AI that understands your business.
Generic AI knows a little about everything. Hrida AI builds and fine-tunes models for specialized business tasks - adapting model behaviour to your data, terminology, and workflows.
Custom
Domain
Fine-tune
Approach
Private
Deploy
Fine-Tuning Pipeline
Hrida AI · Custom Models
Domain Data
Structured Output
{ "category":
"billing_dispute"
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}
Benchmarked
Beyond Generic AI
Models built around
your business.
Off-the-shelf models may struggle with specialized terminology, structured outputs, and domain-specific requirements. Fine-tuning adapts a model to recurring tasks and examples from your business.
Higher task relevance
Adapt model behaviour to your domain, instructions, and expected outputs.
Consistent structured outputs
Build models for repeatable tasks: classification, extraction, and specialized text generation.
Optimized inference economics
Evaluate smaller, task-specific models as alternatives to larger general-purpose models.
Deployment flexibility
Choose a deployment approach based on your performance, privacy, and infrastructure needs.
What We Build
A smarter way
to build AI.
Four model types, each designed for a different business need - from domain knowledge to structured extraction to task-specific performance.
"The indemnifying party shall defend, indemnify and hold harmless the indemnitee from any claims arising out of breach of representations..."
{
"type": "indemnification",
"party": "indemnifying_party",
"scope": ["defend", "indemnify", "hold_harmless"],
"trigger": "breach_of_representations",
"risk_level": "high"
}Our Process
From business data
to specialized AI.
A structured engagement that starts with understanding your workload, not assuming fine-tuning is always the answer.
4
Engagement stages
Custom
Evaluation criteria
Private
Deployment option
Your
Data & ownership
Understand the use case
Identify the task, current limitations, expected outputs, and measurable success criteria.
Prepare the data
Evaluate and prepare representative examples for model adaptation and testing.
Adapt and evaluate
Fine-tune suitable models and benchmark against the baseline using task-specific evaluation criteria.
Deploy and improve
Integrate the selected model into your application and monitor performance as requirements evolve.
Why Fine-Tune
Built for measurable
business impact.
We evaluate the right approach for your workload rather than assuming fine-tuning is always the answer.
We evaluate before we build.
We assess whether fine-tuning, RAG, prompt engineering, or a combination of approaches is the right fit for your workload. The goal is measurable improvement - not model complexity for its own sake.