For years, AI development tools have promised faster coding, smarter automation, and greater efficiency. But in highly regulated industries like aerospace and defense, where every line of code must be flawless, secure, and compliant, these promises have not only fallen short—they’ve introduced new risks.
The reality is that generic AI coding assistants aren’t just inadequate; they are actively dangerous.
Studies show that 52% of AI-generated code contains errors, leading to inefficiencies, technical debt, and potential system failures. Even more alarming, 40% of code generated by Copilot was found to have security vulnerabilities. Generic AI coding assistants can expose mission-critical applications to exploits and compliance violations.
Most AI coding assistants today function as black-box models that generate code by predicting patterns—not by reasoning about engineering principles, security requirements, or mission-critical software architecture. These tools are appropriate for no-code or low-code citizen developers and small teams who are building MVP applications from scratch—not for the teams responsible for the software and embedded systems that power fighter jets, missile defense systems, military satellites, and classified networks.
Tabnine is not just another AI coding assistant—it is the first AI Software Development Platform engineered for the rigorous demands of aerospace, defense, and security-conscious industries.
The world’s largest aerospace and defense companies, along with multiple government agencies, have already chosen Tabnine because they understand that AI cannot be an unchecked, unpredictable tool in mission-critical environments. It must be a structured, secure, and reliable extension of their engineering teams—enforcing precision, security, and compliance at every stage of the software development lifecycle.
Fully Private, Air-Gapped Deployments
Unlike generic AI tools that expand attack surfaces and expose sensitive data to third parties, Tabnine is fully private, secure, and controlled by your organization. It can be deployed on-prem, in a private VPC, or even air-gapped—ensuring that proprietary data stays within your secure perimeter.
Model Flexibility and Architectural Freedom
Tabnine’s platform is model-agnostic, giving you the architectural freedom to use any set of LLMs and SLMs within your secure environment. Simply deploy your desired models inside your perimeter, and the Tabnine AI software development platform functions as a fully self-contained system—eliminating the risk of data leakage.
Code Quality and Compliance Built-In
What sets Tabnine apart is not just how it generates code, but how it validates and refines it. Unlike other AI tools that produce code based on generic training data, Tabnine integrates directly into your active repositories, documentation, and engineering workflows. Tabnine’s AI recommendations are contextually aware, structurally sound, and validated against your organization’s security and compliance standards.
Enterprise Context Engine and Human-in-the-Loop AI
Tabnine’s Enterprise Context Engine and AI agents enhance the output of any model, delivering production-ready code that meets your engineering standards.
The Context Engine executes real-time knowledge enhancement using a sophisticated Retrieval-Augmented Generation (RAG) architecture. It leverages re-rankers, embedding models, and code-specific semantic, vector, graph, and agentic RAG to ensure AI-generated code reflects your organization’s standards and best practices.
Tabnine’s contextually aware human-in-the-loop AI agents ensure that developers remain in control. Every AI-generated suggestion includes a diff, an explanation with references, and requires developer approval before being applied.
Tabnine supports over 600 languages, libraries, and frameworks out of the box. But what sets it apart for aerospace and defense is its ability to handle specialized, often poorly documented languages used in mission-critical applications. Engineering teams are already using our AI agents to augment their workflows in developing telecommunications payloads for satellites, embedded systems and control algorithms for avionics systems, multi-mission air defense systems, and digital integration suites across military and security domains.
High-Reliability Real-Time Languages
•Ada – Avionics, missile guidance, and command systems
•C & C++ – Radar, missile systems, and UAVs
•SPARK – Military-grade cybersecurity and classified systems
•Assembly – Low-level military firmware and cryptographic hardware
AI, Machine Learning, and Cybersecurity
•Python – Autonomous systems, cybersecurity, drone control
•Rust – Memory-safe military software and secure embedded systems
Secure Hardware and Embedded Systems
•Verilog/VHDL – FPGA-based cryptographic systems and secure processors
Legacy Code and Systems
•COBOL – Legacy military and government systems (e.g., logistics and payroll)
The codebases with data on these languages must remain fully private, thus exposing them to third parties and compromising data sovereignty is not an option. Addressing out the box LLM performance for these use cases through the creation of synthetic training data isn’t viable either. This makes Tabnine’s support for secure fine-tuning a powerful advantage for our customers.
Fine-tuning increases the specialization of the underlying LLM or SLM, making it more effective at understanding and generating language-specific syntax, patterns, and logic.
Take COBOL as an example. Fine-tuning a private model on COBOL would help a model: learn COBOL’s structured and verbose syntax, recognize legacy coding patterns (e.g., batch processing and record-based file I/O), better predict the code based on context, suggest more efficient refactoring, improve code translation performance and modernization efforts between COBOL and more modern languages like Java or Python.
Bring Your Own Model – If you have internal model development and fine-tuning efforts underway, Tabnine can integrate them directly into your engineering workflows.
Private Fine-Tuned Models – If you don’t have internal fine-tuning efforts, Tabnine can fine-tune private code completion models for your specific use cases and engineering needs.
In each case the platform operates as a fully self-contained solution and Tabnine gives you complete control over your model mix with 3 unique capabilities:
1. Architectural Freedom: Deploy fine-tuned models and connect them into your Tabnine platform.
2. Model Governance: Determine what models are approved for usage in your organization.
3. Model Flexibility: Engineers can select the appropriate model for each task directly from their IDE in real time using our model switcher drop down.
When any model is deployed inside Tabnine’s AI software development platform every query is knowledge enhanced with accurate real-time data from your organization by Tabnine’s context engine. The context engine uses 4 levels of progressive personalization to enhance the output of every LLM or SLM you deploy, for every agent and task in your SDLC, with organizational knowledge bases, standards, and policies.
The Tabnine Context Engine ensures our AI code completions, IDE chat, and human-in-the-loop SDLC agents deliver accurate, secure, and compliant code enabling engineering teams to scale their delivery without scaling technical debt and vulnerabilities.
•80% increase in code acceptance rates from agent and chat interactions
•36% lift in code completion acceptance rates
Tabnine is being used today by thousands of engineers in aerospace, defense, and government organizations around the world to build mission critical systems. It is a fully integrated platform that learns, adapts, and enforces your organization’s security policies, coding best practices, and compliance requirements. By leveraging RAG, security guardrails, and governance frameworks, Tabnine ensures that every line of AI-generated code is contextually accurate, security-validated, and standards-compliant before it leaves an IDE.
Unlike black-box AI models that force organizations to blindly trust their outputs, Tabnine provides full transparency and control over how AI is used in development. Teams can define strict security fences, enforce predefined compliance rules, and ensure that AI-generated code meets their exacting standards. The result is not just AI-assisted development—it’s AI that strengthens engineering teams, augmenting their SDLC without introducing risk.
Join us every Wednesday for our weekly live stream “Tabnine Office Hours” where we educate hundreds of engineering leaders on effective ways to use AI, answer their questions, address their concerns, and give them a live interactive demo of our platform. If you’d like a private one-on-one overview or want to explore evaluating Tabnine contact us to begin your AI Adoption journey. We’ll guide you towards a customized solution that’s tailored to your unique needs, set up a proof of value, and provide your engineering teams with training on how to make effective use of AI.
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