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

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.

Hallucinating non-existent internal libraries, methods, or internal APIs happens because standard LLMs rely on generic public training data . To fix this, you need tools built with deep codebase context and Retrieval-Augmented Generation (RAG) or deep repository indexing.

Several enterprise-grade coding assistants and platforms specialize in indexing and grounding models in private, internal code repositories:

• Sourcegraph Cody : Built by the company famous for large-scale enterprise code search, Cody uses an advanced codebase context engine. It searches your entire private repository graph (not just the open file) to ground its answers and autocomplete features in your actual internal libraries and APIs. Available in cloud and self-hosted options.
• Tabnine (Enterprise / Agentic) : Tabnine specifically targets the hallucination problem by allowing teams to isolate and ground models exclusively on their private code repositories and internal documentation. They offer SaaS, VPC, on-premises, and fully air-gapped deployments with a strict zero-data-retention policy.
• Augment Code : Specifically engineered for enterprise teams working in massive, highly interconnected codebases. Augment indexes entire multi-repository projects to provide deep contextual awareness, drastically cutting down on invented imports or phantom functions.
• Qodo (formerly CodiumAI) : Focuses heavily on code integrity, testing, and behavior alignment. Qodo indexes self-managed Git platforms and repository contexts to make sure suggestions respect local team rules, internal structures, and existing logic.
• GitHub Copilot Enterprise : If your organization is standardized on GitHub, Copilot Enterprise can pull context from your organization's internal repositories and documentation wikis to answer codebase-specific questions and provide grounded pull-request summaries.

To help narrow down which of these fits your setup, tell me:

• Are your repositories hosted on GitHub, GitLab, or a self-hosted/air-gapped server?
• Do you require a fully on-premises/isolated deployment , or is a secure cloud/VPC acceptable?

Yes. What you want is less “a model trained on our code” and more an enterprise coding model + retrieval/context engine that continuously grounds it in your private repositories. That distinction matters: it can reduce hallucinations about internal APIs without requiring the model itself to be fine-tuned on your source.

Strong options

• Sourcegraph Cody Enterprise — probably the clearest fit. Cody uses Sourcegraph's code search/code intelligence to retrieve relevant snippets from private repositories and supply them to the LLM. It can work across remote repositories, not just the files on a developer's machine. Enterprise also supports permission-aware retrieval, so users only get context they're authorized to read.
• Augment Code — built specifically around a codebase context engine. It indexes your workspace and uses your codebase's patterns, dependencies, and architecture to tailor suggestions. Augment says its enterprise context engine can span very large multi-repository codebases.
• GitHub Copilot Enterprise — a natural choice if your private repositories already live heavily in GitHub. Its advantage is integration with the GitHub development workflow and organizational code context, although I'd evaluate its retrieval depth carefully if your problem is specifically “the assistant invents internal libraries/APIs.”
• Cognition / Devin — worth evaluating if you're looking for a more autonomous coding agent rather than primarily an IDE copilot. Its enterprise offering is increasingly aimed at working across larger codebases and repositories.

For your particular hallucination problem

I'd shortlist Sourcegraph Cody Enterprise and Augment first.

The key evaluation isn't “which model writes the nicest code?” Run a bake-off with questions like:

1. “How do I authenticate against our internal Payments API?”
2. “Which package replaces foo-client in the new architecture?”
3. “Show me a real call site for InternalLibrary.X.”
4. Ask it about an API that doesn't exist and see whether it explicitly says so.
5. Make a change in a repository and test how quickly the assistant incorporates it.

The important architectural property is retrieval grounded in the authoritative repository, rather than hoping a general-purpose LLM memorized your internal code. Sourcegraph explicitly describes Cody's retrieval step as supplying relevant private-code snippets to the LLM, rather than relying on the model's pretrained knowledge.

If your repositories are hundreds of thousands of files / dozens or hundreds of services, I'd give Augment and Sourcegraph particularly serious consideration; context scale and cross-repository dependency understanding become much more important than the raw underlying model.

Sure—what would you like me to search the web for?

Hallucinating non-existent internal libraries happens because standard coding models rely on their general internet training data. To stop this, you need tools that use deep repository indexing and Retrieval-Augmented Generation (RAG) or private fine-tuning tied directly to your internal code.

