GenerativeAIConsulting

LLM Selection & Fine-Tuning

Choosing the wrong model is one of the most expensive mistakes in generative AI. Our LLM consulting helps you evaluate models systematically against your actual workloads - not synthetic benchmarks. We test foundation models from Anthropic, Meta, Cohere, Mistral, and others through Amazon Bedrock, comparing accuracy, latency, cost per token, and compliance characteristics for your specific use cases.

When a foundation model needs domain adaptation, we design and execute fine-tuning strategies using SageMaker. This includes training data curation, evaluation pipeline setup, and A/B testing frameworks to validate that fine-tuned models actually outperform base models on your metrics.

• Multi-model benchmarking against production workloads via Amazon Bedrock
• Cost-performance tradeoff analysis across model families
• Fine-tuning strategy design and execution on SageMaker
• Model versioning, evaluation pipelines, and regression testing

RAG Architecture & Knowledge Systems

Retrieval-augmented generation is how enterprise generative AI systems stay accurate and grounded. Rather than relying solely on a model's training data, RAG connects your LLM to your proprietary knowledge bases, documents, databases, and APIs - producing responses that reflect your organization's actual information.

Our generative AI consulting team designs RAG architectures that balance retrieval accuracy, latency, and cost. We handle document ingestion pipelines, chunking strategies, embedding model selection, vector store design, hybrid search configurations, and re-ranking layers. For organizations with complex knowledge landscapes, we build multi-source RAG systems that pull from structured and unstructured data simultaneously.

• Document ingestion, chunking, and embedding pipeline design
• Vector store architecture using Amazon OpenSearch or PostgreSQL pgvector
• Hybrid search with semantic and keyword retrieval
• Multi-source RAG with structured data, APIs, and document repositories
• Retrieval evaluation and accuracy measurement frameworks

Prompt Engineering & Output Quality

Prompt engineering is the difference between a generative AI system that produces occasional useful output and one that delivers consistent, reliable results. Our approach treats prompts as production code - versioned, tested, and systematically optimized rather than ad-hoc and fragile.

Tactical Edge builds prompt management systems that include structured prompt templates, few-shot example libraries, output validation layers, and automated quality scoring. We design guardrails that catch hallucinations, enforce formatting requirements, and flag low-confidence outputs before they reach end users.

• Systematic prompt design with version control and A/B testing
• Output validation, hallucination detection, and confidence scoring
• Few-shot example curation and chain-of-thought optimization
• Guardrail implementation for content safety and format compliance

Content & Workflow Pipelines

Enterprise generative AI creates value when it is embedded into business workflows - not isolated in a chatbot. Our generative AI consulting services include designing and building content pipelines that automate document generation, summarization, classification, extraction, and transformation at scale.

We build pipelines that connect LLMs to your existing systems - CRMs, document management platforms, data warehouses, and communication tools. Each pipeline includes quality checkpoints, human review stages where appropriate, and monitoring to track output quality over time.

• Automated document generation, summarization, and classification
• Data extraction and transformation pipelines with LLM processing
• Integration with existing enterprise systems and workflows
• Human-in-the-loop review stages and quality monitoring

AI Governance for Generative AI

Generative AI introduces governance challenges that traditional AI systems do not. Output unpredictability, intellectual property concerns, data privacy in prompts, and rapidly evolving regulations all require a governance framework designed specifically for GenAI workloads.

Our generative AI consulting includes building governance frameworks that cover model usage policies, data handling protocols, output monitoring, bias detection, and regulatory compliance. We help organizations navigate the EU AI Act, NIST AI Risk Management Framework, and industry-specific requirements as they apply to generative AI deployments.

• GenAI-specific usage policies and acceptable use frameworks
• Data privacy controls for prompt and response handling
• Output monitoring, audit trails, and explainability
• Regulatory compliance mapping (EU AI Act, NIST AI RMF, SOC 2)
• Intellectual property and copyright risk management

Cost Optimization & Performance

Generative AI costs can scale rapidly if not managed deliberately. Token costs, inference latency, embedding computation, and vector storage all compound as usage grows. Our generative AI consulting includes designing cost-efficient architectures from the start - not retrofitting cost controls after bills spike.

We implement strategies like model routing (using smaller, cheaper models for simpler tasks and reserving larger models for complex ones), caching layers for repeated queries, prompt optimization to reduce token usage, and provisioned throughput on Amazon Bedrock for predictable pricing. Every architecture decision includes a cost-performance tradeoff analysis.

• Multi-model routing to match task complexity with model cost
• Semantic caching and response deduplication
• Token usage optimization through prompt compression
• Provisioned throughput and reserved capacity planning on AWS
• Cost dashboards and usage alerting with automated scaling policies

What is generative AI consulting?

Generative AI consulting helps organizations plan, build, and deploy production systems powered by large language models and other generative AI technologies. This includes LLM selection, retrieval-augmented generation (RAG) architecture, prompt engineering, content pipeline design, governance frameworks, and cost optimization. Tactical Edge provides generative AI consulting focused on enterprise-grade systems that operate reliably at scale.

How do you select the right LLM for an enterprise use case?

LLM selection depends on the specific use case, performance requirements, cost constraints, and data sensitivity. Tactical Edge evaluates models across dimensions including accuracy, latency, token costs, context window size, and compliance characteristics. We benchmark candidates against your actual workloads using AWS Bedrock and SageMaker, then recommend the model or combination of models that best fits your requirements.

What is RAG and why does it matter for enterprise generative AI?

Retrieval-augmented generation (RAG) connects a large language model to your proprietary data sources so it can generate accurate, contextual responses grounded in your organization's knowledge. RAG is critical for enterprise generative AI because it reduces hallucinations, keeps responses current without retraining, and allows you to leverage existing data assets. Tactical Edge designs RAG architectures optimized for accuracy, latency, and security.

How long does a generative AI consulting engagement typically take?

Timeline varies by scope. A focused proof-of-concept for a single generative AI use case typically takes 4 to 8 weeks. A full enterprise generative AI implementation - including RAG architecture, governance framework, and production deployment - generally runs 3 to 6 months. Tactical Edge structures engagements to deliver measurable value at each phase rather than delaying results until the end.

Does Tactical Edge use AWS for generative AI implementations?

Yes. As an AWS Advanced Tier Services Partner, Tactical Edge builds generative AI systems on AWS infrastructure including Amazon Bedrock for managed LLM access, SageMaker for custom model training and deployment, and supporting services like OpenSearch for vector search and Lambda for serverless inference. Our AWS expertise ensures clients get production-grade infrastructure with proper security, scalability, and cost controls.