Audit & Consulting
On-demand expert hours for AI strategy reviews, model audits, code reviews, and bug fixes. Perfect for short-term diligence.
- Architecture & cost audits
- LLM eval & benchmarking
- Same-week start
Code · Train · Deploy
Engineer the
intelligent
layer of your
business.
AtlasML is a dedicated AI/ML engineering studio. We embed senior teams into your stack to ship generative AI, machine learning, NLP, computer vision, and production-grade ML infrastructure — without the hiring overhead.
0+
Models shipped
0
Engineers on bench
100%
Client retention '25
Engagement Models
Three ways to work together.
Each engagement is built around your timeline and risk profile — from a one-week audit to a long-term embedded team.
Dedicated Resources
Senior AI/ML engineers embedded full-time into your team for ongoing development, training, and deployment.
- 1–8+ engineers per pod
- Your stack, your standups
- NDA & full IP transfer
End-to-End Delivery
Scoped milestones, predictable cost, defined ROI. Best for greenfield AI products and clear-cut MVPs.
- Discovery → deployment
- Fixed milestones & price
- 90-day handover support
What we build
A full-stack AI capability,
A full-stack AI capability,
on tap.
From a one-off prompt-engineering audit to a production agent platform. Ten disciplines, one team — all senior, all permanent staff.
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02
03
04
05
06
07
08
09
10
Generative AI & LLMs
RAG, fine-tuning, prompt orchestration, multi-agent systems with OpenAI, Anthropic Claude, and open-weight models.
Machine Learning & Predictive Analytics
Classification, regression, forecasting, recommendation systems — and the feature stores that keep them honest.
Natural Language Processing
Sentiment, classification, entity extraction, document intelligence and conversational interfaces in 40+ languages.
Computer Vision
Detection, segmentation, OCR, visual QA, and real-time video inference deployed to edge and cloud.
AI Agents & Autonomous Systems
Tool-using agents, multi-step workflow automation, and guardrailed agentic pipelines that don't hallucinate the kitchen sink.
MLOps & AI Infrastructure
CI/CD for models, observability, drift detection, GPU orchestration. Production from day one — never a Jupyter notebook in prod.
Custom Model Training
From data curation to LoRA fine-tunes to full-stack pre-training runs — on your data, for your domain.
Data Engineering
Pipelines, lakes, vector stores, and the boring infrastructure that makes the magic models actually possible.
Dedicated AI/ML Resources
Hand-picked senior engineers, embedded into your team. Pay monthly, scale up or down, retain all IP.
AI Strategy & Consulting
Where to invest, what to build, what to skip. Board-room clarity on AI roadmap, ROI, and risk.
Why AtlasML
Production-grade
Production-grade
by default.
Most AI work dies in the proof-of-concept stage. We build for the stage after — secure, observable, scalable, and measurably tied to business outcomes.
01
Built for real business use
We start with a clear-eyed review of your data and goals. If AI isn't the right answer, we say so. If it is, we scope it to where the leverage actually lives.
02
Custom solutions, never templates
Every model and pipeline is built around your data, your stack, your KPIs. No off-the-shelf wrappers dressed up as innovation.
03
Future-ready infrastructure
Cloud-native, observable, version-controlled. Your AI systems grow with you, not around you — and never lock you to one provider.
04
End-to-end ownership
Strategy, data, model, deployment, monitoring. One team, one accountability — instead of three vendors blaming each other on a slack call.
How we work
A six-step path
A six-step path
from idea to impact.
A structured, production-ready approach. No mystery, no "AI magic" — just engineering rigor applied to a new substrate.
01
Discovery
We sit with your team, audit your data, and map the AI opportunity to your real business constraints. You leave with a clear, honest answer — not a sales pitch.
02
Planning & Analysis
Architecture, model selection, data flow, cost ceilings, and a measurable success metric. Documented, signed off, then ruthlessly followed.
03
Model Design
We pick the right tool — fine-tuned LLM, classical ML, vision model, agent — based on cost, latency and accuracy, not on what's hyped this quarter.
04
Validation
Offline benchmarks, A/B harnesses, red-teaming. We don't deploy until we can prove the model beats the baseline on your data.
05
Integration & Testing
Wired into your APIs, apps, and workflows. Load tested, security-reviewed, fully documented for your engineering team.
06
Deploy & Monitor
Roll-out with shadow mode, drift detection, cost dashboards, and on-call coverage. Your model stays useful long after launch day.
Stack & Tooling
The tools we
The tools we
actually ship with.
A pragmatic stack — best-in-class for every layer, no religious wars. We meet your team where it already lives.
Foundation Models
GPT-4 / GPT-5
Claude Opus
Gemini
Llama 3
Mistral
Qwen
ML Frameworks
PyTorch
TensorFlow
Hugging Face
scikit-learn
XGBoost
JAX
Orchestration
LangChain
LlamaIndex
DSPy
Airflow
Prefect
Temporal
Vector / Data
Pinecone
Weaviate
pgvector
Qdrant
Snowflake
DBT
Cloud & GPU
AWS Sagemaker
GCP Vertex
Azure ML
Runpod
Modal
Lambda Labs
MLOps
MLflow
Weights & Biases
Kubeflow
BentoML
Triton
vLLM
Observability
LangSmith
Arize
Helicone
Datadog
OpenTelemetry
App & APIs
FastAPI
Next.js
tRPC
Supabase
Postgres
Redis
Selected work
Shipped & in production.