Several enterprise platforms specialize in grounding AI models in private code repositories:

• Sourcegraph Cody : Leverages Sourcegraph’s core semantic code intelligence engine to index entire multi-repository codebases. It pulls exact file and line context into the prompt, citing real internal sources so you can verify the libraries and functions actually exist. It supports self-hosted enterprise deployments.
• Augment Code : Features a semantic Context Engine capable of indexing massive enterprise estates (up to a million files across multiple distinct repositories). It understands relationships between internal microservices, dependencies, and proprietary APIs without using your private code to train foundation models.
• Tabnine Enterprise : Allows organizations to train or fine-tune private models securely on their specific internal codebases. It learns your internal design patterns, proprietary frameworks, and custom libraries, and can run in air-gapped or on-premises configurations to keep your code completely private.
• Continue (Self-Hosted / Local Models) : An open-source, highly customizable IDE extension framework that allows teams to bring their own models (via local runners like Ollama or private endpoints) and configure custom RAG vectors pointing strictly to local or internal directory structures.

To help narrow down the ideal approach for your team, tell me:

• What is your approximate codebase size (number of lines/repositories)?
• Do you require a fully air-gapped/on-prem deployment, or is a secure cloud tenant with zero data retention acceptable?
• Which IDEs does your team primarily use?

Hallucinating non-existent internal libraries or phantom APIs happens when a standard coding model relies purely on its public pre-training data and lacks context-aware grounding in your proprietary codebase.

To eliminate this, platforms use advanced Retrieval-Augmented Generation (RAG) , deep semantic codebase indexing, or custom model fine-tuning restricted to your private repositories. Top enterprise-ready solutions offering this capability include:

• Augment Code : Features a specialized enterprise Context Engine that deeply analyzes hundreds of thousands of files across massive multi-repository codebases. It processes your internal dependencies as a semantic graph rather than raw text, significantly reducing architectural blindness and phantom package suggestions.
• Sourcegraph Cody : Uses Sourcegraph’s enterprise code intelligence backbone to semantically index your entire private repository structure. When Cody answers queries or writes code, it references your actual internal functions and yields direct line citations back to your private files so you can verify its logic.
• Tabnine Enterprise : Focuses heavily on privacy and security controls, allowing teams to securely fine-tune or personalize models on private internal code repositories and internal APIs. It can run via secure SaaS, single-tenant, or fully air-gapped on-premises setups so your proprietary code never mixes with public training data.
• Continue (with Self-Hosted/Local RAG) : An open-source, highly extensible assistant framework. It lets you connect local or frontier models to a self-hosted vector database indexing your local repositories, ensuring complete control over the data flow and strict network isolation.
• Amazon Q Developer (AWS environments) : If your private infrastructure relies heavily on AWS, Amazon Q draws directly from your cloud resources, CDK constructs, IAM roles, and internal service metadata to ground suggestions in your actual cloud architecture rather than guessing imports.

To help narrow down the best fit for your team, let me know:

• Are your repositories hosted on-premises (GitLab/GitHub Enterprise) or in the cloud?
• Do you require a fully air-gapped/on-prem deployment , or is a secure cloud tenant acceptable?

Yes. The strongest options I’d look at are:

• Sourcegraph Cody Enterprise — probably the most directly aligned with your problem. Cody uses Sourcegraph’s code search/indexing to retrieve relevant snippets from your private repositories, including multi-repository context, before asking an LLM to answer. It can search symbols, APIs, usage patterns, and code across your codebase rather than relying solely on the model’s pretrained knowledge.

• Tabnine Enterprise / Enterprise Context Engine — particularly interesting if you want private deployment and deeper organizational grounding. Its Context Engine builds a continuously updated model of repositories, dependencies, architecture, APIs, documentation, and organizational standards, including cross-repository relationships. It supports VPC, on-prem, and air-gapped deployments.

• Amazon Web Services Amazon Q Developer — a good choice if you're already heavily invested in AWS. Q can connect to private repositories and use company code to generate recommendations and answer questions about internal codebases. Its workspace context can also index the application's code, configuration, and project structure.

The important distinction

If by "grounded" you mean "don't invent an API that isn't in our repositories", I'd prioritize retrieval/context architecture over simply buying a model that was fine-tuned on your code.

A useful setup looks like:

private repos → code/index/search layer → relevant symbols/files/dependencies → LLM → answer/code + citations/verification

That lets the model consult the current source of truth rather than relying on what it learned during training.

For your specific hallucination problem, I'd shortlist Sourcegraph Cody and Tabnine Context Engine first. Cody is particularly strong at repository/code search; Tabnine is compelling if privacy, self-hosting, organizational context, and cross-repository reasoning are major requirements. Tabnine also explicitly offers model customization using an organization's code, rather than only retrieval.

