Frequently Asked Questions

Business Questions

How much does it cost?

Implementation costs vary based on your document volume, complexity, and deployment approach. Typical ranges:

Initial Implementation:

Ongoing Costs:

What’s Included:

Most clients see ROI within 3-6 months through reduced manual search time, faster project cycles, and lower risk exposure.

How long does implementation take?

Typical timelines:

Phase 1 - Discovery & Architecture (2-3 weeks):

Phase 2 - Implementation (4-8 weeks):

Phase 3 - Deployment & Training (2-4 weeks):

Total: 8-15 weeks from kickoff to production, depending on complexity.

Faster Start Options:

What kind of ROI can I expect?

Quantifiable Benefits:

Time Savings:

Cost Reduction:

Productivity Gains:

Example ROI Calculation:

For a team of 10 people saving 15 hours/week each at $100/hour loaded cost:

This doesn’t include reduced risk exposure (missed clauses, compliance gaps) or competitive advantages from faster decision-making.

Is my data secure? What about compliance?

Security & Privacy:

Data Handling:

Deployment Options:

Compliance Capabilities:

No Data Training: We never use your documents to train models. Your data is yours—we just make it accessible to AI.

How does it integrate with our existing systems?

Seamless Integration:

Document Sources:

User Interfaces:

Authentication & Access:

Technical Integration:

Most integrations are straightforward—we work with your IT team to ensure smooth deployment without disrupting existing workflows.

Do you offer training and ongoing support?

Yes. Every implementation includes:

Initial Training:

Documentation:

Ongoing Support Options:

Standard Support (included for 90 days post-deployment):

Premium Support (optional):

Managed Services (optional):

Most clients start with standard support and graduate to premium support as they expand usage across the organization.

What if our documents change frequently?

Built for Dynamic Content:

Incremental Updates:

Update Frequency Options:

Change Management:

Example: Construction projects add RFIs and submittals daily. We can ingest new documents every hour automatically, making them searchable within the same business day—without disrupting ongoing queries or requiring manual reprocessing.

Can we start with a pilot project?

Absolutely—we recommend it.

Typical Pilot Approach:

Scope (4-6 weeks):

Pilot Investment:

Pilot Success Criteria:

After Pilot:

Most clients start with pilots—it’s the best way to prove value with limited risk and investment.

Understanding Large-Context AI

What is a context window?

A context window is the maximum amount of text (measured in tokens) that a large language model can process in a single interaction. Think of it as the model’s “working memory.” For example, if a model has a 128K token context window, it can process roughly 100,000 words at once—including both your prompt and the model’s response.

Modern LLMs have rapidly expanded context windows from 4K tokens (early GPT-3) to 32K, 128K, and even 1M+ tokens in the latest models. However, real-world enterprise documents often exceed even these impressive limits.

Why can’t I just use a model with a larger context window?

While larger context windows help, they face several practical limitations:

Cost: Processing costs scale with context length. A 1M token context window can cost 10-100x more than a 10K window per query.

Latency: Larger contexts take longer to process. What might take 2 seconds with 10K tokens could take 30+ seconds with 500K tokens.

VRAM Requirements: Longer contexts require exponentially more GPU memory. A model that fits in 24GB VRAM at 32K tokens might need 80GB+ for 128K tokens.

Quality Degradation: Even when models support large contexts, accuracy often degrades with very long inputs—the “lost in the middle” problem where models struggle to attend to information buried deep in context.

Document Size: Many enterprise document sets still exceed even 1M tokens—merger agreements with exhibits can reach 5M+ tokens, construction projects 10M+ tokens.

What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that allows LLMs to work with information beyond their context window limits by retrieving only relevant chunks when needed, rather than loading entire documents.

How it works:

  1. Chunking: Documents are split into smaller, manageable pieces
  2. Embedding: Each chunk is converted to a numerical representation (vector)
  3. Indexing: Vectors are stored in a searchable database
  4. Retrieval: When you ask a question, the system finds the most relevant chunks
  5. Generation: Only relevant chunks are sent to the LLM with your query

Benefits:

Best for: Question-answering, fact-finding, compliance checks, and targeted document searches.

What is GraphRAG and how is it different from standard RAG?

GraphRAG extends traditional RAG by building a knowledge graph that captures relationships between concepts, entities, and document sections.

Traditional RAG retrieves chunks based on semantic similarity to your query. It’s like searching for individual puzzle pieces.

GraphRAG understands how concepts connect. It retrieves not just relevant chunks, but also related information through the knowledge graph—like finding puzzle pieces that fit together.

Example: In a legal contract, traditional RAG might find the indemnification clause when you ask about liability. GraphRAG would also retrieve connected clauses about insurance requirements, dispute resolution, and limitation of liability—even if they don’t directly mention “indemnification.”

Best for: Complex reasoning across document sections, understanding relationships, regulatory compliance with interconnected requirements, and systems engineering with component dependencies.

