How do you keep enterprise data private when using LLMs?

Naraway never sends enterprise data to LLMs via consumer-facing APIs that use data for training. All LLM integrations use enterprise API agreements (OpenAI Enterprise, Anthropic API, or Google Cloud Vertex AI) which guarantee data is not used for training. For highest sensitivity data (medical, legal, financial), Naraway deploys open-weight models (Llama 3, Mistral) in your own cloud infrastructure — data never leaves your VPC. For hybrid setups, sensitive fields are redacted before sending to external APIs and re-inserted from your data layer after the LLM response is processed.

What types of internal processes benefit most from AI automation?

The highest-ROI internal AI use cases are: document extraction and classification (processing invoices, contracts, compliance documents — saving 2-5 hours per document reviewer per day), knowledge search (replacing 'email HR' queries with instant accurate answers from your policy library), meeting summarisation and action item extraction (removing manual meeting notes entirely), first-draft generation for reports, proposals, and RFP responses (cutting writing time by 60-80%), and customer support triage (classifying and routing incoming requests before a human sees them). Naraway prioritises use cases with measurable time savings, not AI experiments that sound impressive but lack clear ROI.

How long does it take to build an internal AI tool?

A focused internal AI tool — for example, a document Q&A system over your company's policy library, or a contract extraction tool — typically takes 4-8 weeks to build, test, and deploy. This includes: data ingestion pipeline (parse and chunk your documents), vector embedding and storage (Pinecone, Qdrant, or pgvector), RAG pipeline with LLM integration, admin interface for document management, user interface for your team, and evaluation benchmarks to measure answer quality before launch. Naraway does not launch AI tools without an evaluation suite — 'it seems to work' is not an acceptance criterion.

Global AI Engineering — GPT-4, Claude, Gemini on Your Data

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Your Data. Not a
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Q: What is RAG and why is it better than fine-tuning for internal knowledge tools?

RAG (Retrieval-Augmented Generation) retrieves relevant documents from your knowledge base at query time and provides them as context to the LLM, so answers are grounded in your actual data rather than the model's training data. Fine-tuning bakes knowledge into the model weights permanently — it is expensive, requires large amounts of training data, cannot be updated without retraining, and does not cite sources. For internal knowledge tools (policies, procedures, product documentation), RAG is almost always the right choice: cheaper, updatable by non-engineers, and produces traceable answers with source citations that build user trust.

Generic AI tools fail in enterprise contexts because they do not know your policies, your products, your clients, or your processes. Naraway builds internal AI tools trained on your specific knowledge — document intelligence, knowledge bases, workflow automation, and data extraction that runs on your infrastructure, with your data never leaving your control.

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70%Of enterprise knowledge workers say finding information internally takes more than 30 minutes daily (McKinsey)

4-8Weeks to build and deploy a focused internal AI tool — document Q&A, extraction, or report generation

ZeroTraining data usage — all LLM integrations via enterprise APIs that do not use your data for model training

RAGRetrieval-Augmented Generation — AI answers grounded in your documents with source citations

Use Cases

Four Internal AI Tools with Measurable ROI — Not AI Experiments

Naraway focuses on AI use cases where the time savings and error reduction are measurable before the project begins. These four categories consistently deliver the highest ROI for enterprise teams globally.

Document Intelligence and Extraction

Invoices, contracts, compliance forms, and purchase orders processed automatically — fields extracted, validated, and pushed to your ERP or CRM without human data entry.

Saves 3-6 hours per document reviewer per day

PDF, Word, image, and scan ingestion with OCR

Named entity extraction (dates, amounts, parties, clauses)

Confidence scores per extracted field — low-confidence sent for human review

Audit trail — every extraction logged with source document and model version

Internal Knowledge Base and Policy Q&A

Your HR policies, product documentation, legal playbooks, and operational SOPs become searchable through a conversational interface. Answers cite the exact source document and page.

