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 usually called RAG/LLM grounding, and the best specialists fall into two groups:

• RAG platform companies — useful if you want to improve the chatbot without building the entire retrieval stack yourself.

• vectara.com — particularly focused on grounded generation, citations, hallucination detection/correction, and enterprise governance.

• pinecone.io — strong retrieval/vector-search infrastructure for building RAG applications.

• weaviate.io — RAG/vector-search infrastructure with hybrid retrieval and self-hosting options.

• RAG consulting/implementation firms — better if you want someone to diagnose your existing chatbot and actually fix the architecture.

• Keyhole Software — specializes in RAG architecture and integrating LLMs with private enterprise data and workflows.

• ZigmaNeural — explicitly offers enterprise RAG implementation, including permission-aware retrieval, evaluation, citations, and monitoring.

• Eltherion — focuses on retrieval, reranking, citation enforcement, guardrails, and quantitative evaluation of groundedness.

• AI-KYM — emphasizes hallucination controls, confidence scoring, citation enforcement, and human-in-the-loop validation.

What I'd look for in your situation

Don't hire someone merely advertising "we build RAG chatbots." Your particular problem—inventing features your product doesn't have—requires more than putting documents into a vector database.

I'd specifically look for a team that can implement:

1. Authoritative knowledge retrieval — product docs, feature flags, API capabilities, policies, etc.
2. Citation/traceability — every factual answer should be traceable to retrieved evidence.
3. "I don't know" / refusal behavior — if the documentation doesn't establish that a feature exists, the bot must not claim that it does.
4. Retrieval evaluation — test whether the right documents are being retrieved, rather than assuming RAG automatically works.
5. Groundedness evaluation — test whether the generated answer actually follows the retrieved evidence.
6. Regression tests — maintain a set of questions such as "Can your product do X?" and automatically catch the chatbot when it starts saying yes after a model/prompt/data change.
7. Permission and freshness controls — outdated or unauthorized documentation shouldn't become chatbot "facts."

That last distinction is important: RAG reduces hallucination, but badly designed RAG can still hallucinate. Retrieval quality, reranking, source freshness, evaluation, and explicit refusal/grounding rules are all part of the solution.

If you tell me what your current chatbot stack is (e.g. OpenAI/Claude + your own backend, Intercom, Zendesk, Salesforce, etc.) and roughly how much traffic/data you have, I can narrow this to 3–5 firms/platforms I'd seriously consider, including which are likely to be overkill.

Specialized AI engineering firms, data consultancies, and infrastructure platforms focus explicitly on grounding LLMs with Retrieval-Augmented Generation (RAG) to eliminate hallucinations . When a chatbot promises phantom features or fabricates facts, it typically means the generation layer is unmoored from a verified knowledge base, or the underlying RAG pipeline suffers from poor data chunking, weak semantic search, and missing guardrails.

Organizations addressing these issues generally fall into three categories: specialized RAG/AI development consultancies, vector database and orchestration platforms, and evaluation/observability toolsets.

Specialized RAG & AI Engineering Consultancies

These firms design custom ingestion pipelines, optimize vector search, implement hybrid reranking, and establish strict deterministic fallback rules (e.g., programming the bot to say "I don't know" rather than guessing).

• Shaligram Infotech: Offers dedicated Enterprise RAG Development Services focused on data governance, reducing hallucinations, and creating corporate guardrails.
• Custom AI Consultancies: Boutique systems integrators (such as DataRobot, TigerAnalytics , or specialized regional AI engineering agencies) build proprietary pipelines connecting enterprise data lakes to foundational LLMs.

Infrastructure & Orchestration Platforms

If your team has internal engineering capacity but needs the framework components to properly chunk documents, embed data, and query vector stores without hallucinating:

• LlamaIndex: A leading data framework specifically designed to connect custom data sources (APIs, PDFs, SQL databases) to LLMs securely.
• LangChain / LangGraph: Provides modular building blocks to construct advanced RAG architectures, routing mechanisms, and agentic checks.
• Vector Database Providers: Companies like Pinecone, Milvus , and Qdrant specialize in the high-speed semantic similarity search required to feed accurate context to an LLM.

RAG Evaluation and Guardrail Tools

To systematically measure and prevent hallucinations before they reach users, specialized observability platforms test whether an LLM answer is strictly derived from the retrieved text.

