Jan 28, 2025
Introducing AI-6 TSSM:
Task-Specific Small Models
Enterprise AI’s Hidden Cost:
Paying for 1T Parameters When You Only Need 1%
The enterprise AI landscape is broken.
Modern enterprises operate at the intersection of complex systems - ERP data pipelines, real-time infrastructure monitoring, cybersecurity threat analysis, CRM exception handling - where automation must deliver deterministic results without absurd costs. Yet organizations default to deploying hundred-billion to trillion-parameter LLMs (self-hosted or API-based) for workflows and applications that need exact precision for specialized tasks, not broad capabilities.
The result? Three critical challenges:
Unpredictable costs - we’ve seen API tokens inflate overall spending by 10–100x in real-world use.
Wasted hardware - up to 80% GPU idle time, even for narrow, predictable tasks.
Security blind spots - feeding sensitive enterprise data into 100B-parameter black boxes can mean unnecessary exposure.
These challenges aren’t peripheral - they’re inherent in the architecture. General-purpose models treat capability as a stand-in for efficiency, forcing enterprises to shoulder the cost of unneeded overhead.
At AI-6, we’ve re-engineered AI for the enterprise.
Today, we share results from our private beta with enterprise customers – proving a radical truth: Enterprise AI use cases don’t need artificial general intelligence.
They need artificial SPECIFIC intelligence.
Specialization by Design
Rather than chasing massive parameter counts, TSSM (Task-Specific Small Models) zeroes in on precisely the intelligence your workflows demand. Think 1–2B parameters - a fraction of hundred-billion–parameter LLMs - with no need for GPU clusters.
Smaller by Choice
Built for CPU Deployment: TSSM models run on standard enterprise servers (or even edge devices), yet are powerful enough to drive mission-critical tasks like ERP transaction validation, cybersecurity anomaly detection, or IT ticket triage.
Engineered for Efficiency: We apply quantization, distillation, pruning, and other compression techniques to systematically remove unused parameters, drastically reducing model size. In addition, high-quality synthetic data powers much of our training pipeline, with human post-processing only when necessary - reducing labeling overhead without compromising accuracy. These efforts led us to develop a proprietary “task-per-byte” metric, now a de facto standard among some of our beta customers, to quantify how effectively each megabyte of model space translates to real-world outcomes.
Right-Sized Performance
Task-Focused, Not Domain-Focused: Each model targets a specific workflow - be it checking inventory consistency in SAP, isolating anomalies in security logs, or reconciling payment data in real time. By specializing at the task level, TSSM avoids the overhead that plagues general-purpose models.
Precision Without Waste: By concentrating on task-specific accuracy rather than broad, unused capabilities, TSSM delivers higher “task-per-byte” scores than one-size-fits-all LLMs.
Illustrative Cost Comparison: TSSMs vs. Large LLMs
Real-World Results
Fortune 200 Retailer: Achieved 89% lower costs vs. typical API-based LLM services by using TSSM for database activity monitoring - all while maintaining strict regulatory compliance.
Large Regional Bank: Deployed TSSM on CPU-based clusters for firewall anomaly detection, cutting infrastructure costs and downtime without needing GPUs.
No more choosing between bloated, one-size-fits-all LLMs or building fragile, in-house solutions that don’t scale. TSSM provides a practical path to true enterprise AI - on your infrastructure, under your control, and without runaway costs or security blind spots.
The Orchestration Layer: AI-6 TSMAs
TSSM models rarely operate in isolation. AI-6 Task-Specific Master Agents (TSMAs) coordinate them at scale, routing each request to the most suitable TSSM (or ensemble). This ensures tasks - from invoice reconciliation to network anomaly detection - are handled by specialized models without overloading any single resource.
Beyond dynamic routing, TSMAs also enforce compliance and governance by applying security guardrails and data redaction before sending requests to individual TSSMs. This streamlines adherence to internal policies and external regulations.
All decisions are recorded in a transparent audit log, enabling quick verification and minimal oversight. By uniting specialized TSSM models under one framework, TSMAs offer a scalable, secure approach to enterprise AI - avoiding the overhead and inefficiencies seen with large, general-purpose LLMs.
Looking Ahead
While frontier models continue advancing at breakneck speed, TSSM remains our bet for practical enterprise AI - and we’re doubling down on its evolution:
Self-Service TSSM Training
We’re developing a self-serve module on our platform, empowering enterprises to train and fine-tune TSSMs in-house. From uploading task-specific datasets to monitoring accuracy and cost-efficiency metrics, this feature will give teams full control over their AI lifecycle - without massive compute overhead.Sub-1B Parameter TSSMs
We’re pushing the boundaries of model compression and optimization to deliver TSSMs with hundreds of millions (rather than billions) of parameters. This further reduces compute requirements and costs, making them even more suitable for CPU-based deployments across large enterprises.Iterative Reinforcement Learning (RL)
We plan to integrate RL loops that continuously refine TSSMs for hyper-specific enterprise tasks. Because TSSMs focus on a narrower scope than general-purpose LLMs, each model can be retrained more frequently - adapting as new data emerges without incurring massive compute or downtime.
We’re just scratching the surface of what’s possible when task-specific intelligence drives enterprise workflows. At AI-6 we see a future where AI adapts to you, not the other way around. By streamlining overhead and focusing on real-world tasks, we free teams to innovate, solve bigger problems, and push their organizations forward.