Project Name
Cut Support Resolution Time by 60% for a Network Services Provider with RAG
A mid-to-large North American network services organisation providing managed broadband, signal monitoring, and operations support had technical documentation spread across PDFs, wikis, versioned release notes, and Slack threads. No single system could query all sources. Off-the-shelf LLMs produced plausible but incorrect answers, eroding team trust. New hires took 3 to 4 weeks to become self-sufficient. Applying its AI-First approach, Ksolves built a production-grade RAG knowledge assistant grounding every response in verified source documents with full citation auditability – cutting support resolution time 60%.
- Fragmented Knowledge Base: Documentation across PDFs, wikis, release notes, and Slack threads with no single system able to query all sources simultaneously - high search latency on every support interaction.
- Hallucination Risk: Off-the-shelf LLMs produced plausible but factually incorrect answers against proprietary technical content, causing agents to abandon AI tools entirely.
- Poor Retrieval Relevance: Keyword search failed to match semantic intent for queries using colloquial equipment terms, abbreviations, or symptom descriptions rather than exact documentation language.
- Parsing at Scale: Hundreds of multi-column PDFs, diagrams, and nested tables required event-driven parsing handling continuous document updates without full re-indexing.
- Governance and Auditability Gap: Stakeholders required every AI answer traceable to a specific document, page, and version. No generic chatbot could satisfy this out of the box.
- Governance and Auditability Gap: Stakeholders required every AI answer traceable to a specific document, page, and version. No generic chatbot could satisfy this out of the box.
Ksolves built a production-ready RAG system converting the client's entire document corpus into a semantically indexed knowledge layer. Every answer is backed by a cited source chunk. The system explicitly returns 'I don't know' rather than hallucinate. New documents are parsed, chunked, embedded, and queryable within minutes of upload.
- RAG Pipeline: Vector embedding store combined with a query-optimised LLM - answers generated from and attributed to real document content, not model weights.
- Query Router: Multi-path router classifying queries by intent and dispatching to the most relevant retrieval index - routing equipment queries away from policy documents to prevent irrelevant context injection.
- Event-Based Document Parsing and Chunking: Event-driven ingestion detecting new or modified files, parsing complex PDFs including tables and diagrams, applying semantic chunking preserving contextual boundaries.
- Human-in-the-Loop Feedback Loop: Confidence-threshold gate flagging low-certainty answers for expert review - continuous improvement loop driving accuracy above 90% without full model retraining.
- Source Citation Layer: Every answer includes originating document, section, and version number - full auditability for every AI-generated response.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | RAG Pipeline (LLM + Retrieval) |
| Processing | Document Parsing & Chunking |
| Architecture | Query Router |
| Database | Vector Database |
| Platform | API Gateway & Orchestration Layer |
| Methodology | Human-in-the-Loop Review |
- Support Resolution Time Cut 60%: Before: agents searched 3 to 5 disconnected systems per query adding 4 to 8 minutes per interaction. After: grounded, cited answer returned in under 10 seconds (target).
- 100% of Documentation Corpus Queryable: Before: only approximately 30% of the corpus was discoverable via keyword search. After: 100% of ingested documents semantically queryable within minutes of upload.
- Answer Accuracy Above 90%: Before: AI prototype answers untraceable and frequently incorrect. After: every response cites its source, human-in-the-loop review driving accuracy above 90% in test environments (target).
- Onboarding Cut to Under 1 Week: Before: new hires required 3 to 4 weeks of shadowing. After: onboarding to documentation independence projected under 1 week with AI assistant as first-line resource (target).
- Zero Hallucinations - Grounding First: System explicitly returns 'I don't know' on out-of-scope queries - eliminating the hallucination risk that caused agents to abandon previous AI tools.
“For the first time, our engineers can ask a question and trust the answer – and know exactly which document it came from.”
– VP Engineering or Head of Network Operations.
A North American network services organisation whose engineers spent more time searching disconnected documentation than solving problems – with hallucinating AI tools that eroded trust and new hires taking weeks to ramp – was transformed through Ksolves AI/ML consulting services. A production-grade RAG knowledge assistant now provides instant, citation-backed answers from 100% of the document corpus. Resolution time cut by 60%. Accuracy above 90%. Onboarding under 1 week. Every answer traceable. The citation-backed architecture meets AI governance requirements and serves as a reusable enterprise AI backbone for product teams, onboarding, and customer-facing self-service.
Is Your Enterprise Knowledge Still Buried in PDFs Your Team Cannot Search Reliably?
