Process and access map
We map intake, assignment, review, approval, and notification steps alongside user roles and permissions. This makes the start, end, and owner of the automated workflow explicit.
How AI-powered systems can be used
1. Assistants using company data (RAG)
We define data, access, and verification needs for finding information in authorized company sources and generating answers.
2. AI agents and workflow automation
We design task steps, tool connections, and decisions that need human approval around the workflow.
3. AI features in existing software
We plan the interface and API connections for AI features added to an existing web, mobile, or business application.
4. Model infrastructure and operations
We define model access, deployment, monitoring, and maintenance responsibilities for the project and contract.
Where can AI-powered systems help?
An AI-powered system may help people find information, prepare decisions, or carry out permitted steps in existing software. These are possible uses, not completed customer projects. The approach depends on available data and the current workflow.
Find answers across company knowledge
Imagine contracts and procedures spread across several repositories. A RAG assistant could search approved sources and show passages behind an answer. The team must know which documents are current, who can see them, and what happens when no reliable source is found. Uploading files alone is not enough.
Support repetitive work with clear controls
An emailed request might need classification, a linked record, and approval. An AI agent could suggest a next step or use approved tools. A person should review actions that change records, send external messages, or affect money; the system should log them. Some steps work better with ordinary rules.
Add AI to an existing product
A web application might draft text or summarize a record. Its interface, API, identity checks, and user corrections matter. The team also needs a plan for model access, versions, costs, and errors. AI can analyze cloud or CI/CD work without changing production on its own.
How do we plan an AI project?
These steps turn a broad idea into work the team can evaluate. Tools, timeline, and deliverables depend on the project.
Describe the job and a useful result
We look at today's work, needed output, unacceptable mistakes, and simpler alternatives. Finding information, drafting, classifying, and taking action are different jobs. This shows whether AI helps and where a person makes the final call.
Check data and connections
We examine source ownership, access, freshness, and available APIs. The customer and project team agree how data may be sent, stored, and deleted. Missing access or unreliable sources can limit the work.
Test the work, including failures
A trial can use representative questions, expected answers, and failure cases. We check sources, incomplete replies, and tool limits. The evaluation should reflect the real job, not a few impressive demonstrations.
Decide who runs it afterward
We discuss the interface, integrations, model access, and monitoring. The project may end at handover or include maintenance or ongoing operation. Someone must own later changes to models and sources.
Keeping the system useful and controlled
AI output can be wrong. An assistant should flag missing sources; a system that takes action needs permissions, approvals, and intervention points. User feedback reveals where stronger checks are needed. Perfect accuracy or a particular business result cannot be assumed.
No model provider, hosting, retention, license, or security arrangement is selected by default. Existing systems and customer agreements shape those choices. One contained use can expose data and integration limits before expansion.
Sources, rules, and models change after launch. Error review, feedback, and updates need owners. Maintenance or ongoing operation is included only when agreed; no support period or service level is promised here.
Custom automation for your business
Before automating recurring work, we map who starts it, which data it uses, which decisions need human approval, and what happens when a step fails. We design data flows between CRM, ERP, document systems, and existing software within the project scope.
Where AI adds value, we assess functions such as document or request classification, summarization, retrieval from company information, and decision support. We avoid adding AI where fixed rules suffice; the method depends on the data, verification, and operating needs.
Data flow between systems
We define source systems, transfer rules, API connections, and error or retry behavior. The technical capabilities of existing systems determine the scope.
AI-assisted steps
We choose classification, summarization, and information retrieval steps based on actual data and verification needs. We define where a person reviews generated output.
Control and operations
We define human approval for critical decisions, activity logs, monitoring, and manual intervention points. Development, maintenance, and ongoing operations are agreed in the contract.
Questions about AI-powered systems
How is RAG different from a general chatbot?
RAG searches approved sources before replying and uses what it finds to support the answer. The quality of those sources, access rights, and human review still matter.
Can an AI agent act on its own in our systems?
The team chooses which tools it can use, what it may do, and where a person must approve an action. Logs and a way to intervene are part of that design; unlimited autonomy is not assumed.
Can you add AI without replacing our application?
Possibly. It depends on the application's APIs, identity setup, data, and the feature you need. A separate service may work, or the interface and data flow may also need changes.
Which model will use our company data?
We do not choose a provider by default. The type of data, access rights, storage and deletion rules, and contract terms inform that decision before implementation.
Who looks after the system after handover?
That depends on whether the project includes handover only, optional maintenance, or ongoing operation. We agree on who monitors and updates the system; handover does not imply indefinite support.