( AI Automation — Custom AI Assistants )
Custom AIAssistants
Build AI assistants that know your business inside and out. Trained on your documents, deployed on your terms, answering questions your team and customers actually ask.
AIQSO Custom AI Assistants is a service that builds, trains, and deploys AI models on company-specific data for customer support, internal knowledge retrieval, and workflow automation — with self-hosted or cloud deployment options.
Your data. Your AI.
Key Takeaways
- •RAG architecture retrieves your documents at query time so the AI always has current, accurate information
- •Fine-tuning adapts model behavior to your domain language, tone, and specific use cases
- •Self-hosted Ollama deployments keep all data on your infrastructure with no third-party API calls
- •Cloud deployments via Claude and GPT-4 offer higher performance for complex reasoning tasks
- •Multi-source ingestion supports PDFs, wikis, databases, ticketing systems, and custom APIs
( The Problem )
Why Custom AI?
Generic AI does not know your business. An assistant grounded in your own material does.
Generic AI, off the shelf
- Does not know your products, services, or terminology
- Answers plausibly when it should say it does not know
- Sends your queries — and their contents — to a third party
- No control over when the model changes underneath you
- Per-query costs that scale with adoption, not with value
- Cannot reach into your systems to actually do anything
An assistant built on your data
- Grounded in your documents, terminology, and processes
- Cites its sources, and says so when context is missing
- Self-hosted deployment keeps the data on your network
- You control model versions and when they change
- Local inference removes per-query cost from the equation
- Integrated with the systems your team already works in
"Most AI integrators bolt security on afterwards. AIQSO is a security firm that integrates AI — so the assistant gets built inside your compliance boundary, not around it."
How RAG-Powered Assistants Work
Retrieval-Augmented Generation grounds AI responses in your actual data. Instead of hallucinating answers, the assistant retrieves relevant documents and uses them as context for every response.
Document Ingestion
Your documents — PDFs, knowledge bases, SOPs, product catalogs, support tickets — are processed, chunked, and converted into vector embeddings using models like nomic-embed-text. These embeddings are stored in a vector database such as Qdrant or ChromaDB.
Query & Retrieval
When a user asks a question, the query is embedded and compared against your document vectors using semantic similarity search. The most relevant chunks are retrieved, ranked by relevance, and passed to the language model as context.
Generation & Citation
The LLM generates a response grounded in the retrieved documents. Responses include source citations so users can verify information. The model is instructed to say "I don't know" rather than fabricate answers when context is insufficient.
Continuous Learning
New documents are automatically ingested as they are created. User feedback flags incorrect responses for review. Analytics track which questions are asked most frequently and where the assistant underperforms.
Fine-Tuning & Model Customization
When RAG alone is not enough, fine-tuning teaches the model your domain vocabulary, communication style, and specialized reasoning patterns.
Domain-Specific Training
Create training datasets from your best support responses, sales conversations, and technical documentation. The model learns your terminology, product names, and industry-specific language so responses feel natural and accurate.
Tone & Brand Alignment
Fine-tune the model to match your brand voice — whether that is professional and formal, friendly and conversational, or technical and precise. Consistent communication strengthens customer trust.
Task-Specific Models
Train specialized models for distinct use cases: one for customer support ticket classification, another for sales qualification, and a third for internal knowledge retrieval. Each model excels at its specific job.
Ollama Self-Hosted Models
Deploy fine-tuned models locally using Ollama on your own hardware. Models like Llama 3, Qwen, and Mistral run on standard GPU servers. No API costs, no data leaving your network, full control over model versions.
Claude & GPT-4 Integration
For tasks requiring the highest reasoning capability — complex analysis, nuanced writing, multi-step planning — we integrate directly with Claude or GPT-4 APIs with your custom system prompts and context.
Hybrid Architecture
Route simple, high-volume queries to fast local models and complex, low-volume queries to powerful cloud APIs. This balances cost, latency, and quality across different types of interactions.
Deployment Options
Your AI assistant runs where it makes sense for your security, performance, and budget requirements.
Self-Hosted (On-Premises)
Run your AI assistant entirely on your own infrastructure using Ollama and open-source models. All data stays within your network. Ideal for regulated industries, government contractors, and organizations with strict data sovereignty requirements. No per-query costs after initial setup.
Cloud API (Managed)
Connect to Claude, GPT-4, or Gemini APIs for maximum model capability without managing GPU infrastructure. Best for organizations that need the highest quality responses and are comfortable with API-based data processing under enterprise agreements.
Hybrid (Recommended)
Route sensitive queries through on-premises models and complex queries through cloud APIs. A LiteLLM proxy manages routing, failover, and cost tracking across multiple providers. Most organizations start here for the best balance of security and capability.
Edge Deployment
Deploy lightweight models to edge devices or branch offices for low-latency responses in environments with limited connectivity. Sync with central knowledge bases when network is available.
Common Use Cases
Custom AI assistants solve specific problems across customer-facing and internal operations.
Customer Support
Answer product questions, troubleshoot issues, and resolve common tickets using your knowledge base. Escalate complex issues to human agents with full conversation context.
