AI Knowledge Assistant
The buyer is not buying a chat box. They are buying a faithful, governed retrieval engine that happens to speak in natural language.
OVERVIEW
What This Means for Your Business
Enterprise knowledge lives in a dozen systems — Confluence, SharePoint, Drive, Notion, GitHub, Salesforce, Zendesk, Slack — and the people who need it most spend their days searching. AgileX builds a knowledge-grounded conversational assistant that retrieves from your sanctioned content and generates responses that are faithful, cited, permission-aware, and auditable.
Pure vector-DB RAG is no longer the production baseline. We default to hybrid retrieval — vector + BM25 + graph traversal + cross-encoder reranking — with permission-aware access enforced at the index, not after generation. Every response cites its source; uncited responses are blocked at the generation layer.
Quality is treated as a continuous engineering discipline: RAGAS faithfulness, recall, and citation accuracy are first-class metrics, regression-gated on every release. We typically ship a wedge function — sales enablement, support engineering, HR self-service — in 8-10 weeks with a measurable productivity gain.
WHAT'S INCLUDED
Deliverables & Capabilities
Every engagement is tailored to your needs. Here's what we typically deliver.
Connector & Ingestion Pipeline
Read-only connectors to Confluence, SharePoint, Drive, Notion, GitHub, Salesforce, Zendesk, Slack, and custom apps — with ACL extraction, semantic chunking, and PII redaction at ingest.
Hybrid Retrieval Engine
Vector + BM25 + graph traversal with reciprocal rank fusion and a cross-encoder reranker (Cohere Rerank or BGE) — the production default for enterprise queries.
Permission-Aware Access
ACLs enforced at the index level — documents the user cannot see are never retrieved, never embedded into a prompt, never cited. SCIM-driven sync from Okta, Azure AD, or Google Workspace.
Grounded Generation & Citation
Every response cites document, section, owner, and effective date with click-through links — and a faithfulness guardrail blocks responses unsupported by retrieved context.
Agentic RAG for Multi-Hop Queries
Query rewriting, self-reflective re-retrieval, and graph traversal for the queries that single-shot retrieval can't handle — with bounded step caps and latency budgets.
Channel-Flexible Surfaces
Web app, Slack and Microsoft Teams bots, email Q&A, IDE plugins for engineering teams, and embedded panels in Salesforce or HubSpot for sales reps — with shared memory across channels.
OUR APPROACH
How We Deliver
A structured process that balances rigor with speed.
Source-Readiness Audit
A 1-week paid assessment of your knowledge corpus producing a 'knowledge readiness' score, source list, gap documentation, and a deployment plan.
Build & Tune
Stand up ingestion, configure hybrid retrieval, tune the reranker, and label 100+ eval cases per function — until faithfulness clears 0.85 in sandbox.
Pilot with Real Users
Roll out to a wedge function with assist-mode messaging, capture user feedback, and tune retrieval against real queries.
Stabilize & Expand
Operate with bi-weekly retrieval-tuning iterations, scope adjacent functions, and grow the source library as gaps surface from telemetry.
TECHNOLOGIES
Our Tech Stack
We choose the right tools for each engagement — not the trendiest.
IDEAL FOR
Is This Service Right for You?
Mid-market and enterprise organizations with 50K+ documents across 4+ systems
Sales, support, HR, legal, or engineering teams losing hours to fragmented knowledge
Teams whose prior in-house RAG plateaued at 60% accuracy and never recovered
Start a AI Knowledge Assistant Project
Tell us about your challenge and we'll show you how AgileX can help. No pitch decks — just an honest conversation.