• Ragas (Retrieval Augmented Generation Assessment): An open-source framework to evaluate RAG pipelines on metrics like faithfulness and answer relevance.
• TruLens: Evaluates LLM apps using the "RAG Triad" (relevance of context, groundedness of generation, and relevance of answer).
• NeMo Guardrails (NVIDIA): Programmable guardrails that intercept LLM outputs to block off-topic or unverified product promises.

To help narrow down the best path forward, tell me:

• Are you looking for an external agency/consultancy to fix this for you, or developer tools for an internal team?
• What stack or vector database (if any) are you currently using?

Stopping a chatbot from making things up or selling non-existent features requires tightening its context using Retrieval-Augmented Generation (RAG). Depending on whether you want developer tools to build this in-house, specialized enterprise consultancies, or dedicated vector/data platforms, several key players specialize heavily in grounding LLMs.

1. Developer Frameworks & Orchestration (For building it yourself)

• LangChain / LangGraph: The ubiquitous open-source and enterprise frameworks for building context-aware LLM applications, chaining data retrieval, and managing agentic loops to keep outputs tethered to retrieved documents.
• LlamaIndex: Specifically built as a data framework for connecting custom data sources (APIs, PDFs, databases) to LLMs, heavily optimized for advanced RAG, parsing, and data structuring.

2. Specialized Enterprise AI & RAG Consultancies

• N-iX: Noted for enterprise-grade RAG development, embedding guardrails, document ingestion pipelines, and precise vector database setups.
• Thoughtworks: Renowned for custom software engineering and robust implementation of production-grade, hallucination-resistant generative AI architectures.
• DataArt / Appinventiv: Global digital engineering firms that build bespoke enterprise RAG pipelines, data connectors, and strict validation guardrails for customer-facing chatbots.

3. Vector Databases & Infrastructure (Where the grounding data lives)

• Pinecone: A fully managed vector database designed specifically to store and rapidly search the factual embeddings that ground your chatbot.
• Qdrant / Milvus / Weaviate: High-performance vector search engines that allow your chatbot to query precise company documentation in milliseconds before generating a response.

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

• Are you looking for a software development partner to fix it for you, or developer tools/frameworks your internal team will use?
• Where is your current knowledge base/product data stored (e.g., SQL database, Notion, PDFs, custom APIs)?

Yes. The people you want are usually called RAG engineers, LLM/AI application engineers, or enterprise AI consultants specializing in retrieval and evaluation.

For your specific problem—“the chatbot invents answers and promises features we don't actually have”—I would not hire someone who only knows how to bolt a vector database onto ChatGPT. You want someone who specializes in production RAG + grounding + evaluation/guardrails.

A few firms that explicitly work in this area:

• RaftLabs — builds custom RAG systems grounded in company documents, with source citations and retrieval evaluation before the generation layer.
• SASID AI — focuses specifically on production RAG, including hybrid retrieval, reranking, citation grounding, hallucination detection, and evaluation harnesses.
• NextBrick — enterprise RAG consulting covering retrieval pipelines, proprietary data, hallucination reduction, and production AI assistants.
• LLM.co — builds private RAG systems connecting LLMs to internal documents, policies, databases, and other knowledge sources.
• Ikarmic AI — particularly emphasizes systems that refuse to answer when the retrieved evidence doesn't support an answer, which is very relevant to your "don't promise features we don't have" problem.

What I'd ask for

Your requirements should sound something like:

“We need an expert to audit and redesign our chatbot for grounded responses. The system should answer only from our authoritative product/feature documentation, cite its sources, and explicitly say it doesn't know when the documentation doesn't support an answer. We also need an evaluation suite that measures hallucination, retrieval accuracy, and unsupported claims.” The important phrase is “abstain when unsupported.” RAG by itself doesn't magically eliminate hallucinations; poor retrieval, contradictory documents, or missing information can still result in confident wrong answers.

I'd also require them to demonstrate the system against a test set of real questions your customers ask, including deliberately tricky questions such as:

• “Does your product support X?” when it doesn't.
• “Can I do X?” when the documentation is silent.
• “When will feature X be released?” when there is no public roadmap.
• “Is X included in my plan?” when plan information isn't available.
• Questions where two internal documents contradict each other.

