Our chatbot is hallucinating answers and promising features we don't have. Who specializes in "grounding" LLMs with RAG (Retrieval-Augmented Generation)?

Yes. What you want is a RAG / LLM grounding specialist, ideally someone who does more than simply connect a chatbot to a vector database.

For your specific problem—hallucinated answers + the bot promising nonexistent features—I'd look for expertise in four areas:

• Retrieval-Augmented Generation (RAG): retrieving the authoritative product docs, policies, feature lists, etc. before answering.
• Grounded generation / citations: forcing answers to be supported by retrieved evidence rather than the model's general knowledge.
• Answerability and guardrails: teaching the system to say "I don't know / that's not supported" when the source material doesn't establish something. RAG alone doesn't automatically solve this.
• Evaluation: building a test set specifically for hallucinations, unsupported feature claims, retrieval failures, and false confidence.

Specialists worth looking at

• ragverse.ai — unusually focused specifically on RAG and knowledge systems, including grounding, provenance, evaluation, and permission-aware retrieval.
• sasid.ai — focuses on production RAG, hybrid retrieval, reranking, citation grounding, hallucination detection, and evaluation.
• brainslogic.com — specifically advertises fixing existing RAG systems with poor retrieval and adding citations, evaluation, and access controls.
• keyholesoftware.com — more enterprise-oriented, with RAG architecture and integration into internal systems and governance requirements.
• codewave.com — broader AI/data consultancy with RAG implementation, vector search, governance, and LLMOps experience.

There's also a useful distinction: don't necessarily hire a generic "AI chatbot developer." You want someone who can diagnose why your existing bot makes unsupported claims. A good engagement should start with an audit of your current retrieval, prompts, knowledge sources, and evaluation set, rather than immediately rebuilding everything.

If you tell me what your chatbot is built with (e.g. OpenAI API, Azure OpenAI, LangChain, Salesforce, Intercom, custom app) and roughly how many documents/features it needs to know about, I can narrow this down to 3–5 specialists/companies that are particularly suited to your situation, including Boston-area options if useful.

Tackling hallucination and fake product promises requires a solid Retrieval-Augmented Generation (RAG) architecture or an out-of-the-box grounded search-and-retrieval layer. Depending on whether you need a specialized platform/API to plug into your stack or a consulting/development agency to build it custom, several key players specialize in LLM grounding:

Dedicated Grounding Platforms & APIs

If you want a managed backend or API that explicitly measures and reduces hallucinations:

• Vectara: Built specifically for embedding RAG with a heavy focus on grounding accuracy . They feature proprietary tools like the Hughes Hallucination Evaluation Model (HHEM) and Hallucination Correctors designed to keep error rates remarkably low for customer-facing applications.
• deepset (Haystack): Creators of the open-source Haystack framework, they specialize in composable, flexible NLP and search architectures that let development teams precisely control context retrieval and grounding.
• LangChain / LlamaIndex: While these are open-source frameworks rather than standalone vendors, virtually any engineering team or consultant you hire will use them to build your RAG pipelines.

Enterprise AI & RAG Consulting Firms

If you need an external engineering partner to audit your current chatbot, connect your data silos, and implement robust grounding:

• LeewayHertz: Known for custom enterprise generative AI development and accelerating RAG pipelines using proprietary frameworks like ZBrain.
• Grid Dynamics: Specializes in large-scale enterprise deployments, complex data engineering, and RAG-powered conversational agents.
• ScienceSoft / Keyhole Software: Highly rated U.S. and global software consultancies focusing on secure, architect-governed RAG knowledge assistants, particularly for structured and unstructured enterprise data.
• Big 4 / Global Consultancies (Deloitte, Slalom, IBM Consulting): Best suited if your hallucination issue stems from deeply complex, multi-department compliance, governance, and legacy hybrid-cloud data security requirements.

If you'd like, let me know:

• What LLM and framework (if any) your current chatbot is built on
• Where your company data currently lives (SQL databases, PDFs, Notion, Salesforce, etc.)

I can help you outline a step-by-step mitigation strategy before bringing in an outside vendor.

Yes. What you’re looking for is usually an AI/RAG engineering consultancy or LLM application architect specializing in retrieval, grounding, evaluation, and guardrails—not simply a prompt-engineering shop.

