Knowledge assistants
Help employees find answers in approved company documents, with source references and access that respects their permissions.
Practical applications
Start with the work.
Then choose the AI.
Workflow automation
Extract information, classify requests, prepare drafts, and route tasks between the tools your team already uses.
AI agents
Build assistants that can plan and carry out a defined sequence of tasks, with limits and approval steps for consequential actions.
Example use cases to explore together. For delivered work, see our AI support agent, AI withdrawal reviewer and multi-provider AI platform case studies.
From idea to a working pilot
- Identify repetitive work and assess whether AI is a good fit.
- Review data availability, permissions, tool integrations, and operating costs.
- Build a scoped pilot with agreed success criteria and human checkpoints.
- Evaluate output quality, exceptions, and reliability before expanding.
- Document the workflow and help your team operate and improve it.
Useful automation needs boundaries.
We scope what an agent can read and do, where a person needs to approve an action, and how errors are surfaced. Access control, evaluation, and operational ownership are part of the design.
Questions before we start
Do we need to replace our existing systems?
Not necessarily. The first step is to understand your current tools and their integration options. A pilot can focus on one workflow rather than a broad system replacement.
Is every automation an AI agent?
No. A predictable, rules-based workflow may be the better choice. AI is useful when the work involves interpreting language or handling varied inputs; an agent adds tool use and a sequence of actions within defined limits.
Will the agent act without a person?
That depends on the workflow. We define approval steps and escalation paths with your team. Actions such as sending external messages or changing important records can require human review.
What should we bring to the first conversation?
A description of a repetitive process, the tools involved, and what a useful result would look like. Do not send passwords, private datasets, or credentials through the inquiry form.
Connected expertise
A dependable foundation for AI.
AI workflows need well-managed infrastructure, integrations, and operational ownership. Explore the cloud and modernization services that support that foundation.
Explore modernization & securityProjects
Related work we have delivered.
AI agents
An AI support agent that only answers from an approved FAQ.
A support assistant for an online gaming operator that answers players from an approved FAQ, hands everything else to a person, and has no way to touch accounts, data, or money.
Claude
AWS
Amazon EC2
TelegramAI agents
An AI reviewer that checks every withdrawal but never moves money.
An advisory AI layer for an online gaming operator’s withdrawal queue. It reviews every request against account history and explains its verdict, while a deterministic engine keeps sole control of payouts.
ClaudeAWSAmazon EC2
Amazon AuroraAI platform
An AI platform that never depends on one model provider.
A consumer generative-AI product where an in-house router picks the job, a LiteLLM gateway picks the model with automatic fallbacks across providers (three in total), and hybrid RAG grounds answers in cited sources.
LiteLLM
Amazon BedrockClaude
PostgreSQL + pgvector