What about AI hallucinations? We can't have an assistant making things up.

You're right to worry. AI hallucinations cost businesses $67.4 billion in 2024. About 77% of companies are concerned about this problem. The architecture matters here. Standard chatbots generate answers from patterns in training data. They optimize for sounding right, not being right. We use retrieval-augmented generation. The AI retrieves actual documents from your knowledge base before answering. It's grounded in your reality, not statistical guessing. Hallucinations drop dramatically when there's source material to work from.

How accurate are AI assistants really? I've seen some embarrassing public failures.

The failures you've seen are real. Air Canada was ordered to compensate a passenger when its chatbot gave incorrect refund information. A Chevy dealer's bot agreed to sell a Tahoe for a dollar. These happened because the systems weren't properly grounded in business knowledge. Accuracy depends entirely on architecture. Generic AI trained on internet data will hallucinate your business specifics. AI built on extracted organizational knowledge, with proper retrieval and citation, performs at a completely different level. The difference isn't the AI. It's what the AI has access to.

Our knowledge is scattered across documents, emails, and people's heads. Can AI even work with that?

That's actually the problem we solve. The extraction process captures knowledge from wherever it lives: documents, recorded conversations, process observations, expert interviews. We structure it so AI can retrieve accurately. Most AI projects fail here. They skip extraction and try to point AI at raw, unstructured content. The AI can't find what it needs, so it makes things up. Proper extraction creates the foundation. Without it, you're building on sand.

Will this replace our experts or make them less valuable?

It multiplies them. Your experts currently spend hours answering the same questions over and over. The assistant handles those. Your experts get their time back for the work only they can do: the judgment calls, the novel problems, the strategic decisions. Think of it as cloning their ability to answer routine questions, not replacing their expertise. The knowledge stays yours. The experts stay valuable. They just stop being bottlenecked by repetitive inquiries.

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AI Assistants

Your Expertise, Always Available.

Not generic responses. Not hallucinated answers.

An AI assistant that actually knows your business. Real knowledge, accessible to anyone who asks.

The Pattern

Why Most AI Assistants Disappoint

Wrong Answers

Sounds confident but gets details wrong. Every wrong answer erodes trust.

"I Don't Know"

Basic questions return nothing useful. Users give up and ask humans.

Hallucinations

Invents policies and features. Confidently wrong is worse than uncertain.

These aren't AI failures. They're symptoms of a missing knowledge layer. The AI works fine. The architecture doesn't.

The Real Issue

The Problem Isn't the AI

Feed it garbage, get articulate garbage back. An AI assistant is only as good as its knowledge layer.

Documentation Is Stale

Written months ago. Policies changed. Products evolved. Reflects how things used to work.

Documentation Is Incomplete

20% of what experts know. Rest lives in heads, Slack threads, tribal knowledge.

Documentation Is Surface-Level

Tells what to do, not why. No exceptions. No edge cases. No judgment.

Technical Reality

Documentation is Layer 0

Real Knowledge Systems have 6 layers. Most implementations skip straight to retrieval on raw docs. That's why they fail.

1Capture

2Extraction

3Infrastructure

4Upkeep

5Routing

6Output

The Approach

Knowledge First. Then AI.

Capture From Work

Knowledge flows in from daily work, not documentation projects.

Event-driven capture, ticketing integration, automated extraction

Connect Real Sources

One interface to information scattered across dozens of systems.

Real-time sync, API connections, unified data model

Index By Meaning

Find what you mean, not just what you type.

Vector embeddings, entity extraction, context-aware ranking

Know What It Knows

No more confident wrong answers. Honest uncertainty.

Confidence scoring, cross-validation, human escalation

The AI is the easy part.

Building a knowledge layer that's comprehensive, current, and actually captures expertise is the hard part. Most companies skip it. Then wonder why their assistant doesn't work.

What We Build

What Actually Gets Built

An AI assistant is a system, not a product.

Knowledge Foundation

Your expertise captured, structured, accessible

6-layer system, vector DB, entity graph

Assistant Interface

Conversational layer deployed where users are

RAG architecture, multi-channel deployment

Source Integration

Live data from your systems, not snapshots

API integrations, real-time sync, secure credentials

Confidence & Escalation

Knows when to answer vs. when to escalate

Confidence scoring, routing rules, audit logging

Learning Loop

Gets smarter from corrections and feedback

Feedback capture, auto-updates, A/B testing

Beyond Q&A

Beyond the Help Desk

"AI assistant" doesn't mean chatbot. Here's what becomes possible:

Customer Support

Instant answers. Complex issues route to humans with full context.

Zendesk, Freshdesk, web widget

Internal Knowledge

Stop interrupting experts. Policies and procedures instantly accessible.

Slack, Teams, SSO, wikis

Onboarding

New hires learn without bottlenecking seniors. Time to productivity drops.

Role-based access, learning paths

Sales Enablement

Product details, competitive intel, pricing rules on demand.

Salesforce, HubSpot, CRM

Expert Backup

Top performers can't be everywhere. Their expertise can.

Knowledge capture, expert routing

The Multiplication Effect

Better Together

With Data Infrastructure

Assistants access live data, not static knowledge. Answers reflect reality.

From "helpful but limited" to "actually knows what's happening"

Explore

With Intelligent Workflows

"Cancel my order" triggers the cancellation. Conversation becomes action.

From "information retrieval" to "task completion"

Explore

Each piece is valuable alone. Together, they multiply. An assistant that knows your business AND can take action AND sees live data isn't just better. It's a different category of capability.

Ownership

What You Own When We're Done

If we disappeared tomorrow, does everything keep running? If yes, we've done our job. That's the standard.

Knowledge Base — Vector DB, entity graph, exportable

Training Data — Fine-tuning datasets, prompt libraries

Integrations — API configs, sync logic, your credentials

Configuration — Version-controlled, documented, reproducible

Deployment — Docker, K8s, or serverless—your infrastructure

The Standard

No vendor lock-in.
No black boxes.
Complete ownership.

Is This For You?

AI Assistants Make Sense If...

Good fit if you...

AI tools have disappointed—wrong answers, hallucinations

Team answers the same questions repeatedly

Expertise locked in people's heads

New hires bottlenecked by senior staff

Need semantic search, not keyword matching

Want confidence scoring and source citation

Maybe not right if...

Very early stage with minimal knowledge

Looking for a simple chatbot widget

Need a demo, not production infrastructure

Not sure? That's what the discovery call is for.

Start Here

Ready to Make Your Expertise Accessible?

45 minutes to explore what you're trying to solve. No pitch. No pressure. Just clarity on what's possible.

Book a Discovery Call

Data Infrastructure Intelligent Workflows Knowledge Systems

AI Assistants in Practice

Questions from founders who've been burned by chatbots that didn't work.

Most chatbots fail because they're trained on generic data, not your business. When someone asks about your specific process, your policy, your exception handling, the chatbot guesses. And it guesses confidently. That's why 75% of customers feel chatbots struggle with complex issues. What we build is different. It's grounded in your extracted knowledge. When someone asks a question, the system retrieves the actual answer from your documented expertise, then generates a response based on that. It doesn't guess. It cites sources. If it doesn't know, it says so.