Intelligence with control
AI agents and workflow automation
Connect rules, software and language models into supervised workflows that remove repetitive work without hiding operational risk.
REFERENCE ARCHITECTURE01 / SYSTEM
01Trigger and deterministic routing
02Model or extraction step only where interpretation is needed
03Policy, permissions and confidence checks
04Human approval gate for sensitive actions
05Logging, evaluation and cost monitoring
Who it is for
When the current system gets in the way.
Typical teams
- Operations teams repeatedly copying, classifying or reconciling information
- Businesses that need answers grounded in private documents
- Teams exploring agents but requiring permissions and human review
Operational symptoms
- Staff repeat the same multi-step process across different systems
- Important documents are difficult to search or interpret consistently
- Automation attempts fail when inputs are unstructured
- AI experiments lack logging, evaluation or cost limits
Build scope
What Tervyn can build.
The exact boundary comes from diagnosis. These are common system components, not a fixed package.
Human-supervised AI agents
↗RAG and cited knowledge assistants
↗Document extraction and classification
↗Research, triage and tool-using agents
↗Example workflow
A complete path, with ownership at every handoff.
We define the data, interface, services and exception paths together so the workflow remains observable and maintainable.
- 01Trigger and deterministic routingInput and responsibility defined
- 02Model or extraction step only where interpretation is neededValidated handoff to the next layer
- 03Policy, permissions and confidence checksValidated handoff to the next layer
- 04Human approval gate for sensitive actionsValidated handoff to the next layer
- 05Logging, evaluation and cost monitoringResult recorded and observable
Delivery
Built through explicit decisions.
Every stage narrows uncertainty before the next investment is made.
Diagnose
Workflow map and problem definition
Define
Scope, architecture and success criteria
Design
Interface system and tested interaction model
Engineer
Production implementation, integrations and QA
Operate
Deployment, monitoring, support and iteration
Security & ownership
Control should survive the launch.
- ✓Least-privilege access to tools and data
- ✓Model and vendor selection around privacy constraints
- ✓Approval, retry and safe-failure paths
- ✓Auditable prompts, actions and outputs where appropriate
Buyer questions
Before we start.
01What is the difference between automation and an AI agent?
Deterministic automation follows known rules and should be preferred when the inputs and decisions are predictable. AI is useful when work requires interpreting language, documents or ambiguous context.
02Can an agent update our systems?
Yes, with scoped credentials, explicit tools and approval gates suited to the consequence of each action. Read-only or draft-first modes are often the right starting point.
03How do you control AI cost and quality?
We route tasks to appropriate models, set budgets and limits, log outcomes, create evaluation sets, and measure failure modes before expanding autonomy.
Start with the constraint
What is slowing the business down?
Tell us where work is still being copied, chased, repeated or lost. We will help determine whether the answer is better software, automation, AI or a simpler process.