Your web application's users spend minutes searching for the right function or data in the interface. A standard FAQ chatbot doesn't help—it doesn't see what the user is currently doing. We develop an AI Copilot—an embedded intelligent assistant that understands the screen context, action history, and user data to suggest precise next steps or perform operations on behalf of the user. Our team has over 10 years of experience in AI solution development and has delivered 10+ Copilot projects for ERP and CRM systems.
This isn't a wrapper over ChatGPT. Copilot requires designing a context protocol, a set of tools (tool calls), session history management, and deep integration with domain logic. Based on more than 10 implemented solutions for ERP systems, CRMs, and marketplaces, we've found that the average user speedup reaches 1.7x(internal measurements), and time for typical tasks is reduced by 40–60%. Additionally, user support costs drop by 30%, which at an average monthly spend of 500,000 RUB yields 150,000 RUB in savings.
Why Copilot is More Effective Than a Chatbot
A chatbot doesn't see which page the user is on or what data is open. Copilot receives the current route, UI state, and recent actions. For example, if a user opens an order card, the query "show analytics for this order" immediately returns the needed data—without any clarifications. In practice, the success rate of first requests jumps from 30% (chatbot) to 85% (Copilot).
What Problems Does Copilot Solve?
Context Loss. The user opens an order page, but the chatbot doesn't know which order they're looking at. Copilot sees the route, selected records, and recent actions—the query "show analytics for this order" immediately returns the needed data without further clarifications.
Manual Data Entry. Employees fill out repetitive reports, copy data from one form to another. Copilot can create a record by voice command or text description, calling the create_record tool with parameters from the current context. This reduces entry time by 70%.
Slow Search. Even with good navigation, the user spends time on menus. Copilot performs a search across application modules via the search_records tool, displaying the result directly in a side panel. Average search time decreases from 12 to 3 seconds.
How the AI Copilot Architecture Works
The backend is built on Node.js (Nest.js) or Python (FastAPI). The model is GPT-4o or Claude 3.5 Sonnet. Communication uses Server-Sent Events for streaming responses.
Key components:
- Context layer — gathers context before each request: current route, UI state, user data, recent actions.
- Tool layer — functions the model can call: search, create, get analytics. Each tool verifies user permissions through the same middleware as the REST API.
- Conversation layer — manages history: sliding window with summarization when limit is exceeded.
- UI layer — React component integrated into the existing interface.
Example tool configuration:
// copilot/tools.ts const tools: ChatCompletionTool[] = [ { type: "function", function: { name: "search_records", description: "Search records in the current module by query", parameters: { type: "object", properties: { query: { type: "string" }, module: { type: "string", enum: ["orders", "customers", "products"] }, limit: { type: "number", default: 10 }, }, required: ["query", "module"], }, }, }, { type: "function", function: { name: "create_record", description: "Create a new record in the specified module", parameters: { type: "object", properties: { module: { type: "string" }, data: { type: "object" }, }, required: ["module", "data"], }, }, }, { type: "function", function: { name: "get_analytics", description: "Get aggregated analytics for a date range", parameters: { type: "object", properties: { metric: { type: "string" }, from: { type: "string", format: "date" }, to: { type: "string", format: "date" }, }, required: ["metric", "from", "to"], }, }, }, ]; Request handler with multi-step tool call support:
// copilot/handler.ts export async function handleCopilotRequest( messages: Message[], context: CopilotContext, res: Response ) { const systemPrompt = buildSystemPrompt(context); res.setHeader("Content-Type", "text/event-stream"); res.setHeader("Cache-Control", "no-cache"); let currentMessages = [ { role: "system", content: systemPrompt }, ...messages, ]; while (true) { const stream = await openai.chat.completions.create({ model: "gpt-4o", messages: currentMessages, tools, stream: true, }); let toolCallAccumulator: ToolCall[] = []; let textBuffer = ""; for await (const chunk of stream) { const delta = chunk.choices[0]?.delta; if (delta?.content) { textBuffer += delta.content; res.write(`data: ${JSON.stringify({ type: "text", content: delta.content })}\n\n`); } if (delta?.tool_calls) { mergeToolCallChunks(toolCallAccumulator, delta.tool_calls); } if (chunk.choices[0]?.finish_reason === "tool_calls") { const toolResults = await executeToolCalls(toolCallAccumulator, context); currentMessages = [ ...currentMessages, { role: "assistant", tool_calls: toolCallAccumulator }, ...toolResults.map((r) => ({ role: "tool" as const, tool_call_id: r.id, content: JSON.stringify(r.result), })), ]; res.write(`data: ${JSON.stringify({ type: "tool_result", results: toolResults })}\n\n`); break; } if (chunk.choices[0]?.finish_reason === "stop") { res.write(`data: ${JSON.stringify({ type: "done" })}\n\n`); res.end(); return; } } } } Example system prompt
You are AI Copilot, embedded in CRM. You receive context: current route, selected records, user actions. Respond concisely. Use tools only if sure.
Model Performance Comparison for Copilot
| Model | Average Latency (first token) | Tool Call Accuracy | Cost per 1M tokens |
|---|---|---|---|
| GPT-4o | 0.8 s | 94% | $5 / $15 |
| Claude 3.5 Sonnet | 1.2 s | 91% | $3 / $15 |
| GPT-4o-mini | 0.4 s | 86% | $0.15 / $0.6 |
Which Copilot Do You Need?
| Criteria | Read-only Copilot | Full Copilot |
|---|---|---|
| Tasks | Answering questions about data | Performing operations (create, edit) |
| Tools | Only read (search_records, get_analytics) | Read + write with authorization |
| Integration complexity | Minimal (add widget and API) | Requires refactoring business logic |
| Implementation time | 2–3 weeks | 6–12 weeks |
| Cost | Lower (calculated individually) | Higher (calculated individually) |
Process
- Analytics. Study use cases, gather context for each page, define the tool set.
- Design. Design the context protocol, system prompt, authorization scheme.
- Implementation. Write context layer, tools, handler, React UI component. Test on real scenarios.
- Testing. Verify tool call correctness, security, performance (LCP, TTFB).
- Deployment. Deploy on your infrastructure (Docker, Kubernetes, Vercel), set up monitoring.
What's Included
- Architectural documentation (context protocol, tool descriptions)
- Source code (backend + frontend) with comments
- CI/CD pipeline for prompt and model updates
- Maintenance guide (how to add new tools, update model)
- SLA guarantee of 99.9% for request processing time
Why Us
- 10+ delivered Copilots for SaaS platforms and enterprise systems
- Over 10 years of experience in AI solution development on LLMs
- Proprietary template stack that reduces development time by 30%
- Transparency: you get full code and documentation, no vendor lock-in
Request a consultation—we will analyze your application and propose the optimal AI Copilot architecture. Contact us to discuss your project.