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AI That Actually Knows Your Business

Build a RAG-powered knowledge base that answers questions from your docs, SOPs, and internal data, accurately, every time.

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Your team wastes hours finding answers

Information scattered across Notion, Drive, Confluence, and email

New hires take months to get up to speed

Same questions answered repeatedly by senior staff

Outdated documents lead to wrong decisions

No single source of truth for company knowledge

What We Implement

Document ingestion from any source

Source-cited AI answers

Automated knowledge sync

Access controls & permissions

Slack/Teams/web chat interface

Usage analytics & gap detection

Our Process

01

Knowledge Audit

We inventory your documents, identify key knowledge sources, and map information gaps.

02

Ingestion Pipeline

Build an automated pipeline to chunk, embed, and index your documents. Set up sync for live updates.

03

RAG Architecture

Implement retrieval + generation with source citations, confidence scores, and access controls.

04

Deploy & Maintain

Launch with a chat interface, API, or Slack bot. Monitor accuracy and add new knowledge sources over time.

Tools We Use

We select the best tools for your specific needs from our 45+ tool stack.

LangChain / LangGraphChatGPT for BusinessClaude for Business

Frequently Asked Questions

What document formats can it handle?

PDFs, Word docs, Google Docs, Notion pages, Confluence, web pages, CSVs, and more. We build custom connectors as needed.

How accurate are the answers?

RAG grounds AI in your actual data, dramatically reducing hallucinations. Every answer includes source citations you can verify.

Can we control who sees what?

Yes. We implement role-based access so departments only see knowledge relevant to them.

How does it stay up to date?

We set up automated sync pipelines that re-index documents when they change. No manual re-training needed.

Proven Results

Explore
Specap, Inc. case study: How Specap Scaled Their Specialist E-Commerce Platform
Electronics

How Specap Scaled Their Specialist E-Commerce Platform

5x growthAnnual Revenue
Case Study
PxlPeak case study: How PxlPeak Scaled Operations with AI Automation
Marketing

How PxlPeak Scaled Operations with AI Automation

85% fasterContent Production Speed
Case Study

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case study
Under 45 min
Content Production Speed

How PxlPeak Scaled Operations with AI Automation

As PxlPeak grew, the manual workload for lead qualification, content drafting, and client reporting became a scalability bottleneck. The agency was stuck in a high-headcount, low-margin trap, where every new client required a linear increase in manual labor. Additionally, our digital infrastructure lacked the technical hardening required for enterprise-level B2B sales, with fragmented systems and inconsistent data security protocols.

View case study
case study
493
Organic Clicks (90 days)

VM Power Construction: 0 to 493 Organic Clicks and 212K Impressions in 90 Days

VM Power Construction & Remodeling LLC is the parent of a four-division Lehigh Valley contracting family: VM Power Construction (general remodeling, formed 2018 with founder Vincent Karaca's trade history since 2003), VM Power Decks (the legacy V&M Power brand since 2005), VM Power Exteriors (roofing, siding, gutters), and VM Power Flooring (hardwood, LVP, tile, refinishing). Each division operates independently, its own brand, phone, office, team, and website, while the parent LLC holds the licenses (PA HIC #158550, NJ HIC #13VH11744800) and the BBB A-rated profile with 448 reviews on Google. Despite 22+ years of trade reputation and a real customer base, the four division websites were ranking nowhere outside of direct branded searches. Schema across the four sites operated in entity-graph silos, no parent-child Organization references, conflicting NAP, fragmented aggregateRating placements, and zero shared content cluster. None of the four domains showed up for the high-intent informational queries (renovation cost, permit guidance, ADU regulations) that Lehigh Valley homeowners actually search before hiring a contractor. The core problem wasn't visibility, it was that Google had no way to recognize the four divisions as one credentialed family operating across six office locations.

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