Problems we address
- The AI use case lacks a business outcome
- Source ownership and access are undefined
- Unreviewed output enters an important workflow
- Model and API cost is not measured
Back to technology solutions
AI systems and automation
AI Integration and Business Process Automation
We decide where deterministic automation, source-grounded AI and human approval belong instead of applying AI to every problem.
Business needWorking system
01Design
02Development
03Integration
04Measurement
Who it is for
Teams reducing repetitive document, knowledge, content or operational work while controlling data access and output quality.
Target business outcome
A measurable pilot or production scope with sources, controls, evaluation and usage cost made explicit.
Scope and deliverables
- Process and source assessment
- AI versus deterministic automation decision
- Access and human-approval gates
- Working limited pilot
- Evaluation set, logging and failure flow
- Production recommendation and cost model
Scope options
Components that change with the project
Knowledge AI discovery
Document and email processing
Catalogue and content operations
CRM or ERP workflow automation
Use cases
Examples that start with a concrete need
Grounded knowledge assistant
Answers from authorised sources, cites them and declines inaccessible questions.
Document processing
Classifies and extracts fields, routing low-confidence results for review.
Repeatable operations
Automates rule-based steps and leaves uncertain decisions to people.
Working approach
A process that connects the need to launch and measurement
- 01Need and goal discovery
- 02Scope and acceptance criteria
- 03Design and development
- 04Integration and testing
- 05Launch, handover and training
- 06Maintenance and continuous development
Factors affecting budget and timeline
- Source volume and quality
- Integration and access
- Evaluation and review level
- Model, API and tool cost
Information needed to start
- Process examples and error cost
- Permitted sample data
- Users and access roles
- Success metric and approval points
Post-launch support: A prototype is not a production system. Private/on-prem, enterprise SLA, unlimited data or error-free output are not promised without evidence and separate architecture.
Frequently asked questions
Does every automation need AI?
No. Clear rules and structured inputs often favour simpler automation.
Will company documents be sent to a model?
Sensitive data is not used until provider, retention, access and contract boundaries are defined.
How accurate is AI?
There is no blanket guarantee; quality is measured with representative tests, citations and review rates.
Is a pilot production ready?
No. Security, monitoring, failure handling and support must be completed first.
Guides that support the buying decision
An AI assistant grounded in company documentsAutomation or AI for a business process?Quality and usage cost in AI projects
Related technology services
Project enquiry
Describe the need and we will shape the right scope.
A short brief is enough. We clarify the suitable solution, integrations and next step during the discussion.