The Expert Co-Pilot

Empower Your Human Experts

This is where we move beyond the hype of isolated chatbots. We build your team an intelligent Co-Pilot—a specialized, tool-equipped assistant that doesn't just answer questions, but actively collaborates, investigates, and creates alongside your best people.
The Knowledge Reactor functions as the baseline, the common ground for the team. As if you’d be on-boarding this new tool. However, common sense or public data can replace it.

The hype around AI is real, yet powerful models often feel wasted, like revolutionary electrical engines that don’t fit into your existing chassis.
Simple chatbots accelerate tasks, but is this how transformation is supposed to feel? How do you bridge the gap between a model's potential and your team's real-world processes?

The answer lies in frictionless integration. For simple tasks it is your communication channel (Slack, Teams, Discord), but sometimes you will also need a transparent visual interface to keep control.

The Anatomy of a Hydra Co-Pilot

A Co-Pilot is not a single model; it is a carefully configured instance around the Hydra Generator, a model-agnostic agent with a dynamic context, a goal, and a toolkit.

  1. The Agent Generator: This component manages the model initialization, text generation, with multiple safeguards and strategies pre-implemented, and ensuring the final output is validated and reliable.
  2. The Context (PromptContextManager): This is what personalizes the Agent. The context dynamically assembles the calibrated context needed for any given task by combining different COMPLEXITIES for each:
    • Role: Defines its inclinations and perspective. Not just prompting but a guide, safe-guards, and a knowledge base.
    • ToolData: Grants it a set of capabilities. A developer might get the CodeExecutionTool andGitTool, while one for a researcher gets theWebSearchTool or FileStorageTool, and a visual artist might get ImageGenerationTool and3DModelTool. its
    • ThinkingStrategyData: Guides its reasoning process, ensuring it approaches problems with a structured, logical plan.
    • Playground Access: Connection to the Knowledge Reactor for the relevant sections of your digital twin. Connection to a working space for long-term collaboration.
  3. The Conversation: It can plan, use multiple tools, change strategies, adapt, fix errors. Restart, Retry, Give up and constantly communicate. All with secure and validated answers.

This modular assembly means you can create a fleet of highly specialized, low-overhead Co-Pilots for every function in your business.

From Assistant to Augmentation: Value in Action

When your experts are equipped with Hydra Co-Pilots, they are amplified.
Imagine custom tools integrated easily to your 3rd parties and preferred tools as well as your company wisdom (meaning procedures, policies, preferences, history, etc).

  • Featured Demo. The Perplexity-Like Research Assistant: Our demo showcases this perfectly. An expert provides a topic, and the Co-Pilot orchestrates a multi-agent strategy of web search, summarization, and report generation, delivering a synthesized brief with full source transparency. It's the ideal example of augmenting a human's research capabilities.
  • The Financial Co-Pilot: Your CFO can now ask, "What were the key drivers of customer churn last quarter, and how does that correlate with support ticket volume?" The Co-Pilot uses its tools to query the financial database and the Zendesk API
  • The R&D Super-Assistant: An engineer is stuck on a material science problem. They ask their Co-Pilot: "Find recent academic papers and patents related to graphene-based polymer composites for thermal dissipation. Cross-reference with our internal experimental data to see if we've explored this." The Co-Pilot performs the web search, accesses the internal FileStorage for lab results, and delivers a complete briefing.
  • The Legal Advisor: An in-house counsel needs to assess risk. They ask, "A client has proposed a new feature that involves user data sharing. Scan our privacy policies, the latest GDPR updates, and any similar past projects for potential compliance conflicts." The Co-Pilot digests the documents and provides a bulleted list of potential issues, complete with citations.

Just to show how simple it is, here is an actual code example:

1from hydra
2
3model_settings = ModelConfig(
4    provider="ANTHROPIC",
5    model_name="claude-3-opus-20240229",
6    temperature=0.0,
7    max_tokens=4090
8)
9
10copilot_config = GeneratorConfig(
11    name="Developer_CoPilot_001",
12    model_config=model_settings,
13    timeout_seconds=120,    
14    retry_attempts=3,      
15    tool_val_attempts=5
16)
17
18developer_copilot = Generator(config=copilot_config)
19
20developer_copilot.add_role(DeveloperRole())
21
22developer_copilot.add_tool(CodeExecutionTool())
23developer_copilot.add_tool(FileStorageTool())
24developer_copilot.add_tool(WebSearchTool())
25
26print("✅ Co-Pilot is ready!")
27
28task_brief = """
29You must write a code script that extracts all the important keywords from 
30the top 100 articles about AI agents. Save them to a file and make a plot 
31about their evolution through time. Build a docs report.
32"""
33
34# Step 5: Run the Co-Pilot and get the validated result
35result, validation = await developer_copilot.generate(task_brief)
36
37if validation.is_valid:
38    print("✅ Task Complete. Final Output:")
39    print(result)
40
The Knowledge ReactorThe Dynamic Workflow