01
AI & Machine Learning
Models built for a specific job, evaluated against your data, and shipped where they can be monitored.
Evaluation runsvs baseline
Our modelBaseline
AI engineering & product development
We build the model, and everything around it
Vision systems, LLM applications, and the platforms that put them in front of users. Built by one team, from the data pipeline through to the interface someone actually uses.
- Computer vision, LLM applications, and the software around them
- Dedicated teams owning the full stack, from labelling to deployment
- Clients across eight countries and four continents
What we buildVision
one team
Computer vision systems
Detection, segmentation and tracking trained on your imagery, then packaged to run against real cameras and real throughput targets.
PyTorchONNX RuntimeEdge deploy
Explore thisThe tools we build with, day to day
- 2021
- Founded in Surat, India
- 30+
- Members in our team
- 10+
- Clients served
- 8
- Countries served
- 3+ yrs
- Longest running engagement
Building AI systems since day one
One office, in person
Since 2021
Across four continents
Running since 2023
The whole stack
A model is one layer of five
Teams usually arrive with the middle layer solved and the other four missing. The notebook works; there is no pipeline feeding it, no service wrapping it, no interface using it, and no way to tell when it starts drifting.
We build all five, because a gap in any one of them is what stops a model reaching production.
- 01
Data
Ingestion, validation, labelling, and the lineage to prove it
- 02
Model
Training, evaluation against a real baseline, calibration
- 03
Service
Versioned inference API, latency budgets, a rollback path
- 04
Product
The interface where a person acts on the output
- 05
Operations
Drift monitoring, cost tracking, a retraining runbook
What you get
The same standard, whatever we build
A web platform, a mobile app, a data pipeline or a model. The definition of finished does not change, and neither does what you end up owning.
- Acceptance criteria agreed up front
- Before we build, we write down what finished looks like and how it will be checked. No debate about scope at the end.
- Tested, with the gates in CI
- Tests, types and linting run on every change. Where a model is involved, an evaluation gate blocks a release that regresses.
- Deployed, not zipped and emailed
- Environments in code, a real deployment pipeline, monitoring, and a rollback path. Whether that is a web app or an inference service.
- Handover you can act on
- Written runbooks and decision records aimed at your engineers, so your team can extend and operate it without us.
What you end up owningevery project
- src/application code, reviewed
- tests/unit and integration suites
- .github/workflows/CI: test, build, deploy
- infra/environments as code
- docs/runbooks/deploy, roll back, operate
- docs/decisions/why it was built this way
- models/ + eval/if MLweights and metrics
Release gates
- tests
- types
- lint
- build
- eval(if ML)
A change that fails any gate does not reach production.
Selected work
Built, shipped, and still running
Dedicated teams owning a complete development stack, agents running operational work, and engagements that have held for years.
What we do
Four disciplines, one team
Most AI projects need a model, a data pipeline, and an application. Splitting those across vendors is where timelines go. We do all three.
02
Generative AI
LLM features that hold up outside a demo - grounded in your content, measured, and cost-bounded.
03
Product Engineering
The platforms, APIs, and infrastructure that turn a model into something a customer can use.
Deploys
On merge
Environments
In code
04
Data Services
Pipelines and labelled datasets. The part most teams underestimate, and the reason models underperform.
Dataset pipeline
- Ingest
- Validate
- Label
- Review
- Ship
Schema checks at the boundary, agreement measured before volume.
Plus 8 annotation and labelling services
How we work
The parts that decide whether it works
None of this is exotic. It is the discipline that separates a model that demos well from one that is still running a year later.
Evaluation before enthusiasm
Every model ships with a test set and a documented baseline. If we cannot measure whether a change helped, we do not claim it did.
One team, model to interface
The people training the model also build the application around it. No handoff, no translation layer, no arguing about whose bug it is.
Honest scoping
We will tell you when a problem does not need machine learning, and when your data is not ready for the model you want.
Humans on the uncertain cases
Confidence gets calibrated and low-confidence output goes to a reviewer. Automating the easy 90% beats guessing on the hard 10%.
Built to hand over
Reproducible pipelines, written runbooks, and documentation aimed at your engineers. You should not need us to keep it running.
Long engagements, not projects
Our longest engagement has run since 2023, with a dedicated team of 30 on that account. We optimise for the second year, not the first invoice.
How an engagement runs
Measured early, so surprises come cheap
The feasibility work happens in the first two weeks, before anyone has committed a quarter to it. If the answer is no, you find out while it is still inexpensive.
- 01
Scoping call
Week 0You describe the problem and show us the data. We tell you what looks feasible, what does not, and what we would need to find out first.
- 02
Data and feasibility review
Week 1We look at what you actually have - volume, labels, quality, gaps - and establish a baseline. This is where most surprises surface, which is why it comes early.
- 03
Proof on your data
Weeks 2–4A working prototype trained or built on your real inputs, with measured results against the baseline. You get a number, not a demo.
- 04
Build and integrate
Weeks 4–12The production system: pipelines, service, application, tests, and infrastructure. Deployed to staging early and reviewed with you every two weeks.
- 05
Handover or ongoing support
From launchRunbooks, documentation, and a walkthrough for your engineers. Or we stay on and keep running it - which is what most clients choose.
Client testimonials
What our clients say about working with us
We have worked with AnnotateHub for close to 3 years now. They handle all our image and video annotations, as well as computer vision consulting. They are an exceptional provider. They have excellent communication and understanding of our issues. They are both proactive and responsive. They have shown an impressive ability to ramp up resources for us on short notice and they have high standards for quality control. More than a supplier, they have been a strong partner and a part of our success.
Arthur Goujon
Founder & CTO, Sorted Tech
AnnotateHub has proven to be an outstanding partner in computer vision projects, delivering innovative solutions with unmatched expertise. Their services, including facial recognition, object detection, and scene analysis, have significantly enhanced our workflows. Their attention to detail and commitment to delivering high-precision labeling have consistently exceeded our expectations.
Assam
Co-founder, MiniAi Live
Blogs
From our blog
Content Moderation12 min read
A guide to content moderation: benefits, challenges & best approaches introduction
15, Jan 2024
Data Annotation12 min read
How quality data annotation services enhance autonomous vehicle performance
17, Feb 2024
AI in logistics12 min read
AI in logistics: transforming warehousing, delivery, and inventory management
12, March 2024
Questions
The things people ask first
If yours is not here, ask us directly - we answer scoping questions before any commercial conversation.
The first two weeks are a data and feasibility review. We look at what you actually have, establish a baseline, and tell you what accuracy is realistic. You get a written assessment, and if the answer is that this should not be built, we say so.
Start here
Tell us what you're building.
We'll tell you if we're the right team.
The first two weeks are a feasibility review, not a sales process. You get a written assessment and an honest read on what's achievable with the data you have.
- Operating since
- 2021
- Clients served
- 10+
- Team
- 30+
- Longest engagement
- 3+ yrs