AI Integration &
Optimization
Enhanced Intelligence When You Need It
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We enhance workflows with engineered AI agent teams, with the unique capability to integrate Machine Learning when additional precision is required.
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Optimise Core AI Agent Capabilities
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The joy of agents is they can improve themselves, but this needs to be engineered. Agents are like people, they need the right instruction and then to learn from evaluations. This permits working at scale. Unlike traditional "human-in-the-loop" approaches that bottleneck at scale, our AI agents use automated evaluation frameworks to maintain quality across thousands of tasks.
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Build teams of AI "digital interns" that collaborate on complex tasks
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Train agents through structured evaluation, like developing top performers
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Implement quality systems that catch errors before they happen
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Enable agents to critique and improve each other's work
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Scale agent operations without sacrificing reliability
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Empower your people as AI team managers, not just users
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Teams of agents can work across departmental boundaries while respecting data permissions and security protocols - enabling enterprise-wide automation without compromising control.
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Advanced ML Integration Available
When your use case demands additional precision or specialised capabilities, we can enhance your AI agents with custom data science and Machine Learning (ML) components:
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Pattern recognition and trend analysis
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Anomaly detection and predictive analytics
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Document processing
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Specialised data processing pipelines
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Custom ML models provide unique capabilities that can't easily be replicated by competitors. They enhance AI agents' ability to handle complex data formats and unstructured information, while enabling precise computation alongside natural conversation.
This combination delivers both the flexibility of AI and the reliability of traditional software.
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Like having both strategic advisors and technical specialists on your team, our ability to combine AI agents with Machine Learning provides additional capabilities when your business needs demand it.
The Data Science Process
Our approach to data science is based on the Microsoft Team Data Science Process.
You can review the Microsoft TDSP in more detail here.
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Business Understanding
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Identify and articulate the business problem
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Set clear, measurable objectives for what the project aims to achieve.
Machine Learning
Case Study
Context
Agentico was asked to optimise parts inventory for a machinery business with 25,000 lines in stock over 4 sites.
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Stock demand is seasonal and uneven across sites. Demand for many components is not market driven but set by warranty alerts. However, a third of supply must be ordered months in advance in order to achieve discounts.
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Approach
We mapped business processes and interviewed stakeholders, inside and outside the business; warehouse managers, suppliers and finance providers. Data was sourced and algorithms tuned to the historical demand.
Approach Cont...
A prototype was trialled with users. The final model was then deployed with user training for each site and maintained on request.
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Solution
An ensemble model was developed, comprising a 1D convolutional neural network for time series forecasting and a traditional ARIMA model.
Logic was then constructed to accommodate warranty rules and distribution of stock across sites, given costs of picking and transit.
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Data pipelines were built as simply as possible to enable the inevitable maintenance and change of incentives from supplies.
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Impact
The model saved 10% in required stocking levels and reduced parts obsolescence, paying for itself in the first year of usage.
