Services Process
Supporting a successful Highlighter Enterprise Perception System Deployment
To ensure a successful Highlighter implementation, Silverpond offers comprehensive services to develop agents and capabilities that use machine learning to automate your processes. These services are designed to:
* Define project scope, success criteria, and efficiency goals.
Working collaboratively with customers, we will establish clear goals for what defines a successful deployment of an agent into a workflow. This could be a prototype or a production-ready agent. We develop specific metrics tied to business outcomes, such as accuracy rates, processing speed, and resource efficiency so as to measure the outcomes of a successful deployment.
* Facilitate agent development and evaluation.
We use training and evaluation data for the development and testing of an agent. We ensure the datasets have an appropriate taxonomy and represent a balance of positive and negative examples. We also work in an iterative manner, incorporating customer feedback into iterative development cycles to refine model performance. Our evaluation process validates model accuracy, robustness, and alignment with business objectives.
* Establish iterative training and evaluation processes using business-as-usual (BAU) data.
Highlighter has the ability to seamlessly incorporate new data into the training of agents. This means enterprises do not have to separately invest in developing training datasets, but can leverage their existing processes for continual improvement.
* Enable seamless collaboration between human and AI agents.
We help design workflows that allow human experts to complement AI agents, especially in edge cases or ambiguous scenarios where human oversight is required. Highlighter provides the means for staff to validate, override, or augment AI-generated insights. We also help train staff to use the agents to maximise the value of the delivered agents.
Steps in Our Machine Learning Services
1. Pre-Engagement Meeting
Objective: Define initial scope and requirements of the engagement
Activities:
- Discuss data types and availability.
- Establish success criteria and business case.
- Evaluate data quality and initial labeling commitments.
2. First Workshop: “What Are We Doing and Why?”
Objective: Design workflows and prepare for development.
Activities:
- Review data, success criteria, and business case.
- Design training and evaluation datasets and organize labeling resources.
- Collaborate to gather examples of positive and negative cases.
- Implement taxonomy/schema in the workflow
3. Silverpond Planning
Objective: Develop an internal action plan.
Activities:
- Review workshop outputs.
- Create a roadmap for development.
- Align on taxonomy requirements and data expectations.
4. Second Workshop: “When Are We Doing It?”
Objective: Align on milestones and timelines.
Activities:
- Present the development roadmap.
- Discuss delivery schedules, key milestones, and labeling cadence.
5. Agent Development
Objective: Build and deploy the agent.
Activities:
- Execute data labeling, training, and evaluation.
- Integrate the agent into the workflow.
- Validate model performance against defined success metrics.
6. Showcase: “How Did We Go?”
Objective: Present results and enable customer adoption.
Activities:
- Share evaluation outcomes and next steps.
- Collaborate on the post-deployment roadmap for scaling the model.
- Train customer staff to use the agent in workflows.