A few firms that specifically advertise this work:

• ragverse.ai — unusually focused on RAG and knowledge systems. They build production systems that ground answers in company documents and provide citations/provenance.
• sasid.ai — focuses on production RAG, including hybrid search, reranking, citation grounding, hallucination detection, and evaluation.
• keyholesoftware.com — enterprise RAG architecture and implementation, connecting LLMs to internal data, APIs, and workflows.
• raftlabs.com — builds custom RAG systems and explicitly validates retrieval against test sets before building the answer layer.
• eltherion.com — emphasizes retrieval quality, reranking, citation enforcement, refusal when evidence is insufficient, and evaluation against real questions/documents.

What I'd look for in your situation

Your problem actually has two separate pieces:

1. Grounding: The bot should retrieve your authoritative product docs, policies, feature matrix, etc. before answering.
2. Behavioral constraints: If the retrieved information doesn't establish that a feature exists, the bot should say "I don't have evidence that we support that" rather than inventing a capability.

A good RAG specialist should therefore talk about retrieval evaluation, reranking, citations, confidence/abstention, hallucination testing, and guardrails—not just "let's put your PDFs in a vector database." Google, for example, describes grounding as connecting responses to verifiable sources and recommends RAG as the retrieval technique.

One important warning: RAG by itself doesn't eliminate hallucinations. If retrieval returns the wrong document—or the model ignores the retrieved evidence—you can still get confident nonsense. The strongest providers treat evaluation and refusal/abstention behavior as first-class engineering problems.

If you tell me your company's size, what your chatbot is built with (OpenAI/Claude/etc.), where its knowledge currently lives (website, PDFs, Confluence, Salesforce, database, etc.), and roughly your budget, I can narrow this to 3–5 firms that are actually appropriate for your situation, rather than giving you a generic RAG vendor list.

Several AI development agencies, enterprise search providers , and consultancies specialize in building RAG pipelines to ground LLMs in verified data.

Top RAG & LLM Grounding Providers

• Pinecone : Provides the vector database infrastructure required to store and quickly search internal knowledge bases for RAG systems via the Pinecone Platform.
• LlamaIndex : Offers a specialized data framework designed specifically to connect custom data sources (APIs, PDFs, databases) to LLMs securely. You can explore tools at LlamaIndex.
• LangChain : Delivers the orchestration framework used by developers to construct reliable, context-aware RAG chains and agent guardrails. Learn more at LangChain.
• Enterprise Consultancies : Major cloud providers and system integrators like Google Cloud Vertex AI Search and Conversation or Microsoft Azure AI Search provide turnkey enterprise RAG grounding tools.

If you'd like, let me know:

• Where your current data is hosted (e.g., SQL databases, PDFs, cloud storage)
• What framework or LLM you are currently using

I can help you outline a step-by-step implementation plan to add a retrieval layer.

Grounding an LLM using Retrieval-Augmented Generation (RAG) is a specialized software engineering and data discipline. It involves setting up proper document chunking, vector embeddings, hybrid semantic/lexical search, security/permission filters, and evaluation loops to ensure the AI only answers from verified sources.

Depending on your company's scale and whether you need strategic consulting or hands-on implementation, several types of firms and specialized engineering partners focus on this exact problem:

Mid-Market & Enterprise RAG Specialists

• N-iX : Offers an enterprise-focused RAG and AI practice covering semantic vector search, graph-aware querying, continuous groundedness scoring, and compliance alignment.
• LeewayHertz : Builds custom enterprise generative AI applications and specialized RAG architectures using pre-configured vector database connectors.
• ScienceSoft : Specializes in custom embeddings and connecting LLMs securely to enterprise knowledge bases and legacy internal data stores.
• Keyhole Software : Focuses on full-stack vector database integration, custom chunking, and fine-tuning retrieval pipelines for mid-to-large enterprises.
• deepset : Creators of Haystack (an open-source framework for building production RAG systems), offering specialized consulting and architecture design for complex context engineering.

Global & Large-Scale Consultancies

• Accenture AI and Deloitte : Best if you are a massive enterprise requiring heavy governance, regulatory compliance (like HIPAA or the EU AI Act), and massive change management alongside your RAG rollout.
• DataArt & Grid Dynamics : Strong engineering consultancies that build secure, production-grade search and semantic retrieval pipelines tailored to specific verticals like commerce, finance, or healthcare.

To help narrow down which type of partner fits your situation, tell me:

• What is your current tech stack (e.g., LangChain, LlamaIndex, raw APIs)?
• Where does your knowledge base live (Confluence, PDFs, internal SQL databases, SharePoint)?
• Are you looking for a boutique AI agency for a quick fix or an enterprise consultancy?

Yes. What you’re looking for is usually called an enterprise RAG / LLM grounding specialist or RAG engineering consultancy.

