Document extraction
Turn authorized documents into structured fields that a person can verify before use.
AI-assisted workflows
Use AI where it improves a real workflow.
Apply AI to a bounded business task with defined inputs, review points, privacy requirements, fallback behavior, and measurable acceptance checks.
What this can include
AI is most useful when it helps a person complete a specific job—not when it is added as a vague feature. We design AI-assisted workflows around the actual source material, decision risk, expected output, and the person responsible for review.
Classification and routing
Suggest categories, priorities, or destinations while preserving review for uncertain or sensitive cases.
Drafting assistance
Create first drafts from approved context and templates without pretending the output is final.
Knowledge retrieval
Help teams find relevant material from an authorized, scoped source set with traceable references where possible.
Exception summaries
Organize long records or operational events so reviewers can focus on items that need attention.
Workflow agents
Coordinate limited tool actions behind explicit permissions, validation, logs, and approval gates.
Details
Begin with the decision risk
The acceptable workflow depends on what happens if the output is wrong. A low-risk draft can tolerate different controls than a customer commitment, financial change, legal decision, or update to a production system.
Every AI workflow should define
- The allowed sources and types of input.
- The output format and required supporting context.
- Confidence or validation rules that trigger review.
- The person authorized to approve a material action.
- Fallback behavior when the model or provider is unavailable.
- Retention, privacy, cost, and provider constraints.
Details
Verification before automation
- Representative tests
- Use real-world examples that reflect normal cases, ambiguity, missing fields, and adversarial or malformed input.
- Grounding
- Constrain outputs to approved business context and preserve links or citations when the task requires them.
- Structured output
- Validate machine-used fields against an explicit schema instead of trusting free-form text.
- Approval gates
- Require human confirmation before external messages, financial actions, deletion, credential changes, or other material effects.
Details
Provider and data boundaries
Model capability, cost, latency, data handling, retention options, regional availability, and contractual terms can change. We identify provider dependencies and document the assumptions used for the scoped implementation.
Not every process should use AI. Deterministic business rules remain preferable when the inputs and decisions can be expressed reliably without probabilistic output.
Start with the problem
Define a useful first result.
Tell us what happens today, where the work gets stuck, and what should be easier. We can identify the questions and dependencies needed for a responsible scope.