Data Services for LLMs

End‑to‑end data design, training, and evaluation services to turn your base model into a reliable AI assistant.

We provide comprehensive AI training services designed to enhance your model's performance across all critical dimensions.

From prompt engineering and Chain of Thought reasoning to supervised fine‑tuning, RLHF, evaluation, and multimodal labeling, we help you build models that are accurate, aligned, and production‑ready.

01

Finance

  • Asset Management
  • Capital Markets
  • Insurance
  • Banking
  • Payments
02

Healthcare

  • Digital Health
  • Medical Devices
  • DiGA
  • GenAI for Healthcare
  • Clinical Trials
  • Health Information Systems (HIS)
03

CPG & Retail

  • eCommerce
  • Logistics
  • Customer Engagement
  • Fashion
  • Supply Chain Management
04

Travel Tech Solutions

  • Travel
  • Aviation
  • Cruise and Ferry
  • Hospitality
  • Agency Solutions
05

Media & Entertainment

  • Video Business
  • Sports Betting
  • Art Market
  • Music Business
  • Book Publishing
  • Digital Media
06

Mobility & Manufacturing

  • Digital Battery Passport
  • Industry 4.0
  • Smart Car Solutions & Services
  • Smart Environments
07

Education

  • EdTech Data Platforms
  • AI-first EdTech Solutions
  • LMS and Learning Platforms
  • Next-Gen Education Platforms

What's Included

Prompt Engineering — Precise, effective prompts that unlock your model's full potential.
Precision Prompt Design — Prompts crafted for specific tasks, domains, and response formats.
Performance Optimization — Iterative prompt tuning to improve quality and reduce failures.
Consistent Behavior — Templates and guardrails that keep outputs stable across use cases.
Multi‑step Reasoning — Structured prompts that guide models through complex workflows.
Chain of Thought — Datasets that teach models to 'show their work' on complex problems.
Step‑by‑step Reasoning — Training data that encourages gradual, structured thinking.
Logical Connections — Examples that emphasize causal links and intermediate conclusions.
Transparent Thinking — Outputs that are easier to inspect, debug, and trust.
Improved Accuracy — Better answers on multi‑hop and reasoning‑heavy tasks.
Supervised Fine‑Tuning (SFT) — Instruction‑response pairs tailored to your domain.
Curated Instruction Pairs — High‑quality examples that teach desired behaviors.
Task Specialization — Datasets focused on your most important workflows.
Behavior Alignment — Training to match tone, style, and policy requirements.
Performance Enhancement — Systematic improvement on target benchmarks and tasks.
Evaluation — Comprehensive assessment frameworks for quality, safety, and alignment.
Custom Benchmarks — Scenario‑specific tests that reflect your real‑world usage.
Performance Metrics — Quantitative KPIs for accuracy, robustness, and coverage.
Quality Assurance — Human‑in‑the‑loop checks before and after deployment.
Rubrics & Scoring Frameworks — Clear, consistent criteria for rating model outputs.
Multi‑turn Dialogue — Conversation datasets that maintain context over long chats.
Conversational Flow — Scripts that model natural, human‑like interactions.
Context Retention — Data that teaches models to remember and reuse prior turns.
Reinforcement Learning from Human Feedback (RLHF) — Aligning behavior with human preference data.
Reward Model Training — Labeling and modeling human choices over model outputs.
Policy Optimization — Using reward models to steer and improve policies.
Value Alignment — Guardrails that reduce harmful, off‑brand, or low‑value responses.
Multimodal Labeling — Annotation for text, images, audio, and video.
Cross‑modal Understanding — Datasets that connect text with other modalities.
Rich Annotations — Detailed tags, attributes, and relationships for complex assets.
Precise Labeling — High‑consistency labels suitable for high‑stakes use cases.
Diverse Modalities — Pipelines that handle image, audio, video, and document data.

Project Details

Timeline

Typically 6–16 weeks depending on scope and data volume

Pricing

Custom packages based on model size, data needs, and evaluation depth

Benefits

More accurate, reliable responses on your real‑world tasks.

Models that can explain their reasoning instead of giving opaque answers.

Closer alignment with your brand voice, policies, and risk requirements.

Higher‑quality training and evaluation data with clear, auditable structure.

Faster iteration from prototype to production using reusable datasets and rubrics.

Coverage across text‑only and multimodal (text, image, audio, video) scenarios.

Our Process

1

Discovery & Objectives

We clarify your model goals, target use cases, risk constraints, and success metrics.

2

Data & Schema Design

We design prompt templates, Chain of Thought formats, rubrics, and labeling schemas for each service area.

3

Collection, Labeling & SFT

We collect and annotate data (prompts, dialogues, feedback, multimodal assets) and build instruction‑response and RLHF datasets.

4

Training & Evaluation

We run SFT / RLHF training loops and evaluate models against custom benchmarks, rubrics, and quality checks.

5

Deployment & Iteration

We help you deploy, monitor, and continuously improve your models with new data, tests, and feedback.