AI Modernization Services

Model drift in production with no monitoring

Models perform well at deployment, degrade over 6-12 months as data distributions shift, and nobody notices until business metrics decline. Many production models run without active performance monitoring, so the degradation stays invisible until it becomes a business problem.

Prevention: Monitoring and alerting for model performance metrics, not just system metrics, must be in scope from Day 1. A vendor who delivers a model without a monitoring dashboard has not delivered a production-ready system.

Hallucination exposure in customer-facing use cases

LLMs deployed without grounding, retrieval augmentation, or output validation expose companies to factual errors at scale. Illustrative pattern: a customer-facing LLM that answers product questions without grounding can quote incorrect rates or terms, and without an evaluation pipeline the error surfaces through customer complaints rather than internal testing.

Prevention: RAG architecture or fine-tuning for factual use cases; automated evaluation pipelines for output quality; human-in-the-loop review for high-stakes decisions. No customer-facing LLM should go live without a documented evaluation framework.

MLOps gaps leaving models unmanaged post-deployment

The consulting firm builds the model, hands over a Jupyter notebook, and leaves. No CI/CD pipeline for model updates, no feature store, no experiment tracking. The internal team cannot retrain or redeploy without re-engaging the vendor, creating permanent dependency at ongoing cost.

Prevention: MLOps platform setup is a mandatory deliverable, not optional. The engagement must conclude with the internal team demonstrating the ability to retrain and redeploy independently. Require a knowledge transfer sign-off as a go-live gate.

"We'll build a custom LLM" for a classification task

massive over-engineering when fine-tuned open-source models solve the problem at far lower cost. Custom LLM proposals for standard tasks indicate the vendor is selling scope, not solving problems.

No monitoring or observability plan for the deployed model

a model without monitoring is not a production system. If the proposal ends at deployment, it ends before the hard part starts.

AI-washing

traditional automation (rule-based, scripted logic) rebranded as "AI" without actual ML components. Ask to see the model architecture; if the answer is a decision tree or a regex, it is not AI.

No evaluation framework

if the vendor cannot describe how they measure model accuracy, bias, and drift with specific metrics and tooling, they have no way to know whether the model is working.

Single model solution without fallback strategy

production AI requires ensemble approaches or fallback logic for cases where the model is uncertain. Single-model, no-fallback architectures fail silently in production.

How much does AI modernization cost?

AI implementations range from $150K for a single use case on an established platform to $3M+ for enterprise MLOps platform build and multi-model production deployment. LLM integration projects (RAG, fine-tuning) typically run $200K–$600K. The highest cost variable is data quality remediation. Teams that skip data assessment often discover that a large share of the budget goes to data work, not model development.

Build vs buy vs API — how do we decide on AI infrastructure?

API-first (OpenAI, Anthropic, Google Gemini) is fastest to value for standard tasks and is billed per token. Fine-tuned open-source models (Llama, Mistral) cost more upfront ($50K-200K) but eliminate per-query costs at scale and offer data privacy. Custom model training is only justified for truly proprietary data or regulatory requirements that prohibit third-party APIs.

Open source vs commercial models — which is better?

Commercial APIs (GPT-4, Claude, Gemini) outperform on general tasks and require minimal setup. Open source (Llama 3, Mixtral, Qwen) offers lower long-term cost, data privacy, and fine-tuning control. At sustained high volume, self-hosted open source can become cost-competitive. The choice is usually: commercial API for proof-of-concept, open source for production at scale.

What is RAG and when do we need it?

RAG (Retrieval-Augmented Generation) grounds LLM responses in your specific data, preventing hallucinations by fetching relevant context before generation. You need RAG when the LLM needs to answer questions about your internal documents, policies, or product data; accuracy is critical; or the knowledge base changes frequently enough that fine-tuning is impractical. RAG is the standard architecture for enterprise LLM deployment.

How do we manage regulatory risk with AI?

Regulatory risk in AI falls into three categories: data privacy (GDPR, CCPA: don't send PII to third-party APIs without a DPA), AI-specific regulation (EU AI Act: requires risk classification for certain use cases), and sector-specific rules (FINRA for financial advice AI, FDA for medical device AI). Build a risk assessment into Phase 1; high-risk use cases need legal review before development begins.

What ROI should we expect from AI modernization?

ROI varies widely by use case and data readiness, and no single benchmark applies across organisations. Customer-service deflection, document processing, and predictive maintenance are the use cases that most often produce measurable returns, but the size of the gain depends on baseline volumes, labour cost, and data quality. Treat vendor-quoted percentages as claims to test in a pilot. AI projects without pre-agreed ROI metrics often fail to demonstrate value. Define success metrics before the first model is built.