Phase 2: Task-Specific SLMs
Fine-tuned small language models for unit tasks
The Problem
Enterprise AI typically starts backwards: vendors push generic solutions onto teams, forcing adoption of tools not tuned to actual needs. It's a solution in search of a problem. Commercial AI tools can't learn organization-specific patterns. They don't understand your investment decisions, RFP win patterns, or field operation standards. The AI you pay for never becomes a competitive advantage because it wasn't built around where your teams actually need help.
The Solution
Invert the approach: let teams identify where AI would actually help, then fine-tune task-specific models for those exact needs. At ~$214 compute per model and ~1 hour to train, experimentation is cheap. Teams can try an AI application, test it for a week, and decide based on results. Models that save time get refined and deployed. Models that don't fit cost an afternoon to learn from. Value emerges from real workflows, not vendor assumptions.
The Value
Teams discover where AI creates genuine value for their specific work, not where vendors claim it should. $214 per model creating competitive moat that deepens with use. The organization isn't just getting working models. It's learning which problems are actually worth solving with AI, creating institutional knowledge about where automation delivers return. Each proven task model becomes an expert component ready for Phase 3's division-level agents, but delivers immediate return regardless of whether you continue building.
Next: Phase 3 - Mixture-of-Experts Agents
Phase 3 merges your 14 task SLMs into 3 intelligent MoE agents—one per division. Each agent routes queries to the appropriate expert, combining specialized capabilities into multi-task assistants.
The Fundraising MoE can analyze investors, assess fit, analyze capacity, generate engagement strategies, and synthesize portfolio patterns—all by orchestrating the 5 experts you built in Phase 2.
Continue to Phase 3 →