Enterprise General Intelligence
Accurate function-specific tool calling for enterprise AI
The future of AI is function-specific tool calling. This is a fundamentally hard problem.
Enterprise functions require precise, reliable tool invocation. Revenue operations need accurate CRM API calls with correct parameters. Product development needs precise deployment system commands with proper configurations. Each function demands tool calling that understands domain-specific context, workflows, and constraints. This requires deep specialization that general-purpose systems cannot provide.
Choosing the right tool for the function requires deep domain knowledge. A revenue function needs different tools than a product function.
Calling tools with correct parameters demands understanding of function-specific context and constraints.
Function-specific context must be maintained across tool calls. Revenue context differs from product context.
Recovering from tool-calling errors requires function-specific knowledge of what went wrong and how to fix it.
Empower enterprises to run in a generally intelligent way through accurate function-specific tool calling.
Running in a generally intelligent way means autonomous operations that continuously improve, function-specific intelligence that adapts, and systems that get smarter over time. It is enabled by neuro-symbolic architectures that combine neural understanding with symbolic execution.
Enterprise functions operate autonomously with persistent goal pursuit, multi-step execution, and error recovery.
Intelligence selection is continuous. New models are evaluated. Performance improves. Systems adapt.
Each enterprise function runs on intelligence optimized for its specific requirements and workflows.
Agents learn from execution. Performance data accumulates. Intelligence selection evolves. Outcomes improve.
Enterprises can run on general intelligence when the right intelligence is selected for each function.
Function-specific tool calling is inherently difficult.
General-purpose AI systems are optimized for broad capability, not function-specific precision. Enterprise functions require tool calling that understands domain-specific context, workflows, and constraints. This demands specialization that general systems cannot provide.
Each enterprise function has unique workflows, constraints, and best practices. Tool calling must understand these domain-specific requirements. Revenue operations need different tool-calling patterns than product development. This requires specialization that general-purpose systems cannot achieve.
Enterprise functions require deterministic, auditable tool calling. Tool selection must be correct. Parameters must be precise. Error handling must be function-specific. General-purpose systems are probabilistic and cannot guarantee the precision required for enterprise operations.
Different functions require different tool-calling strategies. Revenue functions prioritize goal persistence and outcome quality. Product functions prioritize multi-step execution and context retention. A general system cannot optimize for both simultaneously.
Enterprise functions evolve. Tool-calling requirements change. New tools are introduced. Workflows are refined. Function-specific tool calling must adapt continuously. This requires ongoing evaluation and optimization that general systems cannot provide.
We continuously evaluate hundreds of frontier and open models against enterprise-function-specific agentic benchmarks.
Evaluation is continuous. New models enter the pipeline. Performance is measured on function-specific criteria. The best-performing intelligence is selected for deployment.
Neuro-symbolic architecture enables accurate function-specific tool calling.
We have refined neuro-symbolic execution architecture to solve function-specific tool calling. Neural layers understand intent and context. Symbolic layers generate precise tool calls with correct parameters. The combination enables reliable, accurate, enterprise-grade tool invocation optimized for each function.
Neural layer: Understands natural language requests and function-specific context. Determines which tools are needed and what parameters are required. Handles ambiguity and reasoning about tool selection.
Symbolic layer: Generates precise tool calls with correct parameters. Executes tool calls deterministically. Provides full auditability of tool invocation. Handles errors with function-specific recovery strategies.
Function-specific optimization: The architecture is optimized for each enterprise function. Revenue functions use revenue-specific tool-calling patterns. Product functions use product-specific patterns. Each function gets tool calling optimized for its requirements.
Neural layer: Large language models process natural language requests, extract intent, maintain context across extended interactions, and handle ambiguity through reasoning.
Symbolic layer: Code generation (CodeGen) translates neural understanding into precise Python code. Code is executed in isolated environments with full auditability. Execution is deterministic and traceable.
Integration: Neural understanding flows to symbolic execution. Symbolic execution results inform neural context. The architecture invokes APIs in real-time, eliminating data warehousing and reducing data residency concerns.
Understands natural language, extracts intent, and maintains context across interactions.
Handles ambiguity, reasoning, and complex understanding.
Translates understanding into precise code and structured execution.
Ensures accuracy, auditability, and deterministic outcomes.
Our neuro-symbolic execution architecture is refined for key enterprise workflows: revenue operations, product development, and operational automation. The architecture ensures reliable execution, full auditability, and compliance with enterprise requirements.
We continuously evaluate hundreds of models against function-specific benchmarks. The best-performing intelligence is selected and deployed on neuro-symbolic architectures. Your enterprise functions run on intelligence optimized for their requirements.
