AI Development & Integration
AI Development and Integration for Practical Business Workflows
Support for adding AI capabilities where they genuinely help — document processing, internal knowledge search, assistants and automation — built with attention to privacy, reliability and cost.
Challenges we can help with
When this kind of support is useful
Typical situations where AI Development & Integration expertise makes a difference.
- Staff spending hours searching documents and internal knowledge
- Repetitive document handling that could be partly automated
- An AI prototype that works in a demo but is unreliable with real data
- Uncertainty about how to add AI features without exposing sensitive data
Technical capabilities
What we can support
- 01
LLM-powered features
Integrating model APIs into existing applications and products.
- 02
Knowledge search (RAG)
Retrieval-augmented generation over internal documents using embeddings and vector search.
- 03
Document processing
Extracting, classifying and summarising information from documents.
- 04
Workflow automation
Using AI for well-defined steps, with human review where it matters.
- 05
Evaluation and guardrails
Testing output quality and adding checks for reliability.
Example use cases
Requirements we commonly see
- 1An internal assistant that answers questions from company policy documents
- 2Extracting key fields from invoices or contracts for review
- 3Adding an AI summarisation feature to an existing web application
- 4Improving the answer quality of an existing RAG prototype
Representative examples, not descriptions of specific client projects.
Tools and technologies
Examples of technologies professionals in our network work with.
- LLM APIs
- Python
- Embeddings
- Vector databases
- Retrieval-augmented generation
- REST APIs
Availability for a specific tool or version is confirmed for each requirement. Product names are used only to describe skills and do not imply any partnership with their owners.
Considerations we discuss up front
- Privacy and security: what data may be sent to which model provider.
- Human review: where AI output must be checked before it is acted on.
- Reliability: how answer quality will be tested and monitored.
- Cost: expected usage and how model choice affects running costs.
How Corpolance may help
From requirement to the right specialist
- 1We clarify the workflow, the data involved and what a useful result looks like.
- 2We propose a developer with practical experience building and integrating AI features.
- 3You discuss the design, risks and evaluation approach with them before proceeding.
FAQ
AI Development & Integration questions
More answers are available on our FAQ page.
Often, yes. Many requirements involve integrating model APIs or retrieval into an application you already run.
Related services
Often needed alongside
- Data Science & Machine LearningPractical modelling support, from exploratory analysis to evaluated ML workflows.
- Software DevelopmentDevelopers who can step into your codebase and move features, fixes and integrations forward.
- Cloud ServicesDeploy, run and optimise workloads across AWS, Azure and Google Cloud.
Tell us about your AI Development & Integration requirement.
Share what you are working on and the technologies involved. We'll discuss the requirement and explain the next steps before anything is agreed.