Structured evaluation of your proposed generative AI use cases against four dimensions: value (which specific business metric improves, by how much, over what timeframe?), feasibility (does your data quality, volume, and access support the use case?), risk (what are the failure modes -- hallucination consequences, bias exposure, regulatory liability -- and how severe are they?), and effort (build complexity, data preparation, evaluation infrastructure, and ongoing maintenance cost). Most assessments reveal 1-2 use cases with clear ROI and low risk worth building in the next quarter, 2-3 use cases worth building once data or model quality improves, and several that don't survive honest feasibility analysis. We've assessed use cases across finance, healthcare, logistics, and professional services -- the patterns of which AI use cases actually pay off in production vs. which sound compelling in demos are consistent enough to predict with confidence.

Comparative evaluation of frontier models (GPT-4o, GPT-4o mini, Claude Opus/Sonnet/Haiku, Gemini 1.5 Pro/Flash) and open-source models (Llama 3.3, Mistral, Qwen 2.5) against your specific use case requirements: output quality on a sample of your actual inputs, latency at your expected request volume, and total cost per query including input and output tokens at your usage pattern. Token cost modelling at your projected monthly volume reveals that the difference between GPT-4o and GPT-4o mini is often $2,000-20,000/month at production scale. Hosted API vs. self-hosted open-source analysis covers inference infrastructure cost (GPU instances on AWS or GCP), operational overhead, and data privacy implications of routing data through a third-party API. The recommendation includes the primary model, the fallback model for availability events, and the conditions under which a cheaper or faster model is acceptable for specific query categories.

Latency budget analysis breaks the total p95 response time into model inference (typically 500ms-4s depending on model and token count), retrieval (50-200ms for a well-indexed vector search), and application overhead, so you know before building which parts of the pipeline need optimisation. Prompt caching evaluation quantifies the cost savings available on high-repetition system prompts -- Claude's prompt caching reduces input token cost by 90% on cache hits for prompts over 1024 tokens. Model routing strategies (sending classification queries to GPT-4o mini and complex reasoning to GPT-4o) are costed and tested against your actual query distribution rather than assumed to work uniformly.

Architecture design for retrieval-augmented generation systems before you build them -- because the document processing pipeline, chunking strategy, embedding model selection, vector database choice, and retrieval logic are all decisions that are expensive to change once data is ingested and production queries are running. We design the full RAG architecture: document ingestion and preprocessing (PDF parsing, HTML cleaning, table extraction), chunking strategy (fixed-size vs. semantic vs. structural), embedding model selection (OpenAI ada-002 vs. Cohere vs. open-source BGE), vector database (Pinecone for managed simplicity, Weaviate for multi-modal, pgvector for Postgres-first stacks), retrieval strategy (pure semantic vs. hybrid BM25 + vector vs. re-ranked), and context window assembly. We also design the evaluation framework for retrieval quality -- separate from generation quality -- because most enterprise RAG systems fail because they retrieve the wrong context, not because the model generates a poor response given good context.

Architecture design for AI agent systems that need to plan, use tools, and execute multi-step tasks autonomously: which tools to expose and how to define them to minimize hallucinated tool inputs, memory architecture (short-term in-context vs. long-term vector memory vs. structured key-value stores), state management via LangGraph or a custom state machine, and orchestration patterns for multi-agent systems where specialist agents handle subproblems. Failure mode analysis is the most valuable part: agents that loop (keep retrying a failing tool call), agents that hallucinate tool arguments (call an API with an invalid parameter), agents that produce unsafe actions (send an email or modify a database when not authorized), and agents that exceed context limits and lose coherence. Each failure mode requires a specific guardrail designed before deployment. The consulting output is an architecture document your engineering team can build from, not a general recommendation to "use LangChain."

Structured assessment of an AI system already in development against the requirements it needs to meet in production. We evaluate: the evaluation framework (does a test dataset exist? does it cover the edge cases that will occur in production?), latency and cost benchmarks at realistic traffic volumes (not single-request benchmarks), hallucination handling and output validation logic, error state handling and graceful degradation when the model API is unavailable or returns malformed output, monitoring and logging completeness (can you debug a production failure in under 30 minutes?), and data privacy compliance (is sensitive data being sent to a third-party API that wasn't scoped in your data processing agreements?). The output is a prioritised fix list with severity ratings -- not a general critique of the architecture, but a specific set of issues to address before the launch date, ordered by the probability and consequence of each failing in production.

Design of the repeatable processes that let you update AI systems -- new model versions, prompt changes, RAG pipeline improvements -- without releasing quality regressions. Golden test sets built from your actual production examples, covering the distribution of query types, edge cases, and high-stakes scenarios specific to your domain. Automated evaluation pipelines that run the test set against every code or prompt change in CI/CD, catching regressions before they reach users. LLM-as-judge evaluation for subjective quality criteria that don't have a ground truth answer. Human review sampling processes for the cases automated evaluation can't reliably score. Governance policies covering which AI outputs require human review before action, which data categories can be sent to which model providers, and how user feedback is captured and routed into the improvement cycle. The governance infrastructure that lets your team ship AI improvements with confidence rather than dread.

RAGAS (Retrieval-Augmented Generation Assessment) provides structured metrics for RAG pipelines: faithfulness (does the answer contain only claims supported by the retrieved context?), answer relevancy, context precision, and context recall -- each producing a numeric score that can gate a CI/CD deployment. For generative tasks without a ground truth answer, LLM-as-judge prompts score outputs on defined rubrics (accuracy, tone, completeness, safety) and produce scores that correlate with human evaluators at 80-90% agreement when the rubric is well-designed. Output guardrails (Guardrails AI, custom prompt validators) intercept responses before delivery to enforce content policies, PII redaction, and output schema compliance. Fine-tuning evaluation -- when LoRA or QLoRA fine-tuning is used to adapt a base model -- requires a separate held-out evaluation set that was not seen during supervised fine-tuning, scored against the base model on the same benchmark to confirm the fine-tuned model improves the target task without degrading general instruction-following.