Start with a testable task
Know what a useful result looks like.
Searching internal documents and predicting an operational outcome require different data and evaluation methods. We examine the inputs, the decision the output supports and the consequences of getting it wrong. This helps establish which approach to explore, what evidence is needed and where a person should check the result before it is used.
A focused feasibility exercise can answer a specific technical question before the scope of a larger application is agreed.
AI and machine learning services
Choose the approach around the problem.
Available data, source access and review requirements shape the application and how its results are evaluated.
Talk through your requirementsAI application development
Build applications that assist with document processing, finding information and preparing draft content. Define the source material, required output and review steps within the surrounding business process.
- Document processing
- Search across selected knowledge
- Assisted content preparation
Chatbot & assistant development
Develop conversational interfaces using approved information and connected services. Set topic boundaries and access rules, with a way to request clarification or hand a conversation to a person.
- Conversational user interfaces
- Connections to knowledge sources
- Human support handover
Machine learning development
Prepare data for a prediction or classification task, develop candidate models and compare their results with a relevant baseline. Evaluation uses representative examples and measures suited to the business question.
- Data preparation
- Prediction and classification models
- Model comparison and evaluation
AI integration & evaluation
Connect the selected model to your application and check how the complete workflow behaves. Record useful feedback and review cases involving unfamiliar inputs, missing sources or outputs that require attention.
- Application and model integration
- Evaluation examples and criteria
- Monitoring and user feedback
Illustrative computer vision example
Detect objects, then review the evidence.
This construction-scene example illustrates object detection in an image workflow. A project would define the object categories and representative examples, then examine missed objects and incorrect detections before deciding how the output should be used.
- Define the objects and situations that matter.
- Review incorrect detections and missed objects.
- Connect the output to an agreed human review step.
Example AI application
Search company documents with source references.
An internal assistant could help staff find answers in a selected set of company documents. The application would retrieve material the user is permitted to access and prepare an answer with references. Evaluation would check whether those references support the answer and how the assistant behaves when documents are missing, outdated or contradictory.
- Apply document access rules before retrieving source material.
- Test useful answers alongside unanswerable questions.
- Provide a path to review the source or ask for human help.
- AskA staff member submits a question about their work.
- RetrieveThe application searches the permitted document sources.
- AnswerThe assistant prepares a response with source references.
- ReviewThe user checks the references or seeks further help.
Hypothetical assistant scenario; feasibility and output quality would need to be evaluated for the selected sources.
AI project delivery
Use evaluation to guide the next step.
A prototype is useful when it answers an agreed question about the task, the data or the proposed approach.
Assess the use case
Review sample inputs, source access and the expected output with your team. Define the evaluation examples, success criteria and conditions that would make the approach unsuitable.
What you leave withUse case scope and evaluation planDevelop and compare
Build a focused prototype or model and assess the results. Examine failure cases, compare candidate approaches and identify which limitations must be addressed before integration.
What you leave withPrototype and documented findingsIntegrate and review
Connect the selected approach to the application with agreed access controls and human review. Document monitoring, feedback collection and how future model or source changes will be checked.
What you leave withIntegrated workflow and review procedures
Do we need a custom machine learning model?
That depends on the task. An existing model, a search-based application or conventional software may cover the requirement. We consider data availability, evaluation results and operating constraints before recommending custom model development.
Can we start if our business data is incomplete?
We can begin by reviewing a representative sample. The findings may point to data preparation, improved source coverage or a narrower initial task. They also help distinguish what can be tested now from what needs additional information.
How do you manage inaccurate AI answers?
The design can include source references, human review, fallback behaviour and feedback collection. These controls are chosen around the task and tested with representative questions. They help manage errors; they do not make every output correct.
Your next step
Which task should your AI project address?
Bring example inputs, the output you need and the decision it would support. We can define an initial evaluation scope.