What VRAM do I need for large-context processing?

VRAM (GPU memory) requirements depend on your approach:

Full Context Processing (loading entire documents):

RAG/GraphRAG Approaches:

Optimization Techniques:

For most enterprise applications, RAG-based approaches offer the best balance of performance, cost, and hardware requirements—making large-context processing accessible without expensive multi-GPU setups.

What is multi-layer summarization (RAPTOR)?

RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) builds a hierarchical understanding of documents through multiple layers of summarization.

How it works:

  1. Base Layer: Original document chunks with full detail
  2. Level 1: Summaries of related chunk clusters
  3. Level 2: Summaries of Level 1 summaries
  4. Top Level: Executive overview of entire document

When querying:

Example: For a 5,000-page construction specification:

Best for: Multi-volume document sets, enabling both overview and detail queries, and understanding document hierarchies.

How do you choose between RAG, GraphRAG, and summarization?

The optimal approach depends on your document characteristics and use cases:

Use Standard RAG when:

Use GraphRAG when:

Use Multi-Layer Summarization (RAPTOR) when:

Use Hybrid Approaches when:

Most enterprise applications benefit from hybrid approaches that combine techniques based on query type and document characteristics.

Does the newly released NVIDIA DGX Spark solve the context problem?

The NVIDIA DGX Spark represents a significant advancement in accessible AI infrastructure, but whether it “solves” the context problem depends heavily on your specific requirements, budget, and use case.

What the DGX Spark Offers:

The DGX Spark is NVIDIA’s entry-level AI workstation designed to bring enterprise-grade GPU computing to smaller teams and organizations. With configurations featuring multiple NVIDIA GPUs (typically RTX 6000 Ada or similar professional cards), it provides substantial VRAM—often 48GB per GPU, with multi-GPU setups reaching 96GB or more total VRAM.

For context processing, this means you can realistically handle:

The “Partial Solution” Reality:

While the DGX Spark enables impressive capabilities, it faces practical limitations for extreme context scenarios:

VRAM Constraints: Even with 96GB total VRAM in a dual-GPU configuration, you’re limited when processing truly massive contexts. Running a 70B parameter model at 128K context with minimal quantization approaches or exceeds this threshold. At 1M token contexts, you’d need 400GB+ VRAM—well beyond even high-end DGX Spark configurations.

Cost-Performance Trade-offs: The DGX Spark, while more affordable than full DGX systems ($30K-50K vs. $200K+), still represents significant capital expenditure. For many workloads, this upfront investment may not be optimal compared to alternatives.

Scaling Limitations: As your document corpus grows or team expands, scaling a DGX Spark-based solution requires additional hardware purchases. Cloud APIs scale on-demand without infrastructure management.

Better Solutions for Different Scenarios:

For Maximum Performance - NVIDIA RTX 6000 Blackwell Pro:

NVIDIA’s upcoming Blackwell architecture promises transformational improvements for large-context workloads. The RTX 6000 Blackwell Pro (expected 2025) is anticipated to offer:

A single RTX 6000 Blackwell Pro could potentially handle what currently requires dual RTX 6000 Ada cards, making 128K-256K context windows practical for 70B+ parameter models. For organizations planning significant AI infrastructure investments, waiting for or budgeting for Blackwell-based solutions may offer superior long-term value.

For Flexibility and Scalability - Paid APIs:

Commercial APIs from OpenAI, Anthropic, Google, and others offer compelling advantages:

Cost Efficiency: Pay only for what you use. For moderate workloads (under 10M tokens/month), APIs typically cost less than amortized hardware expenses.

Zero Infrastructure Management: No setup, maintenance, cooling, power, or IT overhead.

Instant Scaling: Handle 10 queries or 10,000 without infrastructure changes.

Access to Cutting-Edge Models: Immediate access to GPT-4, Claude 3.5 Sonnet, Gemini 1.5 Pro with massive context windows (up to 2M tokens) that would be impractical to self-host.

Hybrid Approaches: Use expensive long-context API calls only when needed, while handling routine queries on local infrastructure.

The Right Choice Depends on Your Needs:

Choose DGX Spark or similar local infrastructure when:

Choose RTX 6000 Blackwell Pro systems when:

Choose paid APIs when:

The TeraContext.AI Approach:

Our solutions are designed to work across all these deployment options—and often combine them intelligently. We might architect a system that:

The “context problem” isn’t solved by any single piece of hardware—it requires thoughtful architecture that matches techniques (RAG, GraphRAG, summarization) with infrastructure (local GPUs, APIs, or hybrid) based on your specific documents, queries, and constraints.

Can TeraContext.AI work with my existing LLM provider?

Yes. We’re platform-agnostic and integrate with all major LLM providers:

Commercial APIs:

Open Source Models:

Deployment Options:

We design solutions that work with your preferred models and can adapt as your needs or preferred providers change. Our focus is on the context management layer that makes any LLM work effectively with your large documents.

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