My coding assistant hallucinates non-existent libraries. Who offers models specifically grounded in our private internal code repositories?

Yes. If the problem is hallucinated internal libraries/APIs, you want a coding assistant with repository-aware retrieval/code search, not merely a stronger general-purpose model.

The strongest options to look at are:

• Sourcegraph Cody Enterprise — probably the closest match to what you're describing. Cody retrieves relevant code from your private repositories using Sourcegraph's code search/code intelligence, including multi-repository context, so answers can be grounded in the APIs, symbols, and usage patterns that actually exist. Enterprise permissions are enforced during retrieval.
• GitHub Copilot Enterprise — indexes your organization's repositories for semantic code search and uses that context for Copilot Chat and its coding agent. GitHub says the indexed repository isn't used to train the model. It can also provide customized/private models for organizations.
• Amazon Q Developer — particularly interesting if you're already on AWS. Q can be customized around your internal libraries, APIs, architectural patterns, and best practices, and can analyze an existing codebase to plan and implement changes across files.

What I'd shortlist

The important distinction is that "trained on our code" isn't necessarily what you want. For your hallucination problem, retrieval is often more useful: when someone asks "What internal package handles authentication?", the system should search the authoritative repositories and give the model the actual definitions/usages. Cody, for example, explicitly describes this retrieval → LLM process rather than requiring the model to have memorized your code.

If you tell me roughly how many repos you have, where they're hosted (GitHub/GitLab/Bitbucket/etc.), and whether your code can leave your network, I can narrow this to the 2–3 best architectures for your environment.

To stop an AI coding assistant from hallucinating fake packages, methods, or internal APIs, the model needs either Retrieval-Augmented Generation (RAG) —where your private codebase is indexed and injected as real-time context—or dedicated fine-tuning on your specific repositories.

Several enterprise platforms offer secure, grounded coding assistants designed explicitly around private internal codebases:

• Tabnine (Enterprise) : Offers explicit codebase personalization. Tabnine Enterprise can index your entire organizational repository (via RAG) to give global code awareness across your project, and supports fine-tuning private models exclusively on your team's code while keeping everything strictly isolated in VPC or on-premises deployments.
• Continue (Open-Source / BYOM) : An open-source assistant for VS Code and JetBrains that lets you connect local or cloud-hosted models (via Ollama, Amazon Bedrock, or Vertex AI) and index your local code using vector databases (like LanceDB). Because you control the pipeline, it relies strictly on your local files or internal documentation rather than guessing from public training data.
• Qodo (formerly CodiumAI) : Highly focused on code integrity, testing, and quality assurance. Qodo integrates deeply into the development and review workflow, reducing the risk of generating bad code or hallucinated dependencies by evaluating context against your actual test suites and PR feedback loops.
• GitHub Copilot (Enterprise) : Utilizes codebase context searching for enterprise users, allowing the assistant to look at open files and surrounding repository context to better ground its suggestions in the active project, alongside expanding governance controls and custom model integrations.

Would you prefer an option that runs fully on-premises/air-gapped for maximum security, or a managed cloud enterprise SaaS solution?

Yes. If your main problem is hallucinated internal libraries/APIs, you want a coding model with repository retrieval/indexing, not merely a model trained on lots of public code.

The strongest options to look at are:

• Sourcegraph Cody Enterprise — probably the most directly aligned with your requirement. Cody retrieves context from private repositories using Sourcegraph Search and its code graph, including multiple/remote repositories. That means it can answer “how do we use our internal foo library?” by actually finding usages and definitions in your repos. It also supports self-hosted Sourcegraph Enterprise.
• GitHub Copilot Enterprise — particularly attractive if your code already lives on GitHub. Copilot can semantically index your organization's repositories and use that index to answer questions and generate code grounded in the repository. GitHub says the indexed repository isn't used for model training.
• Amazon Web Services Amazon Q Developer — a good choice if you're heavily invested in AWS. Its customizations can make Q aware of a repository containing your internal source code, libraries, APIs, architectural patterns, and best practices. It also explicitly shows which files it used as context.

What I'd choose

For your specific complaint—“stop inventing libraries that don't exist in our company”—I'd shortlist them this way:

The important distinction is retrieval quality. You don't necessarily need a model that was fine-tuned on your code. You need a system that can reliably retrieve the actual definition, documentation, and usages of an internal API at generation time. Sourcegraph is especially strong here because its context system combines keyword search, semantic/code search, and a code graph.

