ForthClover
RAG and Embedding

Give your AI the context to succeed

Deploy production-ready RAG systems that unlock your organization's knowledge.

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Give Your AI the Context to Succeed

Is generic AI falling short for your business?

Many businesses have critical data and context inside their organization that isn't available for generic models to use. Unlocking this knowledge is critical to delivering accurate, valuable, and reliable outputs. These are common roadblocks we hear about:
  • Your AI can't access the proprietary knowledge that makes your business unique
  • Data privacy requirements prevent using third-party AI services
  • Document processing at scale is too expensive
  • Existing solutions can't scale to billions of embeddings
Retrieval-Augmented Generation (RAG) architectures provide secure, scalable infrastructure for embedding and retrieving your organization's knowledge. If personalized context and data access have been holding you back, we can help.
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Limited availability

Context Engineering Package

Free RAG and Embedding Consultation

Included

  • Business requirements and AI goals
  • Data context and privacy requirements
  • Technical architecture recommendations (database, embedding, retrieval)
  • Implementation roadmap with cost estimates
  • Custom architecture proposal with cost calculator breakdown
  • Risk-Free Path qualification review
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Available to qualified ForthClover engagements

Backed by our Risk-Free Path

  • Risk-free strategy session
  • 100% satisfaction guarantee
  • No production commitment
Peter Lebiedzinski

Peter Lebiedzinski

Founder & CEO, Printpal.io

printpal

RAG and LLM expertise that shipped a POC our customers had been asking for.

Working with ForthClover on our Knowledge Base project has been a seamless experience. Their expertise in RAG applications and LLMs helped us ship a proof of concept that many of our customers had been asking for, and their professionalism made the collaboration both productive and enjoyable.

Your Guide to RAG and Embedding

Proven process for enterprise-grade RAG

Our consultative approach begins with listening to your business needs, challenges, and goals. We complete an early discovery process to ensure RAG is the right path and then deliver a no-pressure plan to build a proof-of-concept you can evaluate.

Phase 01

Discovery and Design

Your requirements drive everything we do. We'll review:

  • Business goals and context requirements
  • Data types, volumes, and freshness
  • Available tools and architectures for RAG

Deliverable: Architecture decision document with cost projections

Phase 02

Development

Build embedding and retrieval pipelines that meet your needs. In this phase we'll deliver:

  • Functional MVP of embedding and retrieval pipelines
  • Vector database designed for efficiency and scale
  • Search and retrieval functionality with citations

Deliverable: Working MVP you can test with real data

Phase 03

Optimization

Our engineers fine-tune retrieval performance for speed and efficiency. We'll provide:

  • Retrieval performance analysis (recall, precision)
  • Error handling and fallback review

Deliverable: Performance report with detailed cost analysis

Deliverable: Production system with monitoring, documentation, and handoff training

Ready to build with AI that understands your business?

No sales pitch. No obligation. Just a strategic conversation about what's possible.

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