Eliminates 80% of "email HR / email legal" queries

RAG pipeline over your document library — answers grounded in your content

Source citations with clickable links to the original document

Admin interface for non-engineers to add, update, and remove documents

Query analytics — see what your team is searching for most often

Customer Support Triage and Response Drafts

Incoming support tickets classified, priority-assigned, and routed automatically. First-response drafts generated from your product documentation for agent review before sending.

Cuts first-response time from hours to under 5 minutes

Ticket classification: bug report, feature request, billing, account access

Sentiment detection — escalates frustrated or at-risk customers automatically

Draft response grounded in your product docs — agent edits before sending

CSAT feedback loop to improve draft quality over time

Report and Proposal Generation

Weekly status reports, client proposals, RFP responses, and market research summaries generated as structured first drafts from raw data — your team edits, not writes from scratch.

Reduces report writing time by 60-80% per document

Structured templates define output format — consistent, on-brand output

Data ingestion from spreadsheets, databases, and APIs populates sections automatically

Output to Word, PDF, or directly into your CRM's proposal fields

Version tracking — all generated drafts stored with input data for audit

Technical Architecture

How RAG Works — AI Answers Grounded in Your Documents

Retrieval-Augmented Generation is the architecture behind every knowledge base and document Q&A tool Naraway builds. It grounds LLM responses in your actual data, produces source citations, and updates instantly when you add new documents — no retraining required.

Your Knowledge Base

Document Ingestion

PDFs, Word files, Notion pages, Confluence, SharePoint, website content — ingested via connectors or file upload

Chunking and Embedding

Documents split into semantic chunks. Each chunk embedded into vector representation via OpenAI or local embedding model.

Vector Database

Embeddings stored in Pinecone, Qdrant, Weaviate, or pgvector. Semantic similarity search retrieves relevant chunks at query time.

↔

LLM

GPT-4o
Claude 3.5
Gemini 1.5
or Llama 3

↔

User Interaction

User Query

User asks a question in natural language. Query is embedded using the same model and used to search the vector store.

Context Assembly

Top-k most relevant chunks retrieved and assembled into a prompt with the user question. The LLM sees only your documents — not general internet knowledge.

Answer with Citations

LLM generates answer grounded in retrieved documents. Source citations with document name and page number shown alongside the answer.

LLM Selection

GPT-4, Claude, or Gemini — Which Model for Which Task

Naraway deploys multi-model architectures — different tasks route to different models based on context window, cost, latency, and task-specific strengths. No single model is best at everything.

OpenAI

GPT-4o / GPT-4 Turbo

Best general-purpose reasoning, function calling, and structured JSON output. Largest ecosystem of tools and integrations.

Context window128K tokens

Best atReasoning, code gen, structured output

MultimodalYes — vision, audio

Best forGeneral tools, code review, data extraction

Anthropic

Claude 3.5 Sonnet / Claude 4

Exceptional at long-document analysis, nuanced writing, and following complex multi-step instructions. Preferred for legal, compliance, and policy document tools.

Context window200K tokens

Best atLong documents, nuanced writing, instruction following

MultimodalYes — vision

Best forContract analysis, policy Q&A, RFP responses

Google DeepMind

Gemini 1.5 Pro / Gemini 2.0

Longest context window available (1M tokens), strong multimodal capabilities including video understanding. Best for entire-codebase or entire-document-library analysis.

Context window1M tokens

Best atVery long context, multimodal (video, images)

MultimodalYes — text, image, audio, video

Best forCodebase analysis, large document libraries, video understanding

3-minute demo: Naraway's internal knowledge base tool in action — an HR team querying their entire policy library in natural language with source citations
Replace with: Screen recording of internal AI tool demo

Data Privacy

Your Data Stays in Your Infrastructure. Always.

The biggest concern global enterprise teams have about AI tools is data privacy. Where does our data go? Is it used to train models? Who can see it? Naraway addresses this at the architecture level — not with a terms-of-service clause.

