Smaller Models, Deeper Intelligence
Own a competitive moat, not a shared capability.
Small Language Models (SLMs) represent a strategic shift from renting a shared capability to owning a competitive moat. While powerful APIs from providers like OpenAI and Gemini offer rapid AI integration, they give you no unique edge—your competitors are using the same generalist tools.
Think of a generic model as a general practitioner; it has broad knowledge but lacks a deep understanding of your business. An SLM, trained on your proprietary data and workflows, acts as a specialist.
This focused approach delivers an AI that is fluent in your specific domain, resulting in a faster, more accurate, and highly cost-effective system that understands your world inside and out.
The three failures of one-size-fits-all LLMs
Production LLMs unlock value, but performance, cost, and governance drift at scale. The bottleneck is fit.
Accuracy Drift. A model-fit problem.
Generic models don't speak your business. They miss edge cases, hallucinate in ambiguous flows, and produce inconsistent outputs (formats, fields, reasoning). As volume grows, "pretty good" becomes operational risk.
Latency and Cost Tax. An unit economics issue.
Every call carries overhead: large prompts, network hops, and variable response time. At real traffic, the math gets painful. P95 latency climbs and unit economics become unpredictable.
Privacy and Governance Risk. An architecture requirement.
If sensitive data must stay inside your environment, external APIs can become a blocker. You need deployment control, auditability, and data residency, not "trust us."
A custom SLM system built for scale
We do not just fine-tune a model. We deliver a deployable system: training, evaluation, optimization, and secure deployment aligned to your KPIs.
Accuracy Locked In. Stable behavior, fewer escalations.
Accuracy Locked In. Stable behavior, fewer escalations.
We define task boundaries, build high-signal datasets, and create an evaluation harness that tests real workflow outcomes, not generic benchmarks. Then we train a specialist model that follows your rules and outputs consistent formats.
Fast, Predictable Inference. Lower cost per query, predictable scale.
Fast, Predictable Inference. Lower cost per query, predictable scale.
We right-size the model for your workload and optimize serving with quantization, caching, and throughput tuning. You get stable P95 performance and unit economics that hold as usage grows.
Private Deployment with Governance. Full control over your data, infrastructure & compliance.
Private Deployment with Governance. Full control over your data, infrastructure & compliance.
We deploy inside your boundary (VPC or on-prem), integrate access control, logging, and guardrails, and align to your compliance and data residency needs.
Use cases, engineered for SLMs
We de-risk these high-volume workflows with SLMs tuned for speed, cost, and consistency.
Real-time voice agents
Execute complex, multi-turn conversations without frustrating lag.
Document processing
Affordably classify and extract data from millions of files at scale.
Private & performant RAG
Power internal knowledge bases with instant, accurate, and verifiable answers.
Domain-specific assistants
Build assistants that understand your private libraries and internal coding standards.
On-device AI for IoT & edge
Deploy lightweight, quantized SLMs directly onto resource constrained hardware for real-time, offline tasks.
Low-latency semantic tools
Power applications requiring instant search, routing, and more with our high-speed semantic search capabilities.
Business impact of a custom SLM
We optimize your model for the metrics that matter, so performance improves across the board.
2-5x faster
Better response times for real-time experiences
50-90%
More throughput with less compute
3-10x
Higher volume without runaway infra
Data stays
VPC / on-prem / edge deployment options
Predictable
Stable formats, fewer retries, fewer escalations