Retrieval-assisted workflow
Ground responses in governed source material with citations and access controls.
Applied, not performative
AI & Automation
Models and automation integrated into governed applications and real operating workflows.
AI / SYSTEM VIEW GOVERNED
EVALUATE / GUARD / REVIEW / OBSERVE
Typical problems
When the system needs deeper engineering.
- 01A manual knowledge workflow is slow or inconsistent
- 02Teams need search across complex internal information
- 03An AI prototype has no production architecture
- 04Automation must coordinate models with existing systems
What RKL delivers
A production-ready technical outcome.
- ↳ Use-case and risk assessment
- ↳ Model and provider integration
- ↳ Retrieval and search architecture
- ↳ Evaluation and quality controls
- ↳ Human review and escalation workflows
- ↳ Monitoring, cost and data-governance controls
Expected outcomes
- ✓Automation tied to a measurable workflow
- ✓Controlled use of sensitive business data
- ✓Observable quality and operating cost
- ✓A maintainable application rather than a disconnected demo
Delivery patterns
Architecture matched to the constraint.
Human-in-the-loop automation
Use confidence and business rules to decide when people review or approve.
Model gateway
Centralise provider access, policy, observability and cost controls.
Technical depth
Capabilities and technology
LLM integrationsAI-enabled applicationsIntelligent searchDocument processingData enrichmentWorkflow automation
PythonTypeScriptLLM APIsVector searchPostgreSQLCloud AI services
When to engage RKL
Bring us the difficult decision or delivery problem.
- The workflow and success criteria are understood
- A prototype must become a secure production capability
- AI must integrate with data and operational systems
Engagement options
AI architecture assessment
Production implementation
Workflow automation
Related capabilities
Data & Platform Engineering
Operational and analytical data foundations built for correctness, access and governance.
ExploreBackend Engineering
Reliable APIs, services and data-intensive platforms for demanding production environments.
ExploreSoftware Architecture
System, solution and enterprise architecture grounded in delivery and operational reality.
ExploreQuestions
Service FAQ.
Does every automation need AI?
No. Deterministic rules and conventional software are often simpler and more reliable. We use models only where they add material value.
How do you manage model quality?
We define evaluation sets, failure categories, confidence or review rules, telemetry and a process for monitoring behaviour after launch.
START A CONVERSATION / SYSTEM READY
Turn an AI capability into a governed production system.
Share the current system, constraints and target outcome. RKL will help define the right next technical step.