We're Hiring: Interns 🚀
We're hiring interns for Full-Stack Development and Social Media Marketing. Join Enally to work on real products, campaigns, and ideas in a fast-moving startup ecosystem.
Supervised Fine Tuning - ML
We prepare high-quality ML and LLM datasets for supervised fine-tuning, evaluation, and model improvement. From data collection and cleaning to annotation, instruction formatting, quality checks, and dataset versioning, we turn raw data into training-ready datasets built for reliable model performance.
SageMaker
Azure Data Factory
Python
PyTorch
Hugging Face
+9
ai
Training data you can trust.
Data Collection
Gather relevant domain data from approved sources and existing datasets.
A focused dataset aligned with your model objectives.Cleaning & Deduplication
Remove duplicates, noise, malformed records and low-quality examples.
Cleaner data with less noise and redundancy.Data Annotation
Apply consistent labels, instructions, responses and quality guidelines.
Consistent examples ready for supervised learning.SFT Dataset
Transform curated examples into structured instruction-response training data.
Training-ready datasets for supervised fine-tuning.Evaluation Data
Create held-out test sets and evaluation examples to measure model quality.
Reliable benchmarks for comparing model performance.Quality Control
Validate schema, duplicates, consistency, formatting and sample quality before delivery.
Production-ready datasets with reproducible quality checks.What's included.
Data Collection
Data Cleaning & Deduplication
Data Annotation
Instruction-Response Datasets
SFT Dataset Formatting
Data Quality Validation
Evaluation Datasets
Dataset Versioning
This is right for you if...
Teams building or fine-tuning LLMs
Companies with raw data that needs to become training-ready
Products needing domain-specific AI models
Teams creating instruction-tuning datasets
ML teams improving model responses
Companies building evaluation and benchmark datasets
How we do it.
01
Define model goals and dataset requirements
02
Collect and consolidate relevant data
03
Clean deduplicate and normalize the data
04
Create high-quality instruction-response examples
05
Apply annotation and quality guidelines
06
Validate datasets through automated and human review
07
Format and version datasets for SFT and evaluation
Tools & tech we use.
SageMaker
Azure Data Factory
Python
PyTorch
Hugging Face
Datasets
JSONL
Parquet
Pandas
NumPy
Label Studio
LLM APIs
SQL
Git
Common questions.
We take raw data, clean and annotate it, format it into instruction–response pairs, and produce fully versioned, quality‑checked datasets ready for fine‑tuning state‑of‑the‑art ML and LLM models.
We follow a multi‑step pipeline: automated cleaning, human annotation, double‑blind verification, instruction consistency checks, and continuous versioning. Each dataset undergoes strict QA before delivery.
Our pipeline handles text (dialogues, FAQs, manuals), code snippets, structured tables, and multimodal data where annotations link text to images or other modalities. We adapt formats to your model’s requirements.
From data receipt to the final versioned dataset, projects usually finish in 2–4 weeks, depending on volume, complexity, and annotation depth requested.
More services
Other ways we can help.
UI/UX Design
Clean interfaces that people actually enjoy using. Research-backed, goal-driven design.
Learn MoreDesign Systems
Reusable components, tokens and guidelines that keep your product looking consistent everywhere.
Learn MoreFrontend Development
Fast, responsive websites and web apps. Built to work perfectly on every device and browser.
Learn MoreBackend Development
Solid APIs and server logic that handle real traffic without breaking. Secure by default.
Learn More