Conductor vs Raw LLMs
Large language models provide powerful capabilities, but enterprise document intelligence requires more than API access. This guide explains what you need to build and helps you decide whether to build or buy.
What you need to build
Raw LLM APIs provide language understanding. Document intelligence requires a complete stack around that capability. Here is what production deployment typically involves.
Document parsing
Medium complexityExtract text, tables, and structure from PDFs, Word docs, images, and other formats. Handle OCR for scanned documents.
Maintenance: Ongoing updates for new formats
Chunking strategy
High complexitySplit documents into appropriately sized segments that preserve context and meaning for retrieval.
Maintenance: Tuning required per document type
Vector database
Medium complexityStore and query document embeddings efficiently. Manage index updates, scaling, and performance.
Maintenance: Infrastructure management, scaling
Retrieval pipeline
High complexityFind relevant document chunks for each query. Implement hybrid search, reranking, and relevance tuning.
Maintenance: Continuous optimisation
Prompt engineering
High complexityDesign prompts that produce accurate, consistent responses. Handle edge cases and failure modes.
Maintenance: Updates with model changes
Citation system
High complexityTrack which document sections informed each response. Provide verifiable source references.
Maintenance: Accuracy monitoring
User interface
Medium complexityBuild search interface, document viewer, citation display, and administrative controls.
Maintenance: Feature development
Security layer
High complexityImplement authentication, authorisation, data encryption, and audit logging.
Maintenance: Security updates, compliance
Hidden complexities
Building document intelligence looks straightforward in tutorials. Production systems encounter complexities that are not obvious until you are deep into development.
Chunking is harder than it looks
Naive chunking (splitting by character count) destroys context. Effective chunking requires understanding document structure, preserving tables intact, handling headers/footers, and maintaining semantic coherence. Each document type may need different strategies.
Retrieval quality determines output quality
If retrieval returns the wrong chunks, even the best LLM produces incorrect answers. Achieving high retrieval accuracy requires hybrid search (semantic + keyword), reranking, query expansion, and extensive tuning.
Citations require architectural decisions
Accurate citations need to be built into the system from the start. Retrofitting citation tracking to an existing RAG pipeline is significantly more difficult than building it in initially.
Prompt brittleness
Prompts that work well in testing often fail on edge cases in production. Model updates can change behaviour unexpectedly. Maintaining prompt quality requires ongoing monitoring and adjustment.
Hallucination mitigation
LLMs can generate plausible-sounding but incorrect information. Building reliable document Q&A requires grounding mechanisms, confidence scoring, and fallback behaviours.
Scale and performance
Systems that work for hundreds of documents often struggle with thousands. Vector search performance, embedding costs, and response latency all require careful architecture.
Team requirements
Building enterprise-grade document intelligence typically requires a cross-functional team. Here are the roles commonly needed.
Total estimate: 4-6 FTEs for initial build, plus ongoing maintenance capacity. Actual requirements vary based on scope, existing infrastructure, and team experience.
Capability comparison
A detailed look at what building with raw LLMs involves versus what Conductor provides out of the box.
When to use each approach
Building with raw LLMs and using a platform like Conductor serve different needs. The right choice depends on your priorities, resources, and timeline.
Build with raw LLMs when:
Highly custom requirements
When your use case requires specific model fine-tuning, custom architectures, or capabilities beyond document Q&A that justify the investment.
Research and experimentation
When exploring what is possible with LLMs, testing hypotheses, or building proof-of-concepts before committing to a production system.
Deep ML expertise available
When your team has significant experience building production ML systems and can leverage that expertise for competitive advantage.
Long-term platform investment
When document intelligence is a core competency you want to own and develop over years, not a tool you need to deploy quickly.
Use Conductor when:
Document intelligence is a tool, not the product
When you need document search and Q&A capabilities to support your business, not as the core of what you sell.
Time-to-value matters
When you need working document intelligence in weeks rather than months, and want to focus engineering resources on your core product.
Enterprise requirements
When you need ISO certifications, UK data residency, audit logging, and enterprise security controls without building them yourself.
Citation accuracy is critical
When your use case requires verifiable, page-level source attribution that users can trust and verify.
Limited ML expertise
When your team is strong in software engineering but does not have deep experience with RAG architectures and retrieval tuning.
Predictable costs
When you prefer subscription pricing over variable infrastructure costs and ongoing development investment.
Decision framework
Use these questions to guide your build vs buy decision.
Is document intelligence core to your product?
If document Q&A is a supporting capability rather than your main offering, building from scratch may not be the best use of engineering resources.
What is your timeline?
Building production-ready document intelligence typically takes 3-6 months. If you need capabilities sooner, a platform approach offers faster time-to-value.
What expertise does your team have?
Building effective RAG systems requires specific experience with retrieval, embeddings, and prompt engineering. Teams without this background face a steeper learning curve.
What are your compliance requirements?
Enterprise requirements like ISO 27001, UK data residency, and audit logging add significant development effort. Platforms that already meet these requirements reduce compliance burden.
How important are citations?
If your users need to verify answers against source documents, citation accuracy is critical. Building reliable citation systems requires significant architectural investment.
Evaluate both approaches
The right choice depends on your specific requirements, timeline, and team capabilities. We can discuss your use case and help you understand whether Conductor fits your needs.