Define the decision
Start from the task, the user, the acceptable error, and the point where a person should stay in control.
- Workflow mapping
- Input and output contract
- Human review points
- Evaluation criteria
AI for repetitive work.
I integrate AI into document processing, research, routing, and drafting tasks.
The work includes data access, tool integration, evaluation, and human review or fallback paths.
Code review in GitLab CI/CD
I integrated LLM-assisted code review into GitLab CI/CD using OpenAI and Anthropic models.
- Workflow
- Merge-request code review
- Integration
- GitLab CI/CD
- Models
- OpenAI · Anthropic
- System concern
- Context · output · review
Problems I help solve
Document intake and routing
Extract, classify, validate, and send incoming documents or messages to the right next step.
Support triage and drafting
Prepare grounded responses, summaries, and actions while keeping human review where it matters.
Internal knowledge workflows
Help teams find and apply information spread across tools, documents, and product data.
Research and engineering automation
Connect models to repeatable research, review, enrichment, or development workflows.
What the work covers
Connect the system
Connect models to the data, APIs, and tools the task requires.
- Model and provider integration
- Document and data retrieval
- APIs, webhooks, and queues
- Permissions and audit paths
Make it operable
Track failures and cost, evaluate real examples, and adjust the system as requirements change.
- Evaluation sets
- Fallback behaviour
- Usage and failure monitoring
- Iteration and model changes
How the work starts
Define the task
We review real examples, available data, and the cost of errors. We agree on the first version and how to evaluate it.
Result: Workflow scope and evaluation criteria
Connect it to your tools
I connect the model, application logic, tools, and review steps in a working production workflow.
Result: An integrated AI workflow with review steps
Review results and refine
I review output quality, failures, cost, and response time, then adjust prompts, retrieval, models, and controls within the agreed scope.
Result: Improvements based on production results
Is this a fit?
A good fit
- The manual task is repeated often enough to justify a system.
- Real examples and a clear success criterion are available.
- The workflow can tolerate a designed review or fallback path.
Outside this service
- There is no specific task for the system to perform.
- The task has no owner, examples, or way to evaluate the output.
- The system must make an unreviewed high-stakes decision with no safe fallback.
Questions before we start
Do we need an AI agent?
Often, no. Extraction, retrieval, classification, or drafting may need a simpler workflow. I assess the task before choosing the architecture.
Can this connect to our existing tools?
Yes, when those tools provide suitable APIs, webhooks, data access, or controlled browser workflows. Integration feasibility and permissions are checked during assessment.
How do we know whether it works?
The project defines representative examples and observable acceptance criteria before production. Evaluation can include output quality, failure classes, latency, cost, and required human correction.
Can we change model providers later?
The system can be designed to keep provider-specific code behind a clear boundary. Portability still depends on the capabilities and behaviour the workflow requires.
Show me the task you want to automate.
Tell me who does it, what goes in, what comes out, and where time or quality is lost.