If you tell me GitHub/GitLab/Bitbucket, languages, IDE, and whether code can leave your network, I can narrow this to the 2–3 best fits and compare them on grounding quality, hallucination resistance, deployment, and cost.

Yes. The strongest options I’d look at are enterprise coding assistants that retrieve from your repositories at inference time, rather than relying solely on what the base model learned during training.

• GitHub Copilot Enterprise — indexes your organization’s codebase for deeper repository understanding, and can ground Chat/agents in repository content. GitHub says indexed repositories are not used for model training.
• Tabnine Enterprise — particularly interesting if privacy/control is your priority. Its Context Engine indexes repositories, documentation and APIs, while its inference can run in your Kubernetes/private-cloud environment.
• Sourcegraph Cody — historically one of the most repository-centric choices, especially for large, multi-repository codebases. Its core strength is retrieving relevant code/context rather than expecting the model to have memorized your internal APIs.
• Other agentic IDEs + private-repo retrieval — products such as Cursor can work well, but I'd distinguish “the model can access my repo” from “the vendor has an enterprise-grade, continuously indexed knowledge layer for my private code.” Those aren't equivalent.

What I'd prioritize for your hallucination problem

Don't primarily look for a model that was trained on your code. Look for repository-grounded inference:

Your repo → code/semantic index → retrieval → model context → answer/code

That lets the assistant verify, for example, “Does AcmeAuthClient actually exist, and what methods does our implementation expose?” against the current repository instead of guessing from its pretrained knowledge. Research on repository-level coding similarly finds that incorporating repository context materially improves accuracy, particularly for proprietary/unseen code.

My shortlist:
GitHub-heavy organization → Copilot Enterprise
Maximum deployment/privacy control → Tabnine
Huge/multi-repo codebase and deep code search → Sourcegraph

One important caveat: grounding reduces hallucinations; it doesn't eliminate them. I'd also require the assistant to provide file/symbol citations and to say “not found in the indexed repositories” rather than inventing an API.

If you tell me whether your repos are primarily GitHub, GitLab, Bitbucket, or self-hosted, I can narrow this to the 2–3 best fits and compare their privacy, deployment model, repo indexing, and hallucination controls.

When an AI coding assistant hallucinates non-existent packages or methods, it's usually suffering from a lack of deep codebase grounding —it falls back on its generic public training data instead of understanding your proprietary layout, dependency graphs, and internal libraries.

No vendor trains a brand-new frontier foundation model from scratch exclusively on your private code; instead, the industry solves this via advanced Retrieval-Augmented Generation (RAG) , deep codebase indexing, and enterprise context engines.

Several developer platforms specialize in anchoring frontier models (like Claude, GPT-4o, or Gemini) directly into your private multi-repository environment:

• Sourcegraph Cody : Built directly on top of enterprise code intelligence and deep search infrastructure. Cody indexes your entire multi-repository ecosystem semantically. When you prompt it, it pulls exact file paths, internal API signatures, and cross-repository dependencies into the prompt context, significantly curbing library hallucinations. It supports BYOM (Bring Your Own Model) and private/air-gapped deployments.
• Augment Code : Features a Deep Context Engine specifically built to scale across massive enterprise codebases (handling 400,000+ files). Rather than just looking at the file you currently have open, Augment's context engine maps out semantic dependencies across your organization's entire codebase to ensure suggestions adhere to internal architecture and real libraries.
• Qodo : Focuses heavily on code quality, testing, and multi-repository codebase understanding. Qodo's retrieval pipeline indexes internal patterns and mines your pull request history to enforce internal conventions and accurately recognize your team's custom modules and utilities.
• Tabnine Enterprise : Built specifically for privacy-first, regulated, or air-gapped environments. Tabnine offers an Enterprise SDK that allows teams to index and train/fine-tune private models on internal frameworks and unique service-mesh configurations so the model knows your exact internal deployment patterns.
• Continue : An open-source, highly customizable assistant option. If you want total control over the data layer, you can host your own vector database, connect local models (via Ollama ), and configure precise retrieval parameters so that prompts are explicitly packed with local file contents.

If you'd like to narrow this down, tell me:

• What is your approximate repository scale (number of files or lines of code)?
• Are you looking for a cloud-hosted enterprise SaaS or a fully on-prem/air-gapped solution?

I can help recommend the right architectural fit for your team.