Your best answers. Everyone’s advantage.
The answer exists somewhere. In a document, an old conversation, a system only one person knows. We build AI that brings the right context into the moment work needs to happen.
Sound familiar?
A customer asks a simple question. Three people and five tabs later, someone can finally answer it.
The cost isn’t just the time spent searching. It’s the interruption, the inconsistent answer, and the experienced person who becomes everyone’s unofficial search engine.
Where we come in
Build the change into the work.
We follow a real request with your team: what they need to know, which sources they trust, and which decisions require judgment. Then we build an application that retrieves relevant context, shows where it came from, and helps complete the next step. Agents can act within explicit permissions; sensitive actions stay behind a review.
One way the work could change
Feel the difference in the everyday.
An illustrative workflow, shaped around the people using it.
- 01Find the right document
- 02Ask the person who knows
- 03Copy an answer into another tool
- 01Retrieve approved context
- 02Review a grounded suggestion
- 03Take the next permitted action
People carry the context between each step.
A question shouldn’t become an afternoon.
An operations assistant can assemble an order’s history, explain why it is delayed, and draft a response. A person approves a refund or a customer promise. The agent handles the context gathering and the routine preparation.
Your team owns the important calls. We make sources, uncertainty, permissions and exceptions visible so useful automation doesn’t become a black box.
Start here.
Choose one recurring question with a clear answer source and a meaningful next action. We can test whether the system earns trust before expanding its responsibilities.
Let’s explore this together ↗︎Planning the work
A clearer first step.
For operations teams whose recurring questions require information from several systems. TWIMCO builds AI applications and agents around a defined responsibility, the evidence needed to do it, and the actions the system is permitted to take.
What can an engagement include?
- A workflow map with a named owner, permitted actions and escalation rules.
- Connections to the agreed documents, business systems and APIs, respecting access permissions.
- An evaluation set, review workflow and operating documentation for the people using the system.
The proposal defines the deliverables and acceptance criteria for your specific workflow.
What should we bring to the first conversation?
Bring a recurring question, representative examples, the source of the correct answer and the person accountable for the next action. Access to systems and permission to use the data are prerequisites for a realistic pilot.
How do we know it is working?
Compare answers against reviewed examples. Track unsupported answers, escalation rates, time to resolution and cost per completed task. Agree on acceptance criteria before expanding permissions.
What determines cost and timing?
The number of systems, access to usable data, exception complexity and review requirements shape the work. A bounded pilot should have a defined scope and acceptance criteria. Ask for a proposal that separates discovery, implementation, infrastructure and ongoing support.
Who owns and maintains the system?
Confirm code and data ownership, repository access, hosting accounts, third-party licenses and support responsibilities in the engagement agreement. Handover should identify who monitors failures, approves changes and maintains integrations.
When is another approach a better fit?
Use conventional software when the rules and inputs are predictable. Use an existing product when it meets the workflow without substantial integration. An agent is useful when interpreting changing context is part of the job.