See what your agents can do

Design an optimal document chunking strategy for Retrieval-Augmented Generation systems, balancing chunk size, overlap, and semantic coherence.

Produces a comprehensive RAG chunking strategy with sizing, overlap, metadata, and evaluation guidelines.

Design observability systems for LLM applications with request tracing, quality monitoring, and debugging workflows.

Delivers an LLM observability architecture with distributed tracing, quality dashboards, and debugging tools for production LLM systems.

Design multi-model LLM architectures with intelligent routing, fallback chains, and cost-quality optimization across providers.

Delivers a multi-model architecture with routing logic, provider abstraction, and cost-quality optimization for LLM applications.

Design data preparation pipelines for LLM fine-tuning with quality filtering, formatting, and evaluation dataset creation.

Delivers a fine-tuning data pipeline with collection strategies, quality filtering, and train/eval split management for LLM customization.

Design LLM response caching systems with semantic similarity matching, cache invalidation, and cost optimization strategies.

Delivers an LLM caching architecture with exact and semantic matching, invalidation policies, and cost-latency tradeoff optimization.

Design comprehensive LLM evaluation frameworks with automated metrics, human evaluation, and regression testing for quality.

Delivers an LLM evaluation framework with metric selection, test dataset design, and continuous quality monitoring for LLM applications.

Design LLM chain and agent architectures with multi-step reasoning, tool use, and state management for complex workflows.

Delivers an LLM agent architecture with chain patterns, state management, and reliability controls for multi-step AI workflows.

Design robust LLM output parsing systems with structured extraction, validation, retry logic, and graceful degradation.

Delivers an LLM output parsing architecture with structured extraction, schema validation, and fallback strategies for reliable outputs.

Design LLM function calling architectures with tool definitions, execution safety, and error handling for agentic workflows.

Delivers an LLM function calling architecture with tool schemas, execution sandboxing, and reliability patterns for agentic systems.

Design context window management strategies for LLMs with efficient token usage, context compression, and retrieval augmentation.

Delivers a context management architecture with token budgeting, priority-based context selection, and conversation memory patterns.

Design systematic prompt engineering architectures with template management, version control, and evaluation frameworks.

Delivers a prompt architecture with template systems, versioning strategies, and evaluation pipelines for production LLM applications.

Design incident response plans for AI system failures covering detection, containment, communication, and post-incident review.

Delivers an AI incident response plan with severity classification, containment procedures, and post-incident review processes.

Design AI transparency and explainability systems with interpretable outputs, audit trails, and stakeholder explanations.

Delivers an AI explainability architecture with model interpretation methods, user-facing explanations, and regulatory documentation.

Design human-in-the-loop AI systems with appropriate automation levels, escalation paths, and human oversight for decisions.

Delivers a human-in-the-loop architecture with confidence-based routing, review interfaces, and feedback loops for safe AI.

Design a bias audit framework for AI systems covering detection, measurement, mitigation, and ongoing monitoring across demographics.

Delivers an AI bias audit framework with fairness metrics, demographic analysis, and mitigation strategies for equitable AI systems.

Design AI red teaming frameworks for systematically discovering vulnerabilities, biases, and failure modes in AI systems.

Delivers an AI red teaming framework with attack taxonomies, evaluation methodologies, and remediation tracking processes.

Design safety guardrail systems for AI applications with input validation, output filtering, and behavioral boundaries.

Delivers an AI guardrails architecture with input/output filtering, behavioral constraints, and escalation patterns for safe AI deployment.

Design model retraining strategies with trigger-based scheduling, data management, and validation for continuous model freshness.

Delivers a retraining strategy with trigger definitions, data window management, and automated validation for model freshness.

Design comprehensive production monitoring for ML systems covering model health, infrastructure, and business impact metrics.

Delivers a production ML monitoring architecture integrating model, infrastructure, and business metrics with unified alerting.

Design ML model governance covering model inventory, risk assessment, approval workflows, and regulatory compliance.

Delivers an ML governance framework with model risk tiers, approval processes, and compliance documentation requirements.