your agents, any model. A2A and MCP ready. You keep control of the front end; Sinequa keeps the retrieval reliable underneath it.
These requirements are drawn from the patterns we see across the industry’s most complex deployments. Read the State of Enterprise Agentic AI 2026 →
See the retrieval layer your AI would run on.
Watch how Sinequa indexes drawings, requirements, and test records across systems, security-trimmed, traceable, and ready for whatever interface or agent you put on top. Ready to see it in your environment?
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These are the multi-step questions your engineers ask every day, where the answer is buried across design, requirements, test, and supplier systems. Whatever AI you build has to answer them. That depends entirely on what it can retrieve.
Candidates returned with compliance documentation, design history, and supplier data in seconds.
Affected requirements, linked test procedures, and coverage gaps mapped across the program baseline.
Agent cross-references the matrix, identifies missing test cases, and drafts procedures for review.
Ranked, sourced, and traceable, directly relevant to program schedule decisions.
Backed by independent recognition from Gartner, Forrester, and SPARK Matrix™.
$0M
Productivity gains
$0M
Warranty avoidance
$0M
Development savings
Measured over three years.
NEW STUDY
The Forrester Total Economic Impact™ Of Sinequa AI-Powered Search
An independent Forrester Consulting study, built on a composite $20 billion enterprise, quantifies what a governed enterprise search and AI layer returns over three years: 299% ROI, $22.4M net present value, and payback in under six months. Based on interviews with five decision-makers across chemicals, manufacturing, life sciences, transport, and aerospace and defense.
With Sinequa, our engineers can ask a complex, multi-part question and get a concise answer plus the underlying evidence. That’s a game-changer for design, warranty, and failure analysis.
Oliver Scott Beard Principal Technical Architect – AI Systems, Cummins
Sinequa connects to 200+ enterprise applications and 300+ file formats, including 2D/3D CAD, with no custom coding. Connectors inherit and enforce your existing permissions, so every engineer and every AI agent sees only what they’re authorized to see. Deployable on-premise, hybrid, or fully air-gapped, with no dependency on external services.
A conversation about your knowledge architecture — where you’re already strong, and where a proven retrieval layer could be the exact accelerator you need. A working session on Northrop Grumman’s toughest knowledge problems.
Trusted by over 2,000 customers across 40 countries.
ChapsVision is a global leader in agentic AI and data solutions. Trusted by over 2,000 customers across 40 countries, ChapsVision delivers industry-specific software solutions powered by agentic AI to meet the complex needs of data-intensive sectors. Its innovative and scalable Data & AI platform is powered by proven technology enablers that accelerate data acquisition, preparation, and analysis.
Driven by significant R&D investments in large-scale data and AI and supported by a targeted strategy of acquisitions and international expansion, ChapsVision has rapidly established itself as the trusted partner in agentic AI for business and government organizations around the world.