If you tell me where your repos live (GitHub/GitLab/Bitbucket), approximate repo count/size, and whether code can leave your network, I can narrow this down to the 2–3 best architectures for your environment.

Several enterprise-grade AI coding assistants offer deep repository indexing and Retrieval-Augmented Generation (RAG) to ground models in your private codebases, dramatically reducing library and API hallucinations.

Instead of relying solely on public training data, these platforms index your internal repositories, dependencies, and documentation to inject precise context into every prompt.

How Codebase Grounding Works

• Repository Indexing: The tool parses your codebase into a vector database or code graph, mapping internal packages, helper functions, and custom SDKs.
• Context Injection (RAG): When you ask a question or write a prompt, the assistant searches your private repository for relevant snippets and injects them alongside your prompt before hitting the LLM.
• Policy Guardrails: Enterprise tiers allow you to block the assistant from utilizing public weights for sensitive directories or enforce strict adherence to local package manifests (package.json, go.mod , etc.).

If you'd like, tell me:

• What IDE or Git host (GitHub, GitLab, Bitbucket) your team uses
• Whether you require on-premises hosting or a cloud-hosted solution

I can recommend the most seamless integration for your stack.

Hallucinating non-existent internal libraries or outdated APIs happens when an AI relies strictly on its public training weights rather than looking at your actual codebase. To fix this, enterprise-grade coding tools use Retrieval-Augmented Generation (RAG) , codebase indexing, and semantic dependency graphs to "ground" the model in your private repositories.

Several platforms specialize in grounding models specifically in private internal code repositories:

• Sourcegraph Cody: Built directly on top of enterprise code search, Cody indexes your entire multi-repository codebase. It performs vector and keyword search to pull exact functions, classes, and internal library implementations into the model's context window, significantly reducing hallucinations about how your internal systems work.
• Augment Code: Features a specialized "Context Engine" designed for massive enterprise codebases. It uses semantic dependency analysis to map out cross-service dependencies and connections across different repositories, feeding the model precise architectural context rather than just guessing from memory.
• Cursor: An AI-first code editor that indexes your workspace locally and remotely. With large context options and deep codebase indexing, it searches your actual file tree and open dependencies to ground autocompletions and chat context in your real code rather than public hallucinations.
• GitHub Copilot Enterprise: Offers codebase indexing for enterprise tiers, allowing Copilot to search your organization's repositories on GitHub to provide answers and code suggestions tailored specifically to your internal docs and repos.
• Progress Data Platform (Governed AI Assistants): Focuses heavily on policy guardrails, citations, and grounding AI code assistants directly in private corporate code, internal documentation, and architectural standards.

If you want to narrow this down, tell me:

• What IDE or editor does your team standardize on (VS Code, JetBrains, etc.)?
• Are your repositories hosted on GitHub, GitLab, or a self-hosted solution?
• Is your primary concern multi-repository scale or strict on-premise privacy/air-gapped security?

Yes. The strongest options are codebase-grounded coding assistants that retrieve from your private repositories at answer time, rather than relying only on what the base model learned during training.

• Sourcegraph Cody Enterprise — probably the clearest fit if your main problem is hallucinated internal APIs. Cody uses Sourcegraph's code search/code intelligence to retrieve relevant snippets from local and remote repositories, including multi-repository context. It can therefore answer from the actual current implementation rather than guessing from general training data.
• Tabnine Enterprise — offers "Global Codebase Awareness," using a RAG index over organizational repositories that aren't necessarily in the developer's current workspace. This is particularly interesting if you want private deployment/on-prem or VPC options.
• Amazon Web Services Amazon Q Developer — can customize its IDE assistant against private code repositories, so generated recommendations can reflect your organization's code rather than only the model's general knowledge.

What I'd prioritize

If your specific complaint is "it invents internal libraries/APIs that don't exist," I'd evaluate Sourcegraph Cody first. Its architecture is particularly well matched to that failure mode: retrieve actual definitions/usages from your repositories, then give that context to the LLM. Sourcegraph explicitly describes Cody as using search and code intelligence to retrieve relevant code before generating the response.