Measurable outcomes, not slide-deck demos. Every project below is live and serving real users today.
Generative AI · Recruitment
NLP · Customer Insights
Computer Vision · Manufacturing
AI Agents · Operations
GenAI · Media & Video
AI Resume Screening Engine
A semantic-search resume screener for a US recruiting firm — parses 20K resumes daily, ranks candidates with explainable scoring, and integrates directly into their ATS.
70%Hiring time saved
20KResumes / day
0.91Match F1 score
Feedback Classification at scale.
A fine-tuned multilingual classifier ingesting 1M+ open-ended survey responses per month — auto-tagging by theme, sentiment, and urgency.
1M+Responses / month
3 daysFrom 3 weeks
Real-time defect detection.
A vision pipeline on the factory floor catching surface defects in <30ms, deployed to NVIDIA Jetson edge boxes with central monitoring.
80%Faster inspection
30msEdge latency
Audit form auto-fill agent.
Multi-step agent that reads source documents and pre-fills compliance audit forms — saving auditors hours per case.
40%Accuracy lift
6 hrsSaved / audit
Video Summarizer for enterprise.
Speech-to-text + abstractive summarization for meeting and training video archives — searchable transcripts, chapter markers, action items.
30 hrsSaved / week
14 langsSupported
Industries we serve
Domain depth,
Domain depth,
not just demos.
Five sectors where we've shipped repeatable AI patterns — bringing both engineering muscle and domain literacy.
By the numbers
Outcomes,
Outcomes,
not output.
A look at what our teams have delivered over the last 24 months — across geographies, verticals, and model classes.
0+
AI projects delivered
0
Senior engineers in-house
0
Countries served
0%
Client retention 2025
Clients
Trusted by founders
Trusted by founders
& engineering leaders.
Real feedback from companies who hired AtlasML for production AI work — not pilots, not POCs.
AtlasML understood our problem in the first call. Six weeks later we shipped a credit-report automation that cut manual work by 65%. Smooth, senior, no hand-holding required.
The NLP pipeline they built cut our survey processing from three weeks to three days. Outstanding technical depth and rare communication discipline for an engineering shop.
Their computer-vision team reduced defect-detection time by 80% on our line. We hired them as a one-month audit; they're now on a yearly retainer.
The generative-AI system AtlasML built saves us 30 hours per week in content production. Genuinely the most ROI-positive engineering hire we've ever made.
Embedded two of their engineers into our team for six months. They left our codebase cleaner than they found it and our roadmap two quarters ahead. Rare.
We tried two other AI agencies before AtlasML. They were the only ones who said "no" to the wrong scope and rebuilt the right one. Adults in the room.
Field notes
From the
From the
workbench.
Long-form posts from our engineers on what's actually working in production AI right now — and what isn't.
Generative AI
AI predictions 2026: from general models to vertical LLMs & autonomous agents.
Why the next phase of AI value sits in domain-specific LLMs and agentic workflows — and how to start shipping them today.
Data ScienceHow ML consulting turns data into smarter business decisions.
A framework for getting from raw warehouse tables to decision-grade insights — without three years of platform work first.
Machine LearningAutomating insurance document processing with AI.
A walkthrough of an ingest-to-decision pipeline for claim documents — what to build, what to buy, what to skip.
FAQ
Common questions.
Don't see your question? Drop us a line — we'll respond within one business day.
What exactly does AtlasML do?
AtlasML is a dedicated AI/ML engineering studio. We embed senior teams to build generative AI applications, LLM integrations, AI agents, predictive ML models, computer-vision pipelines, and production-grade MLOps infrastructure — all under your IP, in your stack.
How does the dedicated-resource model work?
You get full-time AI/ML engineers embedded into your team for an agreed monthly capacity. They use your tools, attend your standups, and report directly to you — without the overhead of hiring, equipment, or HR. Pause, scale or rotate at 30 days' notice.
How fast can a team start?
Most engagements kick off within 7–10 business days after scope and roles are agreed. Urgent ramps are possible inside 72 hours — useful when a model goes sideways in production at 3am.
What technologies do you specialize in?
OpenAI, Anthropic Claude, Gemini, Llama, and Mistral on the model side. LangChain, LlamaIndex, DSPy for orchestration. PyTorch, TensorFlow, Hugging Face for training. Pinecone, Weaviate, pgvector for retrieval. AWS, GCP, Azure for deployment. We pick the stack to fit the problem, not the other way round.
How is pricing structured?
Three transparent models: hourly for short audits and consulting, monthly retainers for dedicated resources, and fixed-price for scoped end-to-end projects. After a free strategy call, you get a written quote with milestones and assumptions — no surprises.
Who owns the code and the model weights?
You do. 100%. Every engagement starts with an NDA and an IP-transfer clause. All source code, fine-tuned weights, evaluation datasets and infrastructure scripts are checked into your repositories from day one.
How do I get started?
Send us a brief description of what you're trying to build at info@atlasml.ai or fill the form below. We respond within one business day with a calendar link for a free 45-minute strategy call — no obligation, no sales theatre.
Let's talk
Build the