For engineering and operations directors who need internal docs, Jira tickets, API specs, or customer case histories instantly queryable. We deliver a production-grade RAG agent with strict role-based access control, vector indexing, and zero training data leakage.
Without deterministic vector grounding and role-based permissions, internal AI initiatives get blocked by security and ops.
Staff lose up to a day every week digging through fragmented Notion workspaces, Confluence silos, Google Docs, and Jira tickets to answer standard process questions for colleagues and clients.
Unsanctioned ChatGPT use leads to employees pasting confidential client records or internal API tokens into public models. Meanwhile, generic bots make up convincing but false answers.
Junior experiments built on toy wrapper scripts fall apart under real workloads due to context window blowouts, runaway token bills, missing vector metadata, and absent role permissions.
A production-grade Retrieval-Augmented Generation (RAG) system with custom embeddings, permission tiers, and zero data leakage.
We review your repositories, export target documentation directories under mutual NDA, configure vector database infrastructure, and establish department RBAC rules.
We chunk and embed documents, wire the vector database, construct the re-ranking pipeline, calibrate similarity thresholds, and implement hard citation rules.
We run an automated 50-question adversarial accuracy test to confirm zero hallucinations, deploy the Slack bot or web widget, and provide full re-indexing scripts.
£3,450
One-time fixed setup • or $4,500 USD
Package Scope:
Security & Guarantee:
No. We configure all integrations through official Enterprise API endpoints that maintain explicit, legally binding zero-data-retention agreements.
We implement automated metadata timestamps and re-indexing webhooks. When you update a doc in Notion or GitHub, the vector store automatically invalidates and updates the corresponding chunks.
Yes. We can deliver this agent as a Lightning Web Component (LWC) that sits in the Salesforce utility bar, allowing agents to draft ticket responses based on internal knowledge articles.
We employ strict context-bounded retrieval. The model is instructed programmatically to ground its answers exclusively in the retrieved chunks. If the vector score falls below our relevance threshold, it outputs a fallback response instead of guessing.
Launch a production-ready, zero-hallucination AI knowledge assistant in under a week. Fully owned in your cloud infrastructure.
✔ Built by Waleed Rafique (Senior Backend & AI Integration Engineer).
✔ 100% private data protection under mutual NDA.
✔ Zero ongoing agency fees — you own the container.