Industry reports this year show that 95% of GenAI pilots never make it to production, 88% stall before deployment, and 70%+ fail to get past proof-of-concept. 

Most teams get to 80% and then spend the next 12 months discovering all the reasons it can’t actually ship. The failures come from the infrastructure, not the inference:

• Data quality & preparation (the cause of failure cited in 85% of cases)

• Security & compliance at scale

• Observability & AI trace

• Edge cases that only show up in real usage

• Lack of guardrails or isolation

• Integration complexity

• Monitoring, governance, and lifecycle management

Production GenAI systems are still mission critical software products with attack surfaces, behaviour contracts, governance requirements, and users whose lives and businesses rely on receiving a correct output.

With the right foundations including defence-in-depth guardrails, real security, GraphRAG accuracy, domain context, observability, governance, and a platform that handles the complexity in one place, you can move to production with confidence.

This guide outlines the fundamentals you should evaluate when choosing (or building) a production-ready GenAI platform. Based on what we’ve learned putting GenAI into production with government, compliance, research, and high-risk industries, here’s what developers should look for.

1. Guardrails That Actually Work

Prompt filtering, regex or system prompts are not guardrails. You need a robust, multi-layered framework.

1. Edge Security (WAF, bot detection, rate-limiting)

2. Air-gapped Classification (intent analysis, jailbreak detection before main model)

3. Prompt Isolation (user input can never become system instructions)

4. Document Validation Pipeline (scan, sanitise, trace every document)

5. Output Validation & Containment (treat LLM output as untrusted)

Caitlyn.ai uses this architecture for mission-critical accuracy in regulated sectors. 

2. Auditable, Provable Security

To ensure a secure and compliant system, you need to be able to audit and prove compliance at every point. A production GenAI system needs robust foundations:

• Private cloud deployment (ideally inside your AWS account)

• RBAC & scoped access rules

• Audit-ready trace logs

• No data leaving your environment

• Versioned retrieval records

Caitlyn.ai comes secure out of the box,and constantly maintained by our team of experts who have already poured over 14,000 hours into development. 

3. Mission critical domain accuracy

If your use case involves domain advice, risk, money, compliance, health, or technical operations, you need more than generic RAG. And you need to build trust by showing the user where the answers have come from. Look for:

• GraphRAG for relationship-aware retrieval

• Industry ontologies (NALT, SNOMED CT, etc.)

• Deep-link citations

• Testing against ground truth

• Intelligent document extraction (tables, images, messy PDFs)

Caitlyn.ai has built-in accuracy guardrails ready for you to adjust as needed.

4. Observability & Debugging

For a production-ready tool, you need to be ready for audits, forensics, performance tuning, regression testing, explaining incidents, and preventing failures from recurring. Obviously, observability is critical to have visibility of: 

• What was retrieved

• Why it was selected

• The scoring trace

• The system prompts used

• What the model output before filtering

• Every transformation and validation applied

Caitlyn.ai has observability and debugging tools in-platform and ready to use. 

5. Integration & Lifecycle Engineering

This point is so often underestimated. Being the software solution that it is, GenAI projects still require the full suite of shipping considerations. 

• Staging environments

• Version control over prompts

• Content-aware regression testing

• RAG evaluation frameworks

• Adversarial prompt libraries

• FinOps + cost controls

• UI components for streaming, citations, uploads

• SSO, RBAC, provisioning

Caitlyn.ai is designed for teams that need to ship value, with mission critical accuracy, and deliver trustworthy knowledge to their clients. 

Ultimately, you need a platform that handles the complexity that comes with a GenAI solution, and ideally in one place. 

Caitlyn was designed to excel at the engineering most teams would take tens of thousands of hours to build or maintain:

• Runs inside your AWS account for full sovereignty

• Defence-in-depth security: classification, isolation, validation

• GraphRAG + ontologies for high-trust answers

• Intelligent ingestion pipelines that work with messy data

• Deep-link citations enforced by technical validation

• Observability across every layer (CloudTrail, S3 Tables, CloudWatch)

• Prebuilt UI components

• Audited by AWS (Generative AI Software Competency)

• Predictable scaling & cost controls

Caitlyn was purpose-built for developers, to remove months of engineering work and let you focus on your core product.

Watch a walkthrough of how it works below, or get started with a demo today.