Multimodal annotation
Scope labeling across text, image, video, audio, documents, 3D and geospatial data. Agree the ontology, format and review process before scaling.
Turn raw data into AI-ready training data.
Structured labeling, evaluation and preparation for text, images, audio, video and LLM responses, with project-specific human quality control.
The business problemLess friction.
Less friction.
A clearer next step.
Start with the work that is getting in the way. Build a focused scope around the decisions and handoffs that matter.
- Raw data lacks consistent labels or usable structure.
- Ambiguous guidelines create disagreement between reviewers.
- Training and evaluation datasets need repeatable quality checks.
What is included
Practical capabilities. Clear boundaries.
A data annotation engagement is built around the parts your business actually needs.
LLM training & evaluation
Create and review prompts, rank responses, prepare fine-tuning examples and evaluate factuality, helpfulness and instruction following.
Collection & preparation
Assess sourcing rights, clean records and validate synthetic examples against an agreed specification.
Guidelines & quality assurance
Build annotation instructions, run a calibration pilot and use peer review, sampling and client feedback to resolve disagreement.
The AI-data division
21 ways to make data useful.
Text · Images · Video · Audio · Documents · 3D / LiDAR · Geospatial · Multimodal · LLM responses
01
Data Labeling & Classification
Turn unlabeled records into consistent categories using an agreed taxonomy.
Learn More02
Image Annotation
Add spatial and semantic labels to images for computer vision workflows.
Learn More03
Video Annotation
Label objects and events across frames while preserving temporal consistency.
Learn More04
Text & NLP Annotation
Structure language into entities, relationships and meaning for NLP models.
Learn More05
Audio & Speech Annotation
Create reviewed speech and audio labels with clear timestamp conventions.
Learn More06
Document & OCR Annotation
Map document text and layout into structured, reviewable fields.
Learn More07
3D & LiDAR Annotation
Scope spatial labeling for point clouds and 3D scenes around your ontology.
Learn More08
Geospatial & GIS Annotation
Label features in imagery and geographic data with explicit spatial references.
Learn More09
Multimodal Data Annotation
Align labels across connected text, image, audio or video inputs.
Learn More10
Generative AI & LLM Training Data
Prepare prompts, responses and judgments for scoped language-model workflows.
Learn More11
RLHF & Preference Ranking
Compare responses against explicit preferences and record reviewer rationale.
Learn More12
Supervised Fine-Tuning Data
Prepare consistent instruction-response examples for an agreed target behavior.
Learn More13
AI Model Evaluation
Evaluate model behavior against a repeatable, project-specific rubric.
Learn More14
AI Red Teaming & Safety Evaluation
Test agreed safety boundaries in a controlled, authorized evaluation scope.
Learn More15
Search Relevance Annotation
Judge query-result relevance using a calibrated scale and clear intent definitions.
Learn More16
Content Moderation & Safety Labeling
Label content against your policy taxonomy with defined escalation rules.
Learn More17
Data Collection & Data Sourcing
Organize data collection around approved sources, rights and coverage requirements.
Learn More18
Data Cleaning & Preparation
Make input data more consistent before annotation, training or analysis.
Learn More19
Synthetic Data Validation
Review generated examples for consistency, relevance and unwanted artifacts.
Learn More20
Annotation Quality Assurance
Build a repeatable review loop for label consistency and acceptance decisions.
Learn More21
Annotation Guideline Development
Turn task intent into practical instructions with examples and edge-case rules.
Learn MoreHuman-in-the-loop quality
Quality is a process. Not a percentage on a page.
Acceptance thresholds, review methods and sampling are agreed for each project. Calibration happens before volume.
Annotator review
Peer review
Quality reviewer
Sample audit
Client feedback loop
We document uncertainty and disagreement, revise guidelines with your team and keep an audit trail of corrections. Specialist medical, legal or financial review is available subject to project requirements and qualified reviewer availability.
ScaleForge Growth System™
One connected system. Six growth loops.
From the first research question to your next stage of growth, each step makes the next one stronger.
Discover
Research the market and customer.
Define
Turn evidence into priorities.
Build
Create the assets and workflows.
Connect
Join systems, people and channels.
Optimize
Use data to improve decisions.
Scale
Expand what is working.
What you receive
Deliverables you can review and use.
Formats, ownership and review stages are agreed before execution. Handover should make the next step easier for your team.
- Annotation guidelines and label schema
- Calibrated pilot batch
- Annotated dataset in agreed format
- Issue and disagreement log
- QA summary and handover
Project toolkit
Built around your workflow.
Client-approved annotation platformJSON / JSONLCSVCOCO
Who it is for
A practical fit for your team.
AI startupsComputer vision teamsNLP and LLM buildersResearch teams
Inspect the approach
An illustrative workflow.
See how an engagement could be structured. This example makes no client outcome claim.
Annotation QA Project
An illustrative pilot showing guidelines, review and disagreement resolution.
An example of the approach. No client outcome claimed.View Case StudyBefore we start
Questions about the work.
What is included in data annotation?
Scope is tailored around your goal. Typical deliverables include annotation guidelines and label schema, calibrated pilot batch, annotated dataset in agreed format, issue and disagreement log, qa summary and handover. We agree formats, review stages and acceptance criteria before starting.
How do we start and what do you need from us?
Start with your business challenge, audience, current workflow and available source material. We propose a focused scope, identify access requirements and agree a pilot or milestone plan.
Can you work with our current tools?
We plan around your existing systems where practical. Relevant tools may include Client-approved annotation platform, JSON / JSONL, CSV, COCO. Access, subscription costs and integration limitations are agreed in the scope.
How are quality and timelines agreed?
We define reviewable milestones and acceptance criteria for the specific project. Data complexity, volume, access and reviewer availability affect timelines. No fixed outcome or performance percentage is guaranteed.
Your next stage starts hereBuild a growth system
Build a growth system
that works smarter.
Start with one business challenge.
We will map the smallest practical next step.