A few firms worth evaluating:

• nextbrick.com — specifically focuses on production RAG architectures and enterprise knowledge systems.
• beyondkey.com — emphasizes source citations, hybrid retrieval/reranking, permissions, confidence thresholds, and “I don't know” fallbacks.
• raftlabs.com — builds RAG systems grounded in company documents and knowledge bases, including citations.
• schaper-consulting.com — focuses on connecting LLMs to company knowledge while incorporating permissions, grounding, and hallucination controls.

But don't hire someone who only says “we'll add a vector database”

Your particular problem—the bot inventing answers and promising nonexistent features—requires more than basic RAG. A good specialist should address:

1. Authoritative knowledge sources — product docs, feature matrix, pricing, policies, etc.
2. Retrieval quality — hybrid search, chunking, metadata, reranking.
3. Grounded generation — answers must be supported by retrieved material.
4. Explicit refusal behavior — if the information isn't found, the bot should say “I don't know”, rather than guess.
5. Feature/entitlement controls — especially important if it's claiming that your product has functionality it doesn't actually have.
6. Citations/evidence — ideally every substantive answer can be traced to a source.
7. Evaluation — a test suite of real customer questions designed specifically to measure hallucinations, unsupported claims, and false promises.
8. Guardrails outside the LLM — don't rely on prompting alone to prevent the model from making commitments.

RAG reduces hallucinations; it doesn't magically eliminate them. Poor retrieval can actually feed the model misleading context, so retrieval quality and evaluation are critical.

If you're choosing a vendor, I'd specifically ask them: “Show me how you prevent the model from answering when the retrieved sources don't support the answer.” That's a much better test than asking whether they “do RAG.”

The people you’re looking for are usually called RAG engineers, LLM application architects, AI engineers specializing in retrieval, or GenAI consultants. For a production chatbot that is hallucinating and inventing features, you typically want someone who understands more than just prompting—they should be able to redesign the knowledge pipeline, retrieval layer, evaluation process, and guardrails.

Good specialists typically have experience with:

• RAG architecture

  • Document ingestion pipelines
  • Chunking strategies
  • Embeddings
  • Vector databases (e.g., Pinecone, Weaviate, pgvector, Milvus)
  • Hybrid search (keyword + semantic)
  • Reranking models

• LLM evaluation and reliability

  • Creating “golden” question sets
  • Measuring retrieval accuracy
  • Testing whether answers are actually supported by sources
  • Adding confidence thresholds and “I don’t know” behavior

• Product grounding

  • Connecting the bot to your actual product documentation, API capabilities, pricing rules, feature flags, and support knowledge
  • Preventing the model from claiming nonexistent features
  • Adding tool/API checks before making promises

Some places to look:

• Specialized RAG/LLM engineering consultancies that build production retrieval systems, such as N-iX, ScienceSoft, or other firms focused specifically on enterprise RAG delivery.
• RAG-focused boutiques that emphasize grounding, citations, and retrieval quality, such as Ragverse.
• Independent RAG architects/consultants (often found through AI engineering communities, conference speakers, or referrals) who specialize in retrieval evaluation rather than generic chatbot development.

When interviewing candidates, ask:

1. “How would you stop our bot from claiming features we don’t have?”
   A strong answer mentions grounding answers in authoritative sources, tool/API verification, and refusal behavior—not just “better prompts.”

2. “How do you measure whether RAG is working?”
   Look for answers involving retrieval metrics, faithfulness/groundedness evaluation, regression tests, and monitoring.

3. “Have you fixed an existing RAG system that was hallucinating?”
   Production debugging experience matters more than building a demo.

A common failure mode is hiring a “prompt engineer” when the real issue is that the model has no reliable source of truth or no mechanism to verify claims. You likely need someone who can audit the entire path:

user question → retrieval → context selection → model response → validation → final answer

rather than only changing the prompt.

When a chatbot starts promising features you don’t have or fabricating facts, it usually means the underlying Large Language Model is relying entirely on its parametric memory (its training data) rather than being anchored to a strict, verifiable source of truth.

Fixing this requires implementing a robust Retrieval-Augmented Generation (RAG) pipeline—handling data ingestion, smart chunking, vector database architecture, hybrid search, reranking, and rigorous evaluation.

