Offerings we provide
1. Foundational Training and Experimentation Environments
To support internal adoption and continuous experimentation (essential for prototyping), we offer structural support.
• Platform Access and Guided Exercises: Vendors enable access to basic foundation models within existing workhub platforms. They provide access to cloud-based AI platforms, enabling employees to experiment with building AI agents or custom GPTs.
• Formal Training and Certifications: Offering access to advanced certifications and structured learning on foundational GenAI topics, such as ethical considerations and Large Language Models (LLMs).
• Community and Collaboration Structures: Supporting the establishment of a GenAI community of practice (CoP) and mentorship programs to foster peer-to-peer sharing of use cases and continuous collaboration.
• Skill Assessment Tools: Providing skill assessments (binary or variable) to certify critical skills required for mitigating GenAI risks and identifying advanced skills for maximizing productivity. This helps organizations design targeted learning pathways.
2. Data Labeling and Annotation
Since high-quality labeled data is crucial but often expensive and time-consuming to produce, we provide augmented solutions:
• AI-Augmented Annotation: Tools utilize emerging techniques like active learning and transfer learning to automate feature extraction and classification. This reduces the manual overhead and improves label quality in a shorter time.
• Programmatic Labeling: Platforms (like Snorkel Flow) enable organizations to encode Subject Matter Expert (SME) domain knowledge as code (labeling functions) to label data consistently at scale.
• Human-in-the-Loop (HITL) Validation: Most modern tools integrate HITL processes, allowing data scientists and domain experts to validate AI-generated labels before consumption, ensuring greater accuracy.
• Broad Format Support: Annotation tools support various use cases, including computer vision (images, video) and NLP (text, chat, documents), offering features like semantic segmentation and entity annotation.
To enhance model prototyping, commercial solutions reduce the resource-intensive work of feature engineering:
• Automated Feature Generation: AFE platforms (such as FeatureByte and Tecton) leverage AI to automatically generate and select features using a use-case-agnostic framework. These platforms can suggest new features and rank them by usefulness.
• GenAI Augmentation for FE: Services provide AI assistants within the platform that can map use cases to feature engineering methods, enhancing accuracy and reducing the time needed for model iteration.
• Feature Management Platforms: Tools offer essential management capabilities like feature versioning, lifecycle management, feature pipelining (for batch and streaming data), and orchestration.
Since the success of AI solutions relies on high-quality, verified data, we provide platforms and tools to accelerate data preparation, which is traditionally the most time-consuming phase of machine learning model implementation.
• GenAI-Assisted Data Preparation: Commercial tools offer AI-augmentation in the form of intelligent inference of transformations, automatic pipeline generation, and intelligent code completions. These low-code/no-code interfaces help less technical users generate AI-ready data, lowering the barrier to entry for data transformation. Examples of such platforms include Prophecy and Modak Nabu.
• Unstructured Data Processing: Specialized solutions handle the ingestion and preprocessing of complex, unstructured data (PDFs, text files, HTML, audio, video) required by GenAI. These tools offer capabilities like text extraction, cleaning, and chunking.
• Vector Embeddings and Knowledge Bases: Platforms help generate embeddings (converting text to vector strings) and facilitate integration with vector databases like Pinecone and Weaviate. Services also include the development of knowledge bases essential for Retrieval-Augmented Generation (RAG) systems.
• Data Lineage and Quality Management: Tools enable the visualization of data pipelines to maintain data lineage and provide augmented quality checks that automatically scan for missing or erroneous values.
For implementing agentic AI specifically, we offer the foundational architectural components required for development, deployment, and governance of multi-agent systems:
• Core Agent Components and Runtime: we provide solutions for managing the internal functions of AI agents, including the Agent Orchestrator and the Agent Runtime. This runtime often includes functionality for hosting enforcement and plugin configuration.
• Memory Management: Services dedicated to managing Memory (both short-term and long-term) for the agents.
• Prompt Management: Tools for handling Prompt Management to optimize agent interactions.
• LLM Services and Model Serving: Providing access to the necessary LLM Services and Model Serving infrastructure.
• Tool Use Integration: Services to provide the mechanism for agents to interface with external resources and APIs, often through a Tool Execution Sandbox and Tools client.
• Evaluation and Governance: Critical for experimentation, we offer services for Agent Evaluation using methods such as Deterministic Evaluation, Human-in-the-loop (HITL) oversight, and LLM Judges. Furthermore, they provide the necessary Agent Policy Engine and Guardrails for governance.
• Security and Identity: we facilitate the required Agentic AI IAM (Identity and Access Management) Infrastructure.
• Observability: Providing Telemetry & Observability tools to monitor the performance and transaction flow of the agents.