Applied AI
01
Launching and Scaling Enterprise AI
The rapid growth of AI has and continues to generate significant risk and potential benefits for organizations. Our clients have been turning to us for help launching, evaluating and then scaling AI across their organizations. From opportunity discovery to model turning, our services ensure that their programs focus on the more valuable opportunities first and operate at an ever accelerating technology speed while still ensuring responsible use and governance.
Experience with the following tools, frameworks, and languages: ChatGPT, Claude, Gemini, Copilot, Notion, Github, AWS, GCP, Azure
02
MCP Optimization & Security
The Model Context Protocol has rapidly become the connective tissue of enterprise AI — linking models to tools, data, and systems at scale. As organizations expand their AI capabilities through MCP integrations, performance, reliability, and security must advance together. Our clients turn to us to ensure their MCP deployments are fast, resilient, and hardened against the unique vulnerabilities that come with giving AI systems access to business-critical infrastructure. From latency optimization and tool architecture reviews to permission modeling and governance frameworks, we help organizations accelerate their AI programs without compromising control.
Experience with the following tools, frameworks, and languages: Claude MCP, OpenAI tool use, LangChain, LlamaIndex, AWS IAM, Azure RBAC, OAuth 2.0, Python, TypeScript
03
Agentic Development
AI agents are rapidly moving from experimental prototypes to production systems that execute complex, multi-step workflows on behalf of your organization. Building agents that are reliable, efficient, and safe requires more than prompt engineering — it demands thoughtful architecture, robust tool integration, and rigorous testing across unpredictable edge cases. Our clients engage us to design and build agents tailored to their highest-value use cases, from internal process automation to customer-facing applications.
Experience with the following tools, frameworks, and languages: Claude, ChatGPT, Gemini, LangChain, LlamaIndex, AutoGen, AWS Bedrock, Azure AI, Google Vertex AI, Python, TypeScript
04
AI FinOps and Tokenomics
As AI moves from pilot to production, the cost of inference, token consumption, and model orchestration can scale faster than the value being delivered, eroding ROI and creating budget pressure that slows programs down. Organizations often lack the visibility and frameworks needed to understand where AI spend is going, which workloads justify premium models, and how to architect for efficiency without sacrificing capability. Our clients engage us to align infrastructure spend with business value by tracking LLM unit economics, optimizing GPU allocation, and auditing token-based billing.
Experience with the following tools, frameworks, and languages: AWS Cost Explorer, Azure Cost Management, Google Cloud Billing, LangSmith, LangFuse, Helicone, OpenMeter, Anthropic API, OpenAI API, Python
05
AI Data Management
The quality, structure, and governance of your data determines the ceiling of what your AI programs can achieve. As organizations scale their AI initiatives, data pipelines that were sufficient for traditional analytics quickly become bottlenecks — too slow, too siloed, or too poorly documented for models to use effectively. Our clients engage us to assess and redesign their data infrastructure with AI consumption in mind, from retrieval architecture and embedding strategies to data quality frameworks and access controls.
Experience with the following tools, frameworks, and languages: Snowflake, Databricks, dbt, Pinecone, Weaviate, pgvector, AWS S3, Azure Data Lake, Google BigQuery, Apache Spark, Python
