For B2B SaaS: Fewer Escalations, Faster Answers
Higher deflection, shorter handling times, and consistent resolutions across teams.
RAG Retrieval Accuracy
Our hybrid Graph + Vector retrieval finds the right evidence before the model answers. You get traceable responses, clear citations, and dashboards that show accuracy and ROI.
Why Retrieval Accuracy Matters
Wrong answers cause escalations, lost revenue, and brand risk. Accurate retrieval lifts conversion for Shopify, cuts ticket load for B2B SaaS, and speeds spec-to-part selection in Industrial settings.
For Shopify & Shopify Plus: Revenue-Safe Answers
Precise pre-purchase guidance reduces wrong-fit returns. Policies and catalogue data are retrieved correctly, every time.
For Industrial Distributors & Manufacturers: Spec-to-Part Clarity
Graph-aware retrieval removes ambiguity across datasheets, catalogues, and standards, so engineers get the exact part first time.
How Our Hybrid Retrieval Works
We combine Graph and Vector signals with business constraints to maximise precision, minimise hallucinations, and keep answers auditable.
1
Knowledge Graph: Contains Relationships, Constraints, Ground Truth
Encodes entities, versions, and rules so answers respect product, policy, and compliance relationships.
2
Vector Search: Similarity search on semantic chunks with metadata
Finds the most relevant passages using embeddings enriched with source, version, and recency tags.
3
Rank Fusion: Combine graph semantic matches; rerank for accuracy
Merges graph matches and vector candidates, then reranks with domain signals for the best, explainable result.
4
Confidence Scoring, Abstention & Guardrails
If confidence is low, we abstain and hand over with citations and a proposed fix. Policy guardrails block unsafe or out-of-scope answers.
KB Watchdog: Keep the Knowledge Base Consistent
KB Watchdog monitors changes, flags conflicts, and stops drift before it reaches customers.
Contradiction & Drift Detection
Highlights conflicting entries, outdated versions, and subtle regressions as your content evolves.
Coverage Gaps & Freshness Alerts
Surfaces unanswered intents and stale articles, with suggestions for new or updated content.
Human-in-the-Loop Workflow
Simple review and approve flows route issues to owners with diffs, impact, and one-click fixes.
Measuring Accuracy (and Proving It)
Accuracy is tracked continuously and improved via test sets, live feedback, and transparent dashboards.
What We Track
Deflection, top-k precision/recall, no-answer rates, handover quality, conversion assists, and content gap trends.
Offline & Online Evaluation
Golden-set tests and synthetic probes offline; A/B and interleaving online with human feedback loops.
The Dashboard
Real-time charts with drill downs to queries, sources, and versions so you can see what changed and why.
Works With Your Stack
Brand-safe logo rows for Shopify, Zendesk, Intercom, and custom systems. See Integrations for details.
Data Sources We Index
Help centres, policy documents, product catalogues, PDFs, CSV/SQL, storage buckets, and internal wikis.
Real-Time & Versioned Indexing
Incremental updates keep indexes fresh while preserving version history for audit and rollback.
Handover, Ticket Dispatch & Suggested Solutions
Every handover includes an AI summary, citations, and a proposed fix grounded in your KB.
Structured Ticket Summaries
Problem statement, steps already taken, source references, and a clear next action.
Dispatching & Next-Best-Action
Smart queueing, macros, and related articles to speed resolution and keep context intact.
Where We Outperform Common Alternatives
A side-by-side table shows where our approach wins on accuracy, governance, and effort to maintain.
| Alternative Approach | Limitations | Our Advantage |
|---|---|---|
| Generic “LLM Chat” Widgets | Often unpredictable and light on controls. | We add smart chunking, metadata, and graph constraints for reliable answers. |
| Single-Vector RAG | Misses relational rules and versioning. | We fuse graph constraints and reranking to raise precision. |
| Rules-Only or Keyword Search | Brittle and high-maintenance. | Our symbolic-plus-semantic blend is robust as content changes. |
Implementation Approach
A de-risked, phased rollout that proves value early and scales safely.
1
Discovery & KB Audit
Inventory sources, map entities, and surface contradictions with KB Watchdog.
2
Pilot Scope & Golden Set
Define intents, acceptance criteria, and the evaluation plan with a curated test set.
3
Production Hardening
Security reviews, scaling and observability, SLOs, and fallbacks before wider launch.
Client Spotlight: Math Angel
"Math Angel" uses our agent and analytics to deliver precise, personalised student support and faster content discovery without leaking context between sessions.
What Stood Out
Hyper-personalisation from graph-linked user, curriculum, and content data.
Pricing & Next Steps
Pricing reflects data volume, connectors, and governance requirements. See Pricing for packages and FAQs.
Book a Consultation (Primary CTA)
A 30-45 minute technical session to review sources, target outcomes, and the fastest path to launch.
FAQs
Graph constraints + vector search + reranking, with KB Watchdog to prevent drift, confidence gating, and full analytics.
Yes, when the content is in scope. Otherwise we abstain or hand over with context.
Retrieval-first generation, strict confidence thresholds, policy guardrails, and an explicit abstain path.
Yes. We use multilingual embeddings and content tags, with a site-wide language switcher.
Data isolation, role-based access, and audit trails. See our Security & Compliance page for details.
Access to KB sources, catalogue samples, policies, and 20-50 example queries to build the golden set.
Ready to Improve Your Retrieval Accuracy?
Get traceable, accurate answers backed by hybrid Graph + Vector technology.
