Generative AI Development for Enterprise Workflows

Generative AI Development , Frequently Asked Questions

• What is generative AI development and what services does it include?

  Generative AI development is the practice of building production applications powered by foundation models , large language models like GPT-4, Claude, and Gemini, image generation models like Midjourney and DALL-E, code generation models, and multimodal models. Services include LLM fine-tuning, retrieval-augmented generation (RAG) systems, enterprise chatbots and assistants, AI agents for multi-step workflows, custom content generation pipelines, code generation copilots, and document intelligence systems. ScalaCode delivers the full stack: model selection, prompt engineering, data pipelines, integration with enterprise systems, safety guardrails, evaluation frameworks, and ongoing operations.

• How long does it take to build a generative AI application?

  A typical generative AI project ships in 12 to 20 weeks from discovery to production. Proof of concept prototypes ship in 4-6 weeks. Production MVP systems (RAG knowledge assistant, customer service bot, document intelligence tool) typically take 10-16 weeks. Enterprise-scale deployments with multi-agent orchestration, fine-tuned models, and deep integration across several systems can run 20-30+ weeks. Simpler API-integration projects (ChatGPT embedded in an existing application) can ship in 4-8 weeks.

• Which foundation models do you work with?

  We are model-agnostic and work with the full range of commercial and open-source foundation models. Commercial: OpenAI GPT-4, GPT-4o, GPT-4 Turbo; Anthropic Claude Sonnet, Opus, and Haiku; Google Gemini 1.5 Pro, Flash, and Nano. Open-source: Meta LLaMA-3 (8B, 70B, 400B variants), Mistral and Mixtral, Microsoft Phi, Google Gemma. Model selection is driven by your use case requirements , long-context reasoning (Claude), multimodal (Gemini), cost-efficient high-volume (fine-tuned LLaMA-3), enterprise compliance (Azure OpenAI), or data-residency (self-hosted open-source).

• How do you handle hallucinations and ensure generative AI accuracy?

  We prevent hallucinations through multiple layered techniques. RAG grounding ensures every answer cites retrieved source documents, making the model’s output verifiable against truth. Structured output validation enforces response formats at the API level. Confidence scoring and refusal patterns handle out-of-scope queries explicitly. Continuous evaluation against ground-truth datasets catches accuracy regressions. For high-stakes applications, we add human-in-the-loop review checkpoints. Tools like LangSmith, Galileo, and custom evaluation harnesses automate regression testing before every prompt or model change. Typical production accuracy on well-scoped enterprise RAG use cases is 90-95%.

• What is RAG and when should I use it?

  Retrieval-Augmented Generation (RAG) is a pattern that combines a foundation model with a retrieval system over your proprietary documents. When a user asks a question, the system first retrieves relevant documents from a vector database, then feeds those documents as context to the LLM which generates an answer grounded in your data. RAG is the default pattern for enterprise knowledge applications because it provides accuracy grounded in your specific information, keeps proprietary data secure (documents are retrieved, not trained into the model), allows real-time updates (edit a document, RAG uses the new version immediately), and provides citation links back to source material. See our dedicated RAG development services page for deeper coverage.

• Should we fine-tune a model or use prompt engineering?

  Start with prompt engineering and RAG; move to fine-tuning only when you’ve validated the use case and need additional capability that prompts cannot deliver. Prompt engineering is faster, cheaper, easier to iterate, and works on any model. Fine-tuning requires training data, infrastructure, evaluation, and commits you to a specific base model. Fine-tuning makes sense when: you need the model to adopt a very specific tone, format, or domain vocabulary; you’re running at enough scale that the smaller fine-tuned model’s lower inference cost pays back the training investment; RAG has hit its accuracy ceiling for your use case. In most enterprise projects, 80%+ of value is captured via prompt engineering and RAG, with fine-tuning reserved for specific high-value use cases.

• How do you handle data privacy and security with generative AI?

  Data privacy is foundational to our generative AI architecture. For commercial APIs (OpenAI, Anthropic, Google), we use enterprise deployments (Azure OpenAI, AWS Bedrock, Vertex AI) that provide contractual data-retention commitments and zero-retention options where required. For strict data-residency requirements, we deploy open-source models (LLaMA-3, Mistral) on your own cloud infrastructure so no data leaves your environment. We apply PII redaction at the prompt level, output filtering for PII leaks, audit logging of every AI request, role-based access controls inheriting source-system permissions, and encryption in transit (TLS 1.3) and at rest (AES-256). Our practices support SOC 2 Type II, HIPAA (with BAA), GDPR, CCPA, and ISO 27001.

• What does generative AI development cost?

  Generative AI development costs typically range from $25K to $500K+ depending on scope. Proof-of-concept prototypes start at $25K-$60K over 4-6 weeks. Production MVP systems (RAG knowledge assistant, custom chatbot, document intelligence) typically run $80K-$200K over 12-16 weeks. Enterprise multi-system deployments with fine-tuned models and integration across CRM/ERP commonly run $250K-$500K+ over 6-12 months. Ongoing operational costs include foundation model API spend (typically $500-$50K per month depending on volume), infrastructure (hosting, vector DB, monitoring), and managed service fees if you want us to operate the system. We provide fixed-price project quotes after Phase 1 discovery.

• How do generative AI agents differ from chatbots?

  Chatbots are conversational interfaces that answer questions or provide information , one turn in, one response out. AI agents are systems that autonomously execute multi-step tasks , plan, retrieve information, use tools, take actions in external systems, verify outcomes, and iterate until the task is complete. A chatbot might answer “what’s our return policy” (information retrieval). An AI agent might handle “process this customer refund” (read the policy, verify eligibility, execute the refund in the payment system, update the CRM, send confirmation email). Agents are appropriate when a business outcome requires multiple steps across multiple systems with conditional logic. Chatbots are appropriate when you need fast, accurate information access or simple transactional flows. We build both , with distinct patterns for each.

• Do you offer ongoing operations after launch?

  Yes. Most generative AI systems require ongoing operations , prompt refinement, foundation model upgrades, cost optimization, evaluation against drift, safety guardrail tuning. We offer managed operations (we run the system end-to-end), shared operations (we handle AI-specific tasks while your team owns general ops), retainer-based support (on-demand enhancements), and full knowledge-transfer handoff (we train your team). Ongoing activities typically include foundation model version migrations (GPT-4 → GPT-5), prompt library versioning, new evaluation dataset addition, cost alerts and optimization, new feature development, and security patch application. Most of our enterprise engagements include a 6-12 month operations phase post-launch.