Artificial Intelligence and Data
Generative AI systems
A real generative AI system is not just pasting a chat widget: it defines who can access what, where answers come from (a versioned knowledge base, not “the whole internet”), how copy respects brand voice and regulation, and what happens when the model hallucinates. Viscale builds the full stack—ingestion and indexing of materials you authorize, semantic retrieval, orchestration of calls to cloud or controlled model APIs, web UI or Teams/Slack integration, and a dashboard for frequent questions, documentation gaps, and cost by department. All with SSO, audit trails, and human review where risk demands it.
We start from usage: who will ask, about what, and how bad it is if the answer is wrong. Then we layer the “brain”: retrieve the right chunks from your PDFs and articles first, only then ask the model to write on top of that—with a citation or source link when possible. We block sensitive topics and empty generic answers with filters before anything hits the screen.
What we typically ship
Internal “ask the handbook” chat
HR, IT, or ops with sources you uploaded—no random Wikipedia blended in.
Customer portal copilot
Explains invoices, deadlines, and documents in plain language with a link to the official PDF.
Policy and compliance assistant
Answers only with excerpts from legal-approved internal policies.
On-brand asset generator
Brief plus good examples; output passes a tone and length validator.
Technical catalog Q&A
Engineers ask for specs; answers cite the published datasheet.
Meeting summary for the intranet
Connects to authorized recordings; only calendar participants see the text.
Smart search over a legacy pile
Thousands of old PDFs become queryable without migrating everything to a new CMS.
Sales training simulator
Customer persona with objections; reps practice without logging chats to production.
Widget inside your existing app
Iframe or API: “explain this field” with context from the open screen.
Controlled multilingual mode
Same base, answers in the user language with a fixed product glossary.
Performance and cost move together: caching for similar questions, context size limits, and smaller models for internal drafts. For customer-facing copy (email, social), the flow adds review or an automatic banned-word checklist. Privacy is designed in: personal data does not enter the search index without agreed anonymization.
Evolution is continuous: when you publish a new handbook or change pricing, the ingestion pipeline refreshes the index and flags breakage. We train comms or IT to tweak “garden” prompts without calling us for every comma—while technical guardrails stop anyone from opening the system by accident.
Portfolio of Generative AI systems
Deliverables
Production system
URL or integration on agreed channels.
Application repository
Code, infrastructure as code, or deploy documentation.
Indexed knowledge base
Source list with last ingestion timestamps.
Administrator manual
Upload files, reindex, pause features, and read logs.
Technical privacy policy
What is sent to the model, retention, and deletion.
Versioned prompts and configuration
To trace behavior changes over time.
Usage and cost dashboard
By team or period as agreed.
Automated quality tests
Reference questions that run on every deploy.
Incident runbook
Provider outage, corrupted index, latency spikes.
Handoff session
Internal IT or vendor takes over confidently.
Expansion guide
How to add CRM or another repository in a second cycle.
Legal review checklist
Items for counsel before opening a new topic area.
Execution methodology
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Usage and risk discovery
Personas, allowed data, and what the bot must never say.
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RAG or hybrid architecture
Sources, indexing, context trimming, and model choice by scenario.
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Prototype on a real subset
Dozens of documents to validate quality before a big bang.
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Authentication and roles
SSO, groups, and visibility by folder or metadata.
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UI and accessibility
Responsive web or integration; keyboard and screen readers when required.
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Content guardrails
Filters, blocklists, and safe messaging when no source exists.
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Ingestion pipeline
Updates when manuals change; handling bad OCR or broken PDFs.
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Load and cost testing
Simulate user spikes and estimate provider bills.
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Go-live and monitoring
Anonymized question logs, thumbs feedback, and alerts.
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Training and governance
Who approves new documents and who may change base prompts.
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Post-launch roadmap
Prioritized new sources, languages, or integrations.