Internal Knowledge Base
Give employees instant access to SOPs, HR policies, technical documentation, and institutional knowledge through a conversational interface instead of searching through file shares.
Sales Qualification
Pre-qualify leads by asking discovery questions, matching needs to products, and routing qualified prospects to the right sales rep with a summary of the conversation.
Document Analysis
Upload contracts, invoices, or reports and ask questions about their content. Extract key terms, compare documents, and generate summaries without manual review.
Onboarding Assistant
Guide new employees or customers through setup processes, answer their questions in real time, and track completion of onboarding checklists automatically.
Compliance & Policy
Answer regulatory questions by referencing your compliance documentation. Flag potential violations and provide citations to the specific policy or regulation that applies.
( Industries )
Built for Your Industry
The retrieval architecture is the same everywhere. What changes is the compliance boundary it has to live inside.
( 01 )
Healthcare & Medical
HIPAA obligations, medical terminology, patient communication load
Self-hosted deployment so protected health information never leaves the network
- Deploys inside your HIPAA boundary
- Trained on your clinical terminology
- Patient intake assistance
- Integrates with EHR APIs
( 02 )
Legal & Law Firms
Document review hours, client confidentiality, contract analysis volume
On-premises document analysis where privilege survives the architecture
- Contract clause extraction
- Legal research assistance
- Document summarisation
- No third-party processing
( 03 )
Financial Services
Regulatory scrutiny, data sensitivity, report production
Auditable AI for document processing inside your compliance boundary
- Policy and regulation lookup
- Financial document analysis
- Customer inquiry handling
- Retrieval audit trail
( 04 )
Manufacturing
Technical documentation sprawl, training cost, equipment troubleshooting
A technical assistant for operations and maintenance floors
- Technical manual Q&A
- Maintenance procedure lookup
- Onboarding assistance
- Parts and spec lookup
( 05 )
Professional Services
Repeated client questions, documentation overhead, knowledge trapped in people
Knowledge base plus client-facing support on one retrieval layer
- Client support assistant
- Internal knowledge retrieval
- Project documentation Q&A
- Institutional knowledge capture
( 06 )
Defense & Government Contracting
CUI handling, CMMC control obligations, prime-contractor flow-downs
Assistants that deploy inside the enclave rather than around it
- CUI stays inside your boundary
- Maps to NIST 800-171 control expectations
- Policy and FAR/DFARS lookup
- Air-gapped deployment supported
( Packages )
How to Engage
Assessment first. Nothing gets built until the use case is worth building.
Assistant Build
Proven architecture, your data
A RAG-powered assistant built on architecture we already run in production, grounded in your documents and deployed where you want it. The fastest path from "we have knowledge nobody can find" to something your team uses daily.
What's Included
- Document ingestion and vector indexing
- Retrieval tuned and evaluated on your content
- Deployment on your infrastructure or AIQSO-hosted
- API integration and documentation
- Data preparation support
- Initial support period included
Deliverables
- Working assistant grounded in your data
- API access and integration guide
- User walkthrough session
- Technical documentation
( Process )
How the Build Runs
Six steps from first call to a deployed assistant your team actually uses.
Discovery Call
30 minA free 30-minute call to understand the problem, the data you already have, and whether custom AI is even the right answer.
Assessment & Proposal
1-2 weeksA closer look at your data, workflows, and constraints, returned as a scoped proposal with a timeline — not an open-ended engagement.
Data Preparation
1 weekWe structure and clean the source material so retrieval performs. This is where most AI projects quietly fail, so it gets its own phase.
Build & Evaluation
1-2 weeksIngestion, retrieval tuning, and iterative evaluation against real questions from your team, scored before anything reaches production.
Deployment
1 weekProduction deployment on your infrastructure or AIQSO-hosted, with the integration and runbook documentation to go with it.
Handover & Support
2-3 daysUser walkthrough, technical handover, and a support period — then either you run it or we do under managed hosting.
Related Services
AI Automation & Integration
Overview of all AI automation services including assistants, integrations, and strategy.
AI Integration Services
Connect AI models to your existing CRM, ERP, and business tools via APIs and middleware.
Workflow Automation
Automate repetitive business processes with n8n workflows and AI-powered triggers.
Is This Right for You?
When to Use This Service
- Ifyour team answers the same questions repeatedly from customers or employees — a RAG-powered assistant can handle 60-80% of routine inquiries immediately
- Ifyou have extensive documentation that people struggle to search through — a conversational AI interface makes knowledge accessible without knowing exact search terms
- Ifyou need AI that understands your specific products, processes, and terminology — custom training on your data produces far better results than generic chatbots
- Ifdata privacy or regulatory requirements prohibit sending data to third-party APIs — self-hosted Ollama deployments keep everything on your infrastructure
When This May Not Be the Right Fit
- Ifyou do not have existing documentation or knowledge base content to train on — build your knowledge base first, then add AI on top of it
- Ifyour use case is a simple FAQ with fewer than 20 questions — a static FAQ page or basic chatbot widget may be more cost-effective
- Ifyou need the AI to take irreversible actions without human oversight — start with human-in-the-loop approval before enabling autonomous actions