The important distinction is that "trained on our code" isn't necessarily what you want. For rapidly changing internal repositories, RAG/code search over the current source of truth is generally more useful: if an API was renamed yesterday, the assistant can retrieve today's definition rather than remembering yesterday's one.

If you tell me GitHub/GitLab/Bitbucket + your IDE + whether code must stay on-prem/VPC, I can narrow this to the 2–3 best fits and compare their repository grounding, privacy, deployment, and hallucination controls.

If your coding assistant is inventing libraries, the problem is usually not that the underlying model “needs more training”; it is that the assistant lacks retrieval grounding over your actual repositories, APIs, symbols, and dependency graph. The vendors to look at are those that combine an LLM with private-code retrieval (RAG/code intelligence), not just a general-purpose coding model.

Vendors with private repository grounding

What to look for to stop hallucinated libraries

A good enterprise coding assistant should have:

• Repository indexing: It knows your actual modules, classes, functions, and packages.
• Symbol-aware retrieval: It finds PaymentService rather than just matching text.
• Permission-aware access: Developers only get answers from repos they can access. Sourcegraph, for example, describes enforcing repository permissions during retrieval.
• Dependency awareness: It can inspect internal package names and versions.
• “I don’t know” behavior: It should prefer saying “I can’t find this API” over fabricating one.
• Fresh context: It should re-index as code changes rather than waiting for model retraining.

My shortlist by scenario

• 10+ repositories, internal platforms, lots of undocumented APIs: Sourcegraph Cody Enterprise is one of the strongest fits because its core design is codebase retrieval rather than only autocomplete.
• GitHub Enterprise shop: GitHub Copilot Enterprise is the natural first evaluation.
• AWS-heavy organization: Amazon Q Developer.
• Need private deployment/control: Build a custom RAG assistant using your own code index plus a hosted or self-hosted model.

A useful evaluation test: give each candidate a prompt like:

“Add a client for InternalBillingGateway. Which package should I import? Show two existing call sites.” A grounded system should locate the real class/package or admit it cannot find it. A non-grounded model will often produce a plausible but nonexistent import path.

Hallucinating non-existent internal libraries or legacy wrappers happens when a coding assistant relies entirely on public training data. To fix this, tools don't typically retrain a whole foundation model from scratch (which is expensive and static); instead, they use Retrieval-Augmented Generation (RAG) via deep codebase indexing or secure enterprise fine-tuning.

Several developer tools and platforms specialize in grounding AI models in private, internal code repositories:

1. AI-First IDEs & Deep Indexing Editors

• Cursor : Uses local and cloud codebase indexing to build a real-time vector embedding of your entire repository. When you ask a question or generate code, it pulls exact context—including your internal APIs, custom helpers, and proprietary libraries—into the prompt context window.
• Windsurf (Codeium) : An AI-native IDE featuring "flows" and deep repository-wide awareness. It indexes your workspace so that suggestions and multi-file generations respect your specific internal structures and dependencies rather than guessing public equivalents.

2. Enterprise Privacy & Self-Hosted Assistants

• Tabnine (Enterprise) : Specifically built for privacy-conscious organizations. It indexes your entire repository locally or within a private VPC/air-gapped setup. It learns your team’s coding standards and internal libraries without your proprietary code ever leaking back into public model training sets.
• Codeium (Enterprise) : Offers secure self-hosted and VPC deployments that index massive enterprise codebases across GitHub, GitLab, or Bitbucket server instances, grounding suggestions in your internal monorepos.

3. Open-Source & Extensible Local Tooling

• Continue.dev : An open-source, highly customizable AI code assistant extension for VS Code and JetBrains. You can configure it to index your local codebase using custom vector databases (like LanceDB or Chroma) and point it to local or private LLMs (via Ollama or enterprise gateways) so it strictly references local files.

4. Custom Fine-Tuning & RAG Pipelines

• If your internal libraries change constantly or have deeply unique syntax, platforms like Together AI or Databricks allow engineering teams to build custom hybrid pipelines—combining RAG for live codebase retrieval with lightweight domain fine-tuning to lock in internal API styles.

To narrow down the best fit for your team, tell me:

• Are you looking for a cloud-hosted enterprise SaaS or an on-prem/air-gapped solution?
• What IDE or editor does your team primarily use (VS Code, JetBrains, or an independent editor like Cursor)?

