What typical migration looks like:
The Protocloud Generative AI approach:
Demo-quality code, fails in production 
MLOps, evaluation, monitoring from day one 
Custom GenAI projects with Protocloud range from $10,000 (POC/prototype: basic RAG or chatbot) to $500,000+ (enterprise multi-agent platform with full MLOps). A production-ready RAG system for a mid-market business typically costs $30,000–$80,000 including data pipeline, vector database, LLM integration, evaluation framework, and deployment. We provide fixed-price proposals after a free architecture session.
A well-scoped GenAI MVP takes 6–8 weeks from kickoff to staging deployment. Production-hardened systems with MLOps, monitoring, and enterprise integration typically require 10–14 weeks. Complex multi-agent platforms or fine-tuned models may take 16–24 weeks. We don’t shortcut the evaluation and hardening phases – that’s what separates production systems from demos.
RAG is the right choice for most enterprise use cases – it’s updatable in real time, cites sources, and doesn’t require expensive training compute. Fine-tuning is warranted when: (1) the task requires highly specific output style or terminology, (2) base model accuracy is below 80% on your benchmark, or (3) inference cost reduction is a primary driver. We run a technical evaluation before recommending either approach.
Yes – private deployment is a standard option for all our GenAI engagements. We deploy on Azure OpenAI private endpoints, AWS Bedrock, Google Vertex AI private service connect, or self-hosted open-source models (Llama 3, Mistral) using vLLM or Ollama on your cloud or on-premise hardware. Full compliance documentation for SOC 2, HIPAA, and GDPR provided.
We implement multiple layers of hallucination control: (1) RAG grounding with source citation requirements, (2) RAGAS faithfulness evaluation in CI/CD, (3) confidence thresholds routing uncertain responses to human review, (4) output validation with Pydantic/Guardrails AI, and (5) content filtering for brand-unsafe outputs. We establish agreed hallucination rate targets in the contract.
All GenAI engagements include 3 months of post-launch monitoring: model drift detection, prompt regression testing, cost monitoring, and monthly performance reports. Extended 12-month support contracts are available. We proactively test your system against major LLM provider model updates and provide free compatibility reports. [FAQ Schema Markup: Add FAQPage schema to this section for Google rich results.]
Yes – when architected properly. The key is grounding (RAG or fine-tuning), output validation, and human-in-the-loop for high-stakes decisions. Our production systems achieve 95%+ accuracy on domain-specific tasks with proper evaluation frameworks. We won’t launch until your acceptance criteria are met.
🛡 Guarantee: If our GenAI system does not meet the agreed accuracy benchmarks in UAT, we will continue development at no additional cost until it does.
Hallucinations are a model characteristic, not a product defect – they’re manageable with the right architecture. RAG grounds responses in verified documents. Output validators reject uncertain responses. Confidence thresholds route low-confidence outputs to human review. Citation requirements force the model to reference source material.
🛡 Guarantee: We include RAGAS evaluation and hallucination rate benchmarking in every production deployment. If hallucination rate exceeds agreed thresholds, we rearchitect at no charge.
Yes – this is exactly why private LLM deployment exists. We deploy on Azure OpenAI private endpoint, AWS Bedrock, or self-hosted models with PII redaction middleware. No customer data reaches OpenAI’s shared infrastructure. Full GDPR, HIPAA, and SOC 2 alignment documentation provided.
🛡 Guarantee: We provide a data flow architecture diagram and compliance documentation before any model is connected to production data.
Cost control is designed in, not bolted on. Semantic caching (GPTCache) eliminates redundant API calls. Model routing (cheaper model for simple queries) reduces average cost per call. Prompt compression reduces token usage 30–50%. We provide cost projection models before launch and monthly cost optimisation reviews.
🛡 Guarantee: We provide monthly cost reports and optimisation recommendations. If costs exceed projections by >20%, we investigate and remediate at no additional charge.
We build provider-agnostic architectures wherever possible. Model abstraction layers allow switching between OpenAI, Anthropic, and open-source models with minimal code changes. We monitor provider changes and proactively test your system against new model versions.
🛡 Guarantee: We provide free compatibility testing whenever a major model version change affects your production system, for 12 months post-delivery.
