Key Takeaways

• Trusted AI drives adoption

• Retrieval-first systems reduce risk

• Hybrid search improves precision

• Governance enables scale

• Strategy accelerates deployment

Challenge

Becht’s knowledge ecosystem contained diverse technical assets, including PDFs, webinars, calculators, interactive PFDs, and structured Q&A records. Existing search relied on string matching, which struggled with industry jargon, abbreviations, and complex technical queries.

Users often failed to surface relevant materials and instead relied on manual expert requests. This limited content utilization, slowed response times, and reduced the commercial impact of premium subscriptions.

In addition, strict liability and safety requirements prevented the use of generative AI for open-ended answers, requiring a retrieval-only approach with strong source attribution and governance.

Strategy

SFAI Labs conducted a comprehensive intake assessment covering user workflows, data sources, security requirements, and commercial KPIs. We defined a product roadmap centered on trusted retrieval, progressive automation, and enterprise compliance.

The strategy emphasized:

• Hybrid search combining vector and keyword retrieval

• Advanced query expansion for technical language

• Dedicated indexing for documents and Q&A content

• Tag-based filtering for disciplines, equipment, and units

• Governance and auditability from day one

This approach aligned technical design with Becht’s commercial and operational objectives.

Solution

SFAI Labs designed and implemented a modular RAG platform integrated into BechtCONNECT, featuring:

• Hybrid semantic and keyword search

• Azure AI Search and embedding pipelines

• Query expansion and multilingual support

• Structured ingestion for documents, webinars, calculators, and iPFDs

• Dual vector stores for documents and Q&A

• Tag extraction and synonym mapping

• Secure, read-only architecture

The system ensured that all responses were grounded in verified source material, preserving trust while significantly improving discovery.

Execution

Week 1–4: Discovery, stakeholder alignment, and data assessment
Week 5–8: System architecture, security design, and infrastructure setup
Week 9–14: Knowledge ingestion, indexing, and tagging pipelines
Week 15–18: RAG integration, API development, and workflow orchestration
Week 19–24: Performance validation, governance hardening, and enterprise rollout

Continuous validation cycles ensured performance, security, and reliability throughout deployment.

Results

• Increased document engagement

• Faster knowledge discovery

• Reduced expert dependency

Business Value

The platform enabled Becht to unlock the commercial value of its knowledge assets by increasing utilization of premium content and improving customer experience. Engineers gained faster access to trusted information, while leadership gained visibility into engagement metrics and usage patterns.

The system reduced operational friction, supported upskilling claims, and created a scalable foundation for future AI-powered offerings.

Why SFAI Labs

SFAI Labs combined AI product strategy, advanced engineering, and accelerated execution to deliver a production-ready retrieval system aligned with Becht’s governance requirements. Our lab model ensured rapid validation, deep domain alignment, and measurable commercial impact.