Specialists in LLM grounding and RAG architecture generally fall into a few clear categories depending on whether you need a full-service custom development agency, enterprise consulting, or evaluation and guardrail tooling:

• Specialized AI & RAG Engineering Firms: Companies like Keyhole Software, ScienceSoft, LeewayHertz , and Grid Dynamics specialize explicitly in custom enterprise knowledge integration, full-stack vector database setup (Pinecone, Milvus, pgvector), and fine-tuning the data ingestion and semantic retrieval layers.
• Global Enterprise Consultants: Major firms like Accenture, EPAM Systems, and IBM Consulting have dedicated generative AI practices that build secure, production-grade RAG architectures with strict role-based access control and compliance auditing for enterprise environments.
• RAG Evaluation and Guardrail Platforms: Sometimes the issue isn't just building the RAG pipeline, but continuously catching when the model strays from the text. Tools and platforms like Galileo AI, Cleanlab, and Nvidia NeMo Guardrails provide evaluation frameworks and real-time confidence-scoring to explicitly flag or block unfaithful, hallucinated outputs before they reach the user.

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

• What is your current tech stack or where is your product hosted?
• Is your data mostly unstructured documents (PDFs, wikis) or structured data (APIs, databases)?

I can help you outline an immediate hallucination-reduction checklist or recommend the right tier of partner for your scope.

Yes. The people you want are usually called RAG engineers, LLM/RAG architects, AI search engineers, or AI consultants specializing in retrieval and grounding. They should know more than just “connect a vector database to GPT”: production RAG requires retrieval quality, reranking, source attribution, evaluation, permissions, and refusal behavior when the evidence isn't there.

A few firms I’d put on a shortlist:

• Keyhole Software — focuses specifically on enterprise RAG architecture and integrating private data with LLM applications.
• Space-O AI — offers RAG assessment, architecture, retrieval strategy, implementation, and evaluation.
• Ragverse — particularly RAG-focused; builds production knowledge systems where answers are traceable to source material.
• BrainsLogic — explicitly works on fixing poor retrieval, hybrid search, reranking, citations, and evaluation.
• RaftLabs — offers RAG implementation aimed at grounding answers in company documents and producing source citations.

One important caveat: RAG doesn't magically eliminate hallucinations. A good specialist should also design the chatbot to say “I don't know” when retrieval doesn't provide sufficient evidence, cite the evidence used, and test it against a deliberately constructed hallucination/groundedness benchmark. RAG improves grounding but can still fail when retrieval is bad.

If you're hiring someone, I'd specifically ask:

“Can you audit our existing chatbot, identify why it invents unsupported product capabilities, implement RAG with citations and refusal/abstention behavior, and give us an evaluation suite demonstrating the reduction in unsupported answers?”

That question will separate genuine RAG practitioners from people who simply know how to build a chatbot.

When an LLM starts promising features you don't have or hallucinating facts, it usually means it is operating on its base parametric memory (guessing based on what sounds plausible) rather than being anchored to a strict source of truth.

Fixing this requires implementing a Retrieval-Augmented Generation (RAG) pipeline—feeding verified product documentation, feature lists, and company knowledge into a vector database and forcing the model to cite or restrict its answers to that retrieved context.

To see how a data-retrieval layer stops a model from making things up, watch this quick breakdown of how RAG architectures work:

01:35:03 Unlocking Business Knowledge Through RAG Based AI ...6 months ago YouTube · Koenig Solutions

Specialized firms, digital agencies, and consultancies focus explicitly on enterprise RAG implementation, LLM grounding, and hallucination reduction:

• Specialized AI & RAG Engineering Firms: Companies like LeewayHertz (known for their ZBrain platform), Vstorm, Markovate , and Azumo build custom production-grade RAG pipelines, handling everything from document chunking and embedding selection to vector database integration (Pinecone, Weaviate, Qdrant) and strict prompt-boundary enforcement.
• Mid-to-Large Digital Transformation Consultancies: Firms like Appinventiv, N-iX , and ScienceSoft specialize in enterprise-scale knowledge retrieval, ensuring that security, role-based access control (so users only query what they are allowed to see), and compliance standards are met.
• Global Enterprise & System Integrators: For massive corporate ecosystems, firms like Accenture AI, IBM Consulting , and Deloitte design governance-first RAG frameworks across multi-department and multi-cloud environments.
• Out-of-the-Box Grounding Platforms: If you want a platform-driven approach rather than custom agency development, tools and platforms like Vectara provide out-of-the-box neural search and specialized hallucination-evaluation models built specifically to drive error rates down below 1%.

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

• Where your product data lives (Notion, PDFs, a relational database, custom APIs)
• Whether you prefer an external agency to build it or an in-house technical roadmap for your own developers

I can give you a more tailored recommendation on how to proceed.