AI Solutions
Copilots Inside Your Workflow
A copilot works alongside your users inside the tools they already use, suggesting and drafting while the person stays in charge of the final decision.
Overview
AI Copilot Development
A copilot differs from a standalone chatbot because it lives inside a workflow and understands the context around it: the record being edited, the document being reviewed, the ticket being answered. Codemotives designs copilots that use that context to propose a draft, find a relevant item or recommend a next step, with the user able to accept, edit or reject each suggestion.
The hardest part is usually interaction design, not the model. Suggestions must be quick, relevant and easy to dismiss, or people stop using them. We also build in controls on what the copilot may read and change, log accepted and rejected suggestions as a quality signal, and make clear that the output is a proposal that needs human judgment.
Context
Problems we solve
- 01
Suggestions nobody uses
Irrelevant or slow prompts become noise. Value comes from precise context and low-friction acceptance.
- 02
Over-trusting the draft
Users may accept polished but wrong output. Interfaces should encourage review, especially for important fields.
- 03
Context and permissions
A copilot should see only what the current user may see, and act only within their rights.
- 04
Measuring real benefit
Usage alone does not show value. We track acceptance, edits and task outcomes to judge it.
Capabilities
What we deliver
- 01
In-app assistant UX
Side panels, inline suggestions and keyboard shortcuts designed to fit the existing interface.
- 02
Context assembly
Gathering the relevant record, history and documents for each request, within the user's permissions.
- 03
Draft and summarize
Generation of replies, summaries and descriptions that users can edit before saving or sending.
- 04
Tool use with confirmation
Actions such as updating a field or creating a task, shown to the user for approval first.
- 05
Feedback capture
Recording acceptances, edits and rejections so quality can be reviewed and improved.
- 06
Admin controls
Settings for enabling features by team, limiting data access and reviewing usage.
Where it fits
- Drafting replies for support agents
- Summarizing long account histories for sales reps
- Suggesting fields while filling in forms
- Assisting analysts with query building
- Helping editors with outlines and rewrites
Technologies we use
- React
- TypeScript
- Node.js
- OpenAI API
- Anthropic API
- pgvector
- Server-Sent Events
- Python
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
What is the difference between a copilot and a chatbot?
A chatbot is usually a separate conversation. A copilot is embedded in the task, aware of the surrounding context and aimed at helping the user complete that task rather than answering general questions.
Can a copilot take actions on its own?
It can, but we generally recommend that anything with consequences needs explicit user confirmation. Low-risk actions can be automated later once you have seen how reliably it behaves.
How will we know it is helping?
We agree measures up front, such as time per task, edit rates on suggestions and user feedback. Adoption numbers alone can mislead, so we look at outcomes too.
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.