Custom LLM applications built on your data

Common Questions About Custom LLMs

What's the difference between RAG and fine-tuning?−

RAG (Retrieval Augmented Generation) is best for factual Q&A where you need sourced, up-to-date answers. It searches your documents and generates answers with citations. Fine-tuning is best when you need consistent style/format or specialized terminology. It trains the model on your examples. Most successful systems use both: fine-tune for your domain and style, RAG for factual grounding. RAG is cheaper ($50K-100K), faster to deploy, and easier to update. Fine-tuning costs more ($75K-150K) but works offline and delivers more consistent behavior.

How accurate can you make the AI?+

For factual Q&A (RAG-based): 90-95% accuracy on domain-specific questions, 98%+ on simple lookup. For generation tasks: 85-90% 'acceptable without edits.' For classification: 95%+ accuracy. We achieve this through evaluation datasets with ground truth, confidence scoring (AI tells you when unsure), human-in-the-loop for low-confidence cases, and continuous monitoring. We set realistic expectations upfront. If 99%+ accuracy isn't achievable with current technology, we'll tell you.

What if my data is messy or incomplete?+

Very common. 80% of projects start with data quality issues. Our approach: Phase 1 (Data Audit) assesses completeness, quality, structure, and estimates cleanup effort. Phase 2 (Data Preparation) does automated cleaning and manual curation for critical data. Phase 3 (Continuous Improvement) monitors answer quality and identifies gaps from usage. Sometimes we recommend a 1-2 month data prep phase before building AI. We can also start with your best data and expand coverage over time.

Can you integrate with our existing systems?+

Yes, we specialize in integration. Common integrations: Document repos (Confluence, SharePoint, Google Drive, Notion), Communication (Slack, Teams, email), Databases (SQL, NoSQL, APIs), CRM (Salesforce, HubSpot, Zendesk), File storage (AWS S3, Box, Dropbox), Authentication (SSO via Okta, Azure AD, Google). If you have APIs, we can integrate. If you don't, we can build scheduled exports, webhook listeners, custom connectors, or direct database access.

How do you handle sensitive data?+

Security and privacy by design. Data Protection: Encryption at rest (AES-256) and in transit (TLS 1.3), access controls, PII detection and masking, audit logs. Deployment Options: Your cloud account (data never leaves your tenant), on-premise (never leaves your network), VPC isolated, or air-gapped. Compliance: HIPAA BAA available, GDPR data locality and deletion, SOC 2 controls. Model Privacy: Azure OpenAI stays in your tenant, self-hosted models give complete control.

What happens when documents are updated?+

Automatic or scheduled updates. Real-time: Webhook triggers when documents change, auto-reindex with 5-15 minute lag. Scheduled: Daily/weekly/monthly re-indexing, incremental updates (only changed docs). Version Control: Maintain document history, query specific versions, track when information changed. Most systems use a combination: real-time for critical docs, nightly batch for everything else.

Can we use our own models or APIs?+

Absolutely. We design for flexibility. Model options: OpenAI (GPT-4, GPT-3.5), Anthropic (Claude 3.5), Azure OpenAI (in your tenant), AWS Bedrock (Claude, Llama, Mistral in your AWS), or self-hosted open-source (Llama 3.1, Mistral, Mixtral). We build an abstraction layer so you're not locked into one provider. You can start with OpenAI and switch to Claude later, A/B test models, or route based on query complexity.