01
Workflow and opportunity assessment
Map the process, current effort, users, source systems, and exceptions. Identify which steps suit rules, AI assistance, or human judgment, then define the pilot scope.
Applied AI · Controlled business workflows
AI workflow automation
Based in Greece, BI Solutions Group designs and implements AI-assisted workflows for international teams working remotely. Work directly with founder Ioannis Bekas to define useful document, knowledge, and operational automation, connect existing systems, and keep review and ownership clear.
When to bring us in
Start with a problem your team recognizes.
- A team repeatedly reads, classifies, extracts, summarizes, or routes similar documents and requests.
- An AI prototype needs integrations, evaluation, permissions, and a clear path into everyday operation.
- Work passes between several tools and people, with unclear exceptions or repeated manual re-entry.
Deliverables
What the engagement can include.
Select the work that addresses your requirement. The agreed scope defines which deliverables are included.
02
Document and knowledge workflows
Scope intake, extraction, classification, summarization, or retrieval over approved knowledge. Define expected outputs, source traceability, and review requirements.
03
Integration and human review
Connect agreed systems through available APIs or workflow tools. Implement permissions, validation, approval queues, failure handling, and an auditable handoff where required.
04
Evaluation and operating guidance
Test representative cases and important failure modes. Document acceptance criteria, monitoring, ownership, recurring usage costs, and the steps for operating or updating the workflow.
How we work
From the first question to a usable handoff.
- 01
Map one useful workflow
Describe the trigger, inputs, expected output, users, and current handling. Establish the business baseline and the decisions requiring human approval.
- 02
Prototype with representative cases
Use approved material to test feasibility and identify difficult cases. Agree measurable acceptance criteria before expanding the implementation.
- 03
Integrate and evaluate
Connect the agreed tools and permissions. Test output quality, exceptions, retries, access boundaries, latency, and cost against the defined requirements.
- 04
Pilot and establish ownership
Review the workflow with its users, document operation and escalation, and agree monitoring and change responsibilities before broader use.
Scope & support
Make the dependencies clear before the build.
What to prepare
- A process owner who can explain current steps, difficult cases, and the acceptable level of error.
- Representative, approved examples plus the rules for using confidential or personal data.
- A list of existing tools, available APIs, access constraints, and the people who can approve changes.
A short description is enough for the initial enquiry. Share sensitive files only after access and data-handling arrangements are agreed.
What determines scope and fees
- Variation, volume, language, and quality of incoming material
- Number of integrations and the capabilities of their APIs
- Evaluation, permissions, review, and audit requirements
- Hosting, model usage, monitoring, and operational support
After the handoff
AI workflows need review when models, prompts, documents, or connected systems change. Support can include failed-run investigation, evaluation updates, quality and usage-cost monitoring, and integration maintenance. Responsibilities and support expectations are agreed separately.
Questions before we start
The practical details.
What is a suitable first AI automation project?
A narrow, repeated process with accessible inputs, a named owner, and a result that can be checked. Document classification, internal summaries, or request triage can be candidates, but suitability depends on your examples, risks, and review requirements.
Does every step need an AI model?
No. Fixed rules, templates, validation, and conventional integrations often handle predictable steps better. The workflow design identifies where AI interpretation is useful and where deterministic behavior or human judgment is required.
Can the workflow use our existing tools?
Often, yes. Feasibility depends on available APIs, permissions, licensing, and your environment. The assessment identifies which systems can connect, where a manual handoff remains useful, and whether additional infrastructure is needed.
How do you decide whether an AI output is good enough?
We agree acceptance criteria using representative cases. Depending on the workflow, evaluation can cover field accuracy, missing information, source support, routing decisions, error handling, latency, cost, and the need for human correction.
How is sensitive business data handled?
Data handling, access, model providers, retention requirements, and review controls are agreed before implementation. Use the initial enquiry to describe the workflow and its constraints; do not include confidential documents or personal data.
How much does AI workflow automation cost?
Implementation fees depend on process complexity, integrations, data preparation, evaluation, and controls. Model usage, hosting, platform subscriptions, and ongoing support can add recurring costs. These dependencies are reviewed when agreeing scope and fees.
Further reading
Useful context for the next conversation.
AI document workflows
A practical introduction for professional-service teams.
From a prompt to a repeatable process
Inputs, outputs, review points, and operating rules.
Monitor a workflow after launch
Quality, cost, latency, and changes over time.
Start with your requirement
Tell us what needs to work better.
Describe the process, current tools, and the result your team needs. We review the context and agree the next step with you.