RAG knowledge bases
We ingest docs, help centers, PDFs, and structured FAQs into chunked indexes with metadata filters. Answers cite sources so users and auditors can verify claims.
Net17 Solutions | AI Chat & Assistants
CustomAIChat&AssistantsTrainedonYourDomain
We build tailored chat systems for support, operations, and sales—structured around your workflows, trained on your knowledge, and designed for real daily usage.
24/7
Availability
RAG
Grounded answers
100%
Your data boundaries
Domain-trained
Answers from your docs
Guardrailed
Safe, auditable responses
Chat that understands your business—not generic small talk
Off-the-shelf chatbots frustrate users with vague answers and no connection to internal systems. Custom AI assistants succeed when they are grounded in your product documentation, policies, and ticket history—and when they know when to hand off to a human. Net17 builds chat experiences that respect those boundaries while still feeling fast and helpful.
We implement retrieval-augmented generation (RAG) pipelines, tool use for lookups and actions, and multi-channel deployment on web, mobile, Slack, or WhatsApp where appropriate. Prompts, models, and evaluation suites are versioned like production code. Stakeholders see test transcripts before launch, not surprises in prod.
Whether you need a customer-facing support assistant, an internal copilot for HR or IT policies, or a sales enablement bot that drafts outreach from CRM context, we align design with measurable outcomes: deflection rate, time-to-resolution, or rep productivity. The assistant becomes part of your stack, not a disconnected demo widget.
Chat interface preview
Replace with widget or in-app chat screenshot
Why generic bots fail
Bad AI support costs more than no AI at all
Generic models hallucinate product details, ignore refund policies, and cannot open tickets in your CRM. Support volume stays high; trust drops. Teams disable the bot and return to manual queues, having spent budget without ROI.
The fix is not a better model alone—it is architecture: clean knowledge bases, retrieval tuning, action APIs, escalation rules, and continuous evaluation on real queries. We build that stack so your assistant improves weekly instead of embarrassing your brand on launch day.
0%
of users abandon chat after one unhelpful AI response
Capabilities
What our AI assistants include
From knowledge ingestion to deployment and monitoring.
Multi-channel deployment
Same brain on your website widget, in-app chat, Slack, or messaging APIs—consistent tone and policy everywhere.
Tool use & actions
Assistants can look up orders, create tickets, book meetings, or update CRM fields through secured function calls—not just text replies.
Human handoff
Confidence thresholds route complex or sensitive chats to agents with full transcript and suggested replies.
Safety & compliance
Content filters, PII redaction, allowed-topic lists, and logging support regulated industries and brand guidelines.
Analytics & improvement
Dashboards track resolution, escalation, and topic clusters. Failed queries feed back into content and retrieval tuning.
Use cases
Assistants we build most often
Customer support
Tier-1 deflection, order status, troubleshooting playbooks, and seamless agent takeover.
Internal helpdesk
IT, HR, and policy questions with links to canonical docs and request creation.
Sales enablement
Battlecards, proposal snippets, and CRM-aware drafting for outbound teams.
E-commerce
Product discovery, sizing help, and cart recovery conversations grounded in catalog data.
How we work
From knowledge audit to live assistant
01
Audit content
Inventory docs, tickets, and policies; identify gaps and owners.
02
Prototype & evaluate
Test retrieval and prompts on real question sets with scoring rubrics.
03
Integrate & deploy
Wire channels, auth, tools, and monitoring; soft-launch with agents in loop.
04
Tune continuously
Weekly review of logs, content updates, and model/prompt version bumps.
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Support deflection target
Update in config
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Avg. first response time
Update in config
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Languages supported
Update in config
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Answer accuracy (eval set)
Update in config
Proof
Outcome patterns
Replace with your verified chat assistant metrics.
E-commerce | REPLACE
Problem: Support team drowned in repetitive product questions.
What we did: RAG assistant on catalog + policies with order lookup tool.
Result
—
REPLACE: deflection or CSAT metric
B2B SaaS | REPLACE
Problem: Onboarding docs were underused; tickets repeated basics.
What we did: In-app copilot with doc citations and ticket creation.
Result
—
REPLACE: ticket reduction metric
Internal ops | REPLACE
Problem: HR policy questions slowed response via email.
What we did: Slack assistant with approved answers and escalation.
Result
—
REPLACE: response time improvement
Clients
What partners say
“REPLACE: Quote on answer quality and handoff to human agents.”
“REPLACE: Quote on speed of deployment and iteration.”
Tech stack
AI & platform stack
Models, retrieval, and infra we implement in production.
FAQ
Frequently asked questions
Retrieval-augmented generation fetches relevant passages from your knowledge base before the model answers. That grounds responses in your facts, reduces hallucinations, and lets you update content without retraining models. It is the standard architecture for serious business chatbots.
