Internal Search & Assistants

Internal Knowledge Assistants

Empower your engineering and operations teams with verifiable natural language search across internal wikis and SOPs.

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Operational Problems Solved

Turn scattered PDFs, Notion docs, Confluence spaces, and architecture repositories into an instant, citation-backed internal Q&A engine.

Engineers spending hours searching for past architecture decisions or deployment runbooks

New employees struggling through fragmented onboarding documentation

Inconsistent adherence to company Standard Operating Procedures (SOPs)

Sensitive company knowledge leaking into unapproved public chat AI tools

System Architecture & Execution Flow

DETERMINISTIC PIPELINE

Document Ingestion

Monitors internal drives and Notion/Git repositories for updates.

Chunk & Embed

Splits texts into semantic chunks and creates high-dimensional vector embeddings.

Query Routing

User query is embedded and matched against vector database using cosine similarity.

Synthesize & Cite

LLM synthesizes concise answers with exact clickable citations to original pages.

What We Deliver

Automated document ingestion and chunking pipeline supporting Markdown, PDF, DOCX, and Notion

Hybrid vector and BM25 lexical search index

Web interface with direct paragraph-level source document citations

Role-Based Access Control (RBAC) ensuring employees only see documents they have permission to access

Deployment to private cloud infrastructure (AWS/GCP/Azure)

Human-in-the-Loop Review Points

Internal staff can flag incorrect answers with 1 click to improve retrieval ranking
Content curators can pin canonical answers for critical compliance policies

Data Privacy & Security Boundaries

Hosted entirely inside your private VPC or dedicated tenant
Strict document permission filtering applied before vector retrieval

Technical Questions

How do you control hallucinations when answers are not in our documentation?

We use strict retrieval grounding, confidence thresholding, and negative rejection prompts: if retrieved context does not contain verifiable evidence, the assistant is constrained to acknowledge the missing information rather than generating speculative answers.

Scope a Production Pilot

We typically deliver a functional staging proof-of-concept for this capability within 2–3 weeks.

Discuss Architecture

Fixed-price scoping milestone

Direct discussion with senior AI engineer

Confidential NDA available

Recommended Tech Stack

Next.jsPythonpgvectorFastAPIOpenAI / Claude EnterpriseDocker

COLLABORATE & SHIP VALUE

Ready to scope your Internal Knowledge Assistants?

Share your current tech stack and dataset requirements. We will prepare an architecture proposal within one business day.

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< 24h Response

Mutual NDA Guaranteed

Zero Obligation Scoping

Zero obligation • Direct technical conversation with engineers • NDA upon request