All LLM integrations are through enterprise API agreements where data is contractually not used for training. For regulated industries (healthcare, finance, legal), Naraway deploys open-weight models (Llama 3, Mistral, Qwen) inside your own cloud VPC — no external API calls, no data egress.

Enterprise API agreements — OpenAI Enterprise, Anthropic API, Google Cloud Vertex AI

Option to deploy open-weight models fully on-premise or in your VPC

PII redaction before external API calls — sensitive fields stripped and re-inserted

Full audit log of every AI query — user, input, output, model, timestamp

GDPR and HIPAA compliant deployment architectures available

Infrastructure diagram showing on-premise LLM deployment — Llama 3 running inside enterprise VPC with no external API calls, surrounded by internal data stores Replace with: Architecture diagram of on-premise AI deployment

Build Process

From Use Case Selection to Deployed AI Tool with Evaluation Benchmarks

Naraway does not deploy AI tools without measuring their performance. Every internal AI tool ships with an evaluation suite so you know the tool is reliable before your team depends on it.

Use Case Selection

ROI mapping across candidate use cases. The one with clearest measurable time savings is built first.

Data Audit

Your source documents reviewed for quality, coverage, and sensitivity. Data preparation and cleaning plan defined.

RAG Pipeline Build

Ingestion, embedding, vector store, retrieval, and LLM integration built and tested on your real data

Evaluation and Benchmarking

100+ test question set with expert-verified answers. Accuracy, hallucination rate, and citation quality measured before launch.

Deploy and Iterate

Tool deployed with admin interface, user feedback loop, and monthly evaluation runs to catch quality drift

Frequently Asked

Internal AI Tools — Questions from Enterprise Teams

RAG retrieves relevant documents from your knowledge base at query time and provides them as context to the LLM, so answers are grounded in your actual data. Fine-tuning bakes knowledge into the model weights permanently — it is expensive, requires large amounts of training data, cannot be updated without retraining, and does not cite sources. For internal knowledge tools, RAG is almost always the right choice: cheaper, updatable by non-engineers, and produces traceable answers with source citations that build user trust.

Model choice depends on the task. GPT-4o excels at general reasoning, code generation, and structured output. Claude 3.5 Sonnet and Claude 4 excel at document analysis, long-context tasks (up to 200K tokens), and nuanced writing — particularly for legal and compliance tools. Gemini 1.5 Pro has the longest context window (1M tokens) and strong multimodal capabilities. Naraway deploys multi-model architectures where different tasks route to different models based on cost, latency, and quality requirements.

Naraway never sends enterprise data to LLMs via consumer-facing APIs that use data for training. All LLM integrations use enterprise API agreements (OpenAI Enterprise, Anthropic API, or Google Cloud Vertex AI) which guarantee data is not used for training. For highest-sensitivity data, Naraway deploys open-weight models (Llama 3, Mistral) in your own cloud infrastructure — data never leaves your VPC.

The highest-ROI internal AI use cases are: document extraction and classification (processing invoices, contracts, compliance documents — saving 2-5 hours per document reviewer per day), knowledge search (replacing email-HR queries with instant accurate answers from your policy library), meeting summarisation and action item extraction, first-draft generation for reports and RFP responses (cutting writing time by 60-80%), and customer support triage. Naraway prioritises use cases with measurable time savings, not AI experiments that sound impressive but lack clear ROI.

A focused internal AI tool — a document Q&A system over your company's policy library, or a contract extraction tool — typically takes 4-8 weeks to build, test, and deploy. This includes: data ingestion pipeline, vector embedding and storage, RAG pipeline with LLM integration, admin interface for document management, user interface for your team, and evaluation benchmarks to measure answer quality before launch. Naraway does not launch AI tools without an evaluation suite — "it seems to work" is not an acceptance criterion.

AI Tools That Work onYour Data. Not aGeneric Chatbot.