See how custom-built AI chat agents outperform off-the-shelf SaaS tools
Key features
Data privacy:
Customisation:
Speed to delivery:
Long‒term cost:
Integration:
Ownership:
Training data:
Hosting location:
Vendor lock‒in:
Use case focus:
Support model:
LogiNet AI chat agent
• Yours, always
• Full, from LLM to UI
• 2–8 weeks
• Predictable
• Deep, business‒specific
• 100% yours
• Your documents, workflows
• On your infrastructure
• None
• Tailored to your business
• Optional AI Ops, consultative
SaaS chatbot tools
• Vendor-controlled
• Limited
• Variable, often long
• Scaling fees
• Generic
• Subscribed access
• Generic datasets
• Cloud-only (external)
• High
• One-size-fits-all
• Ticket-based, limited scope
Built by experts who’ve been doing AI before it was a buzzword
At LogiNet, we’ve been delivering real-world AI solutions since long before GenAI became a trend. From LLM-based assistants to privacy-first automation, we’ve built AI tools that run inside critical systems—fast, safe, and tailored. Our AI chat agents don’t just answer questions. They understand your business.
Here’s what most teams ask before launching their AI chat solution
Privacy & data ownership
We’ve got sensitive data—can we really trust AI with it?
es. That’s exactly why we host it on your infrastructure. No third-party servers. Full control, full encryption, and zero data leakage.
Will any of our internal data be sent to external APIs like OpenAI?
No. We use self-hosted or privately managed models, with clear boundaries on what stays inside your environment. You decide what’s exposed—if anything.
Can we keep the AI from ‘learning’ from our data?
Absolutely. Your data is used for fine-tuning, not training future models. No data is reused outside your instance.
How do you ensure GDPR compliance?
We design privacy-first from day one. Data retention policies, audit trails, and access controls are built in. We support full GDPR compliance—by design.
Cost & budgeting
Can we keep the AI from ‘learning’ from our data?
In the short term, it may cost slightly more—but long-term? You avoid per-seat pricing, scaling costs, and hidden API fees. It pays off fast.
What’s the pricing structure like?
Clear and flexible. We offer fixed-scope packages for PoCs, and modular add-ons for ongoing support. No vague licenses or unpredictable SaaS fees.
Do we need to hire a whole AI team to run this?
Not at all. You don’t need a dedicated AI department. We build systems you can manage with your existing IT or DevOps team—with help available if needed.
Will this help us cut operational costs?
Yes. Our clients often replace or reduce repetitive admin tasks, helpdesk load, and internal request handling within weeks—saving time and headcount.
Speed & time to value
Is this really something we can get up and running in 2 weeks?
Absolutely. In fact, we’ve done it before. Aftermatch went live in 2 weeks with full PoC for investor demo. The key is to keep scope focused and outcomes sharp.
How much of our team’s time will it take?
Minimal. We do the heavy lifting. We’ll need a few key inputs—like documents, typical workflows, and a contact person—but we value your time.
We’ve had projects drag on for months—how do you deliver fast?
Simple. We don’t reinvent the wheel. We work from proven templates and components we’ve battle-tested across other clients.
Can we add features later?
Definitely. We build your system modularly, so you can expand its capabilities over time without redoing the core.
Customisation & security
Can we tailor the chatbot’s tone, language, and behaviour?
100%. From tone of voice to preferred terminology and escalation logic—it’s all customisable.
What’s the difference between this and a regular chatbot?
Ours are LLM-powered, deeply contextual, and trained on your actual content. Think beyond button-based flows—this is dynamic, smart interaction.
Can we plug it into our CRM or knowledge base?
Yes. We integrate with most major systems—Salesforce, HubSpot, Jira, Confluence, SharePoint, and more. If there’s an API, we can talk to it.
How do you protect against hallucinations or wrong answers?
We apply Retrieval-Augmented Generation (RAG) to ground the AI’s responses in real, verifiable data from your systems. That means more accuracy, less guesswork.
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