Enterprise AI Assistants
Assistants that answer from your organization's own knowledge, respecting who is allowed to see what rather than treating the corpus as one open pool.
Enterprise AI & Conversational AI
Assistants grounded in your own knowledge, and the language capability behind them.
Overview
An enterprise assistant is only as good as what it is allowed to know. Nextherrion builds assistants grounded in your own documents and systems through retrieval, so answers come with a source and can be checked — along with the underlying language capability: classification, extraction, summarization, translation and speech.
Assistants and retrieval systems built on your own material, and the language and speech capability that underpins them.
RAG Systems
Retrieval-augmented generation: the model answers from documents it has just retrieved, so responses cite a source and can be verified.
Conversational AI
Natural, context-aware interaction that holds the thread of a conversation instead of treating each message as unrelated.
WHAT WE DO
Enterprise & Conversational AI Services We Offer
Text Classification
Sorting incoming text — tickets, messages, documents — by intent, topic or priority, so routing happens on arrival.
Sentiment Analysis
Reading tone across interactions at volume, to surface where experience is degrading before it shows up in a complaint.
Information Extraction
Pulling specific fields and facts out of unstructured text, turning documents into data a process can act on.
Text Summarization
Condensing long material — threads, reports, case histories — to what the reader needs, with the original still reachable.
Translation
Translation across the languages your customers and teams actually use, applied to support, documentation and content.
Speech-to-Text
Transcribing calls and recordings accurately enough to search, analyze and act on rather than only archive.
Text-to-Speech
Generating spoken output for voice interfaces and accessibility, in a voice consistent with how the business sounds elsewhere.
Voice Assistants
Voice interaction for situations where typing is impractical — on a line, in a vehicle, on a call — with the same grounding as the text equivalent.
AI Communication Systems
Tying these capabilities into the channels your business already runs, so one implementation serves several points of contact.
OUTCOMES
What Grounded Assistants Change
The difference between a useful enterprise assistant and an unusable one is almost always grounding: answers drawn from your material, attributed, and bounded by who is allowed to see them.
- Answers from your own material
- Sources shown and checkable
- Permissions respected in answers
- Less time searching for documents
- Support across languages
- One capability, many channels
FAQ
Frequently Asked Questions
What is RAG and why does it matter?
Retrieval-augmented generation retrieves relevant documents and has the model answer from them. It matters because the answer can cite a source, which is what makes it checkable rather than merely plausible.
Will the assistant make things up?
Grounding in retrieved documents reduces it substantially and does not eliminate it. Showing sources is what lets a reader catch the remainder, which is why we treat it as required rather than optional.
Can it respect who is allowed to see what?
Yes, and it must. Retrieval is filtered by the asker's permissions — an assistant that answers from documents someone cannot open is a data-leak path, not a feature.
What if our documentation is out of date?
Then the assistant will confidently repeat it. Content quality is assessed early, because grounding an assistant in stale material makes the staleness easier to reach, not harder.
Which languages can you support?
Model coverage is broad but uneven, and quality varies considerably by language and domain. We evaluate against your actual content rather than relying on a general claim.
How accurate is speech-to-text on our calls?
It depends on audio quality, accents and domain vocabulary. Testing against your own recordings is the only reliable answer; published accuracy figures rarely survive contact with real call audio.
Does this replace our search?
It usually sits alongside it. Retrieval answers questions; search remains better when someone knows the document they want and simply needs to reach it.
TECHNOLOGY
Built on Industry Leading Technology
AI and Generative AI, agent frameworks, cloud platforms, data tooling and modern application stacks — chosen per problem rather than per preference.
HOW WE DELIVER
Enterprise AI Implementation Process
- 01Knowledge & Content Assessment
- 02Access & Permission Design
- 03Retrieval Architecture
- 04Assistant Development
- 05Evaluation & Accuracy Testing
- 06Pilot
- 07Deployment
- 08Monitoring & Content Upkeep
Want an assistant that answers from your own knowledge?
Start with an assessment of the content it would draw on.