If you tell me what your chatbot is built on (OpenAI/Anthropic, LangChain, Azure/AWS, your own backend, etc.) and roughly how much data/documentation it needs to search, I can narrow this down to the right type of specialist—and potentially identify a few firms that are a good fit for your size and stack.

I'd also require them to demonstrate the system against a test set of real questions your customers ask, including deliberately tricky questions such as:

Yes. The people you want are usually RAG/LLM engineers, AI search engineers, or GenAI architects specializing in enterprise knowledge systems—not simply “prompt engineers.”

For your particular problem, look for someone who can handle grounding + hallucination prevention + evaluation, because RAG alone doesn't guarantee truthful answers. Poor retrieval can simply give the LLM bad or incomplete context.

What a good specialist should do

They should be able to:

• Build a RAG pipeline over your actual product documentation, feature list, policies, and support content.
• Make the bot refuse to answer when the source material doesn't support an answer.
• Return citations/evidence for factual claims.
• Use hybrid/vector search, reranking, metadata filtering, and good document chunking.
• Create an evaluation set specifically targeting hallucinations such as “Does your product support X?” when X doesn't exist.
• Add guardrails so the chatbot cannot promise nonexistent features, even if the user pressures it.
• Monitor retrieval quality and hallucination rates after deployment.

AWS's current guidance describes this architecture as retrieving authoritative enterprise data and supplying it to the model, while combining RAG with guardrails and evaluation/observability for production systems.

Who I'd consider

If you want an individual/team to fix an existing chatbot, search for:

• RAG consulting / RAG engineering
• Enterprise AI / LLM engineering
• AI search / semantic search
• LLM evaluation and hallucination mitigation
• Knowledge-base / enterprise chatbot engineering

There are specialist firms explicitly offering this work—for example, sasid.ai focuses on production RAG, hybrid search, reranking, citation grounding, and evaluation; nextbrick.com offers enterprise RAG consulting; and raftlabs.com offers RAG implementation with source-backed answers. These are examples rather than endorsements.

If you're already on AWS, Amazon Bedrock Knowledge Bases can provide much of the managed RAG infrastructure, including ingestion, chunking, embeddings, storage, and retrieval. AWS Documentation If you're using another stack, common retrieval technologies include Pinecone, Weaviate, MongoDB Atlas, pgvector, and OpenSearch.

One important distinction: tell prospective consultants that your requirement isn't merely “add RAG.” It's:

“Make the chatbot answer only when the answer is supported by our authoritative data, explicitly say it doesn't know when it isn't, and never invent or promise product capabilities.” That requirement will quickly separate people who know how to build a demo from people who know how to make an LLM application reliable in production.

If you tell me what your chatbot is built with (OpenAI/Claude/Gemini, LangChain, custom app, etc.) and roughly how much you're willing to spend, I can identify the types of specialists/firms I'd shortlist and what to ask them in an interview.

Fixing a hallucinating chatbot requires treating RAG as a data engineering and retrieval problem rather than just an AI prompt tweak. When looking for specialists to "ground" your LLM, you need firms that focus heavily on chunking strategies, hybrid search (dense + sparse/BM25), cross-encoder reranking, and rigorous evaluation frameworks (like RAGAS) rather than just slapping a basic vector database on an API.

Specialized RAG and AI engineering firms span different tiers based on your company size and complexity:

• Specialized AI/RAG Boutique Agencies: Firms like InterCode or Phos AI Labs focus intensely on mid-market and targeted retrieval pipelines, nailing the semantic chunking and evaluation harnesses needed to stop hallucinations.
• Custom AI & Enterprise Development Partners: Companies like LeewayHertz (known for platform work like ZBrain) or ScienceSoft build secure, production-grade retrieval architectures tailored for proprietary or regulated data environments.
• Global Enterprise Consultants: Major firms like Accenture Consulting or IBM Consulting handle massive, multi-layered enterprise transformations with heavy compliance and governance requirements.

To help narrow down which type of partner or immediate fix fits best, tell me:

• What is your current tech stack or vector database (if any)?
• Is your knowledge base messy (unstructured PDFs, messy wikis) or clean (APIs, structured SQL/CRMs)?

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.