We evaluate frontier models (GPT-4, Claude, Gemini) and open models (Llama, Mistral, Qwen) as they become available. Evaluation includes both API-accessible and open-source models.
New models enter evaluation within days of release. Evaluation infrastructure scales to assess hundreds of models continuously.
Function-specific benchmarks simulate real enterprise workflows. Revenue benchmarks test goal persistence across long-running sales cycles. Product benchmarks test multi-step technical execution.
Benchmarks are validated against production agent performance. They measure capabilities that matter for autonomous operation.
Selection is based on weighted performance across seven dimensions. Different functions weight dimensions differently. Revenue prioritizes goal persistence and outcome quality.
Selection decisions are data-driven. Performance thresholds must be met. Statistical significance is required for deployment changes.
Production agents are monitored for performance drift. When a new model outperforms the current selection, deployment is updated. The system adapts to the evolving model landscape.
Performance data accumulates over time. Selection decisions improve as more data becomes available.
Flagship agents enable intelligent operations across enterprise functions. Each agent runs on intelligence selected for its specific requirements.
Autonomous go-to-market system for enterprise sales. Powered by neuro-symbolic execution architecture with accurate function-specific tool calling for revenue operations.
Autonomous product development agent. Powered by neuro-symbolic execution architecture with accurate function-specific tool calling for product operations.
Built for regulated, enterprise environments.
All customer data is fully isolated with logical and physical separation. Data is never used for model training.
Every action is fully auditable via real-time audit logs for complete compliance visibility.
Built by engineers with deep experience in autonomous systems, enterprise software, and AI.
Cofounder & CEO
VP Engineering at MainStreet. CEO at Scalable, drove revenue to $500M. Cofounded Simppler (ML talent platform). Applied ML across industries transacting billions.
Cofounder & CTO
Led autonomous systems at Bosch Germany. Built software platform for Ocean Freight Exchange. National Youth Prize in Science & Technology (2012).
Autonomous driving • Robotics • Enterprise software
Founding Engineer & ML Lead
Founding engineer at Crossian, architected platform that scaled to $300M revenue. Built autonomous warehouse systems deployed across multiple Asian countries.
Machine learning • Enterprise systems • Autonomous systems
Our approach is grounded in continuous evaluation, function-specific benchmarking, and neuro-symbolic execution.
Our evaluation methodology is based on agentic capabilities research and function-specific performance requirements. We continuously refine our benchmarks based on production agent performance and emerging research in agentic AI.
We evaluate models on agentic capabilities that matter for enterprise functions. Revenue operations require different capabilities than product development. Our benchmarks reflect these differences.
Evaluation dimensions are weighted differently for each function. Selection is based on function-specific performance, not general capability.
Our architecture combines neural understanding with symbolic execution. Neural layers handle natural language and reasoning. Symbolic layers generate and execute precise code.
This combination enables reliable execution, full auditability, and deterministic outcomes required for enterprise environments.
We evaluate models continuously as they become available. Performance data accumulates. Selection decisions improve over time. The system adapts to the evolving model landscape.
Our benchmarks simulate real enterprise workflows. They measure capabilities that matter for autonomous operation in specific functions. Generic benchmarks cannot capture function-specific requirements.
We combine neural understanding with symbolic execution. This enables reliable, auditable, deterministic outcomes. Pure neural approaches struggle with reliability and auditability in enterprise contexts.
Selection decisions are based on performance data, not assumptions about which model is "best." Different functions require different models. The best model for one function may not be the best for another.
The intelligence selection layer operates as a continuous evaluation and deployment system.
Automated evaluation pipeline that assesses hundreds of models against function-specific benchmarks. New models enter evaluation within days of release.
Data-driven selection based on weighted performance across seven dimensions. Selection decisions require statistical significance and meet performance thresholds.
Selected intelligence deploys to production agents via neuro-symbolic architecture. Deployment is automated, monitored, and can be rolled back if performance degrades.
New models are evaluated against function-specific benchmarks. Performance is measured across seven dimensions. Results are stored in the evaluation database.
The selection engine compares new model performance to current selections. If a new model outperforms the current selection for a function, it is selected for deployment.
Selected intelligence is deployed to production agents via neuro-symbolic architecture. Neural layers use the selected model. Symbolic layers execute generated code. Deployment is monitored for performance.
Production agents are monitored for performance drift. If performance degrades, the system can roll back to a previous selection or deploy a better-performing model.
Enterprises running on general intelligence achieve autonomous operations, continuous improvement, and better outcomes.
Enterprise functions operate autonomously with persistent goal pursuit and intelligent execution.
Intelligence selection evolves. Performance improves. Systems get smarter over time.
Function-specific intelligence delivers optimized results for revenue, product, and operations.