Custom LLMs. Small, Fast & Domain-Specific.
Performix helps enterprises fine-tune small language models, integrate custom LLMs, and build RAG and agent pipelines aligned to domain knowledge, brand voice, performance and compliance needs.
Move beyond generic prompts.
Small language models can deliver strong performance for focused enterprise tasks while reducing latency, inference cost and deployment complexity.Fine-tuning can reinforce domain behavior, tone and structured outputs, while RAG keeps changing business knowledge current.
Choose smaller models when the task does not require frontier-model scale.
Fine-tune tone, format, domain terminology and repeatable task behavior.
Support controlled deployment patterns with enterprise security requirements.
Custom LLM services built for production AI.
We translate business requirements into secure, observable and scalable model patterns for customers, employees, partners and AI agents.
SLM Selection & Benchmarking
Evaluate suitable small and open models across cloud, edge and on-device use cases.
Custom Fine-Tuning
Adapt domain terminology, brand voice, classification, extraction and structured outputs.
RAG + Fine-Tuning
Combine learned model behavior with current enterprise knowledge.
Explore RAG-powered AI →Model Integration
Connect models with APIs, tools, function calling, MCP and enterprise applications.
Explore API Integrations →Deployment & Optimization
Optimize inference for cloud, GPU, edge and controlled enterprise environments.
Governance & Compliance
Build access controls, auditability, data protection and model governance.
Explore AI Governance →Connect the model to the intelligence around it.
Fine-tuning is only one part of production AI. Performix connects the model with enterprise knowledge, tools and governed workflows so it can perform useful work.
From assessment to production models.
We operationalize fine-tuning and integration so model initiatives can scale safely across users, customers and AI workflows.
Assessment & Use-Case Alignment
Inventory tasks, data sources, KPI baselines and risk tiers before choosing a model path.
Model Selection & Data Prep
Benchmark candidate models and prepare the data needed for tuning and evaluation.
Fine-Tune & Integrate
Train, evaluate and connect RAG, tools, APIs and model-serving infrastructure.
Scale & Govern
Monitor quality, drift, latency and cost while establishing governance controls.
Make enterprise AI faster, leaner and more controlled.
Design model infrastructure around the outcomes your business actually needs.
Use smaller models where they deliver the required quality.
Optimize inference for latency-sensitive tasks.
Reinforce tone, structure and domain behavior.
Keep changing facts current while model behavior stays consistent.
Support controlled enterprise deployment patterns.
Track model quality, latency, usage and cost.
Complete the enterprise AI stack.
Custom models work best when the surrounding data, knowledge, integrations and governance are designed together.
Data Strategy
Prepare the data foundation required for reliable AI and model initiatives.
Explore Data Strategy →RAG-Powered AI
Connect models to trusted, current enterprise knowledge.
Explore RAG →Agentic AI
Give AI models governed access to tools, workflows and business actions.
Explore Agentic AI →API Integrations
Connect AI models with enterprise applications and systems.
Explore API Integration →AI Governance
Establish controls for secure, compliant AI deployment.
Explore AI Governance →AI Consulting
Align your AI roadmap, architecture and implementation priorities.
Explore AI Consulting →From model capability to business impact.
Custom AI becomes valuable when it improves how people work, access knowledge and interact with business applications.
BizBot — AI-Powered App Assistant
An AI assistant designed to work directly inside business applications and help users discover features, get answers and take meaningful next steps.
Designed for deployment in days, even for complex applications.
Understands user intent and provides relevant follow-up guidance.
Helps users discover features, explore solutions and move toward meaningful actions.
RAG AI Assistants for Real Business Operations
Performix's RAG approach turns internal policies, manuals, SOPs, engineering standards, contracts and operational knowledge into conversational enterprise intelligence.
Employees can ask questions instead of searching through fragmented document systems.
Reduce repetitive internal support requests and manual knowledge lookup.
Help teams spend less time searching and more time executing meaningful work.
Explore Performix's AI solutions in action.
Model expertise beyond a generic AI implementation.
Compare the delivery capabilities that matter when taking custom models from experimentation to production.
| Capability | Performix | Big-4 | Platform Firms |
|---|---|---|---|
| SLM selection & benchmarking | ✓ | Varies | Platform-specific |
| LoRA / QLoRA fine-tuning | ✓ | Varies | Model-dependent |
| RAG + fine-tuning hybrids | ✓ | Varies | Varies |
| MCP, function calling & SDK integration | ✓ | Varies | Platform-dependent |
| Deployment & optimization | ✓ | Varies | ✓ |
| Governance & compliance | ✓ | ✓ | Varies |
Frequently asked questions.
What is custom LLM fine-tuning?
Custom fine-tuning adapts a foundation model to defined enterprise behaviors, terminology, formats or task patterns. Performix evaluates whether fine-tuning is actually required before selecting the model and tuning approach.
When should we use an SLM instead of a frontier model?
An SLM can be a strong fit for focused tasks where latency, inference cost, deployment control or privacy matter. The right choice depends on the actual business task and evaluation requirements.
How do you decide between RAG and fine-tuning?
RAG is useful when the model needs access to changing enterprise knowledge, while fine-tuning is better suited to repeatable behavior, style, terminology or task patterns. Many systems use a hybrid approach.
What data do you need for fine-tuning?
Requirements depend on the task. Performix starts by defining the desired behavior and evaluation criteria, then helps curate high-quality training examples and appropriate data controls.
Can custom models be deployed in our VPC?
Deployment architecture can be designed around enterprise security, data residency, network and operational requirements, including controlled VPC or self-hosted model-serving environments.
Do you support AI agents and tool calling?
Yes. Model integrations can include function calling, MCP servers, agent tool schemas and application SDKs so models can interact with governed enterprise workflows.
How do you approach governance and compliance?
Governance can include access controls, audit logging, data-handling rules, PII or PHI protections, deployment boundaries and model-risk documentation.
What does a typical assessment cover?
The assessment looks at the business task, current model approach, data availability, performance requirements, integration needs, security constraints and the path to production.
Make your AI faster, cheaper and domain-specific.
Start with a focused technical conversation about model selection, fine-tuning, RAG, integration and deployment.
Find the right model path.
Tell us what you want your AI to do. We'll help identify whether fine-tuning, an SLM, RAG or model integration is the right fit.
Clarify the business outcome and use case.
Evaluate SLM, LLM, RAG and fine-tuning options.
Define integration, deployment and governance needs.
Start with the business problem. We'll help define the technology.