GenerativeAIConsulting

LLM Selection & Fine-Tuning

Model choice affects quality, latency, cost, data handling, and operating complexity. Our LLM consulting tests supported models against representative examples your team approves and the success criteria you define. We also verify current model features, Regions, terms, and pricing before making a recommendation.

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 gives a model relevant material from sources your team approves. It can help responses reflect current internal information instead of relying only on the model's training data.

We design and test ingestion, chunking, embeddings, search, permissions, citations, re-ranking, and response behavior. When a workflow needs several sources, we define how structured data, APIs, and documents are retrieved and how conflicts or missing evidence are handled.

• 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

Prompts, examples, tool instructions, and output schemas affect how a workflow behaves. We version and test them against representative cases so changes can be reviewed before release.

We build prompt templates, example libraries, output checks, and evaluation datasets around the task. Deterministic validation can enforce required formats and business rules; model and retrieval evaluations help identify unsupported or low-confidence responses for review.

• Systematic prompt design with version control and A/B testing
• Output validation and tests for unsupported responses
• Few-shot example curation and task-instruction design
• Guardrails for content policies, business rules, and required formats

Content & Workflow Pipelines

Generative AI becomes useful when it supports a real workflow. We design pipelines for document drafting, summarization, classification, extraction, and transformation, with the review and exception paths the work requires.

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

Model output can vary, prompts may contain sensitive data, and different use cases carry different review and recordkeeping needs. Those decisions should be part of the workflow design.

We help your policy, security, legal, and operations teams define model-use rules, data handling, output review, testing, audit evidence, and escalation paths. Where relevant, we map the design to frameworks and requirements your organization identifies, including the NIST AI Risk Management Framework and the EU AI Act.

• GenAI-specific usage policies and acceptable use frameworks
• Data privacy controls for prompt and response handling
• Output monitoring, audit trails, and explainability
• Control mapping for applicable regulations and frameworks
• 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 evaluate model routing, caching for repeatable requests with the same access rules, prompt optimization, and Amazon Bedrock capacity options. Each recommendation shows the tradeoffs across cost, quality, latency, and operations for your workload.

• 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, test, and operate systems powered by large language models and other generative AI technologies. This includes model selection, retrieval-augmented generation (RAG), prompt and workflow design, evaluation, governance, and cost management.

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) gives a model relevant material from sources your team approves. It can improve grounding and currency, but retrieval quality, permissions, citations, missing evidence, and conflicting sources still need to be tested. Tactical Edge designs those checks around your workflow and data.

How long does a generative AI consulting engagement typically take?

After discovery, Tactical Edge provides a phased plan based on your use case, data readiness, integrations, controls, testing, rollout needs, and team dependencies. Each phase has clear success criteria, so you can make an informed decision before expanding the rollout.

Does Tactical Edge use AWS for generative AI implementations?

Yes. As an AWS Advanced Tier Services Partner, Tactical Edge builds with services such as Amazon Bedrock, SageMaker AI, Amazon OpenSearch Service, and AWS Lambda when they fit the workload. We select the Region and architecture around your data, security, performance, scale, and cost requirements, then test the configuration before rollout.