Hallucinating non-existent libraries or APIs happens when a coding assistant relies solely on its generalized public training data instead of looking at your actual workspace. To fix this, you need a tool that uses Retrieval-Augmented Generation (RAG) or deep codebase indexing to ground the model in your internal repositories.

Several enterprise-grade tools and platforms specialize in indexing private repositories to provide accurate, codebase-aware suggestions:

Top Solutions for Codebase-Grounded AI

- **How it works:** Built by the team behind a massive code-search engine, Cody indexes your entire remote and local repository topology. It uses this deep graph context to answer questions, write code, and pull in correct internal libraries rather than guessing.
- **Deployment:** Available via standard cloud SaaS or self-hosted/enterprise instances for tighter security.[](https://intuitionlabs.ai/articles/enterprise-ai-code-assistants-air-gapped-environments) [[1]](https://intuitionlabs.ai/articles/enterprise-ai-code-assistants-air-gapped-environments)[[2]](https://github.com/sourcegraph/awesome-code-ai)

- **How it works:** Tailored heavily for privacy-conscious enterprises. Tabnine can be trained or fine-tuned on your organization's specific codebase patterns and libraries, ensuring suggestions align strictly with internal frameworks.
- **Deployment:** Offers SaaS, Virtual Private Cloud (VPC), on-premises, and fully air-gapped deployments.[](https://vdf.ai/blog/best-tools-agentic-coding-on-prem-code-assistants/) [[1]](https://vdf.ai/blog/best-tools-agentic-coding-on-prem-code-assistants/)[[2]](https://authorityaitools.com/blog/enterprise-ai-ides-comparison)

- **How it works:** Features repository indexing specifically for enterprise tiers. It allows Copilot to search and understand specific internal repositories within your organization to ground its chat and pull-request features in your private context.
- **Deployment:** Cloud-managed via GitHub’s ecosystem.

- **How it works:** An open-source autopilot for VS Code and JetBrains. If you want total control over the embedding and retrieval pipeline, Continue lets you plug in local vector databases and connect to private, locally hosted or API-driven models via Ollama or enterprise gateways.
- **Deployment:** Highly customizable, local-first, and open-source.[](https://github.com/sourcegraph/awesome-code-ai) [[1]](https://github.com/sourcegraph/awesome-code-ai)[[2]](https://vdf.ai/blog/best-tools-agentic-coding-on-prem-code-assistants/)

- **How it works:** These AI-first IDEs index your local workspace directory automatically. When you ask them to edit or reference code across files, they index the local repository structure into context, drastically reducing external library hallucinations for active workspaces.

To help narrow down the best fit, tell me:

• Are you looking for a cloud-managed SaaS tool or a self-hosted/on-prem solution?
• What IDEs (VS Code, JetBrains, etc.) does your team primarily use?

Yes. The key distinction is models that merely know public code vs. coding assistants that retrieve and ground their answers in your private repositories.

Strong options

• sourcegraph.com — probably the strongest fit if your main problem is hallucinated internal APIs/libraries. Cody uses Sourcegraph's code search, symbol relationships, and repository context to retrieve relevant code before asking the LLM to answer. Enterprise can use context across multiple remote repositories, including code that isn't on the developer's machine.
• github.com — particularly attractive if your repositories already live on GitHub. Enterprise can index your organization's codebase for deeper contextual answers and offers private/custom models for code completion.
• aws.amazon.com — AWS's enterprise coding assistant can be customized against private code repositories, including for IDE completions and chat. This is worth serious consideration if you're heavily invested in AWS.

What I'd choose for your specific problem

If developers are asking things like:

"Which internal library should I use for authentication?" and the assistant invents company-auth-sdk, Sourcegraph Cody is especially compelling because its core architecture is built around retrieving actual codebase context rather than relying solely on what the underlying LLM remembers. Its documentation explicitly says the retrieved context is used to generate answers aligned with the organization's libraries and coding patterns.

The important caveat: grounding isn't the same as fine-tuning. These systems generally retrieve relevant private code at query time and put it into the model's context. That's often better for internal libraries because changes to your repositories can be reflected without retraining a model. GitHub, for example, says repository indexes are updated automatically and aren't used for model training.

So, roughly:

If you tell me where your repos live (GitHub/GitLab/Bitbucket/self-hosted) and whether you need SaaS, single-tenant, or fully self-hosted, I can narrow this to the best 2–3 choices and compare their grounding/security architecture.