AI / ML Engineering
AI that works in production, not just in the demo.
Assistants grounded on your data, agents that complete real tasks, document and voice automation, and classic machine learning for prediction, built with evaluation, guardrails and cost control from the start.
- RAG assistants
- AI agents
- Voice AI
- Document AI
- MLOps
- Evaluation
- Top Rated Plus on Upwork
- 14,355+ hours delivered
- 32+ specialists
- Since 2012
Everything AI / ML Engineering covers
01LLM applications and RAG
Answers grounded in your own content.
- Assistants over your documents with retrieval-augmented generation
- Knowledge bases with citations and access control
- Semantic and hybrid search
- Model choice across OpenAI, Anthropic Claude, Google Gemini and open models such as Llama and Mistral
02AI agents and automation
Software that completes the task, with you in control.
- Tool-using agents that call your APIs and systems
- Model Context Protocol (MCP) servers for your data
- Workflow automation with n8n, Make and Power Automate
- Human approval steps for sensitive actions
03Voice and conversational AI
Calls answered, logged and followed up.
- Inbound and outbound voice agents with Retell AI, Vapi and Twilio
- Call summaries and CRM updates
- Chat assistants for web, WhatsApp and Slack
04Document AI and computer vision
Paperwork turned into structured data.
- OCR and extraction for invoices, forms and contracts
- Classification, routing and validation
- Image and video analysis
05Machine learning and data science
Models that predict what matters to you.
- Forecasting, scoring, churn and recommendation models
- Python, scikit-learn, PyTorch and XGBoost
- Feature pipelines from notebook to production
06MLOps and cloud AI platforms
Deployed, monitored and repeatable.
- Amazon Bedrock and Amazon SageMaker
- Microsoft Foundry and Azure Machine Learning
- Google Gemini platform (formerly Vertex AI)
- MLflow, model registries and drift monitoring
- Vector databases: pgvector, Pinecone, Weaviate and Qdrant
07Safety, evaluation and cost
Measured quality, predictable spend.
- Evaluation sets and regression tests for prompts and agents
- Guardrails and PII redaction
- HIPAA-aware deployments inside your own cloud account
- Token, latency and model cost optimization
Need something specific?
Tell us what you are building. If it fits our engineers, we will scope it. If it does not, we will say so.
Talk to an engineerWhat our engineers work with
From first message to running in production
- 01
Understand
We review your goals, current setup and constraints, and confirm what success looks like.
- 02
Scope
A written scope with milestones, timeline, price and the access we will need.
- 03
Build
We work in your tools with regular demos, so progress is visible every week.
- 04
Run and improve
Handover, documentation and ongoing support, with one point of contact throughout.
Questions buyers ask us
Will our data be used to train public models?
We use provider settings that do not train on your data, and can run models inside your own cloud account when you need full control.
How do you know the AI gives correct answers?
We build an evaluation set from your real questions and test every change against it before release.
Can AI work with healthcare or other regulated data?
Yes, with the right setup: models deployed in your own cloud, access controls, logging and PII redaction.
What does an AI feature cost to run?
We estimate usage costs during scoping and design to keep them predictable, for example by caching and choosing the right model size.
Need something specific?
Tell us what you are building. If it fits our engineers, we will scope it. If it does not, we will say so.
Talk to an engineerReady to Start Building?
Get a free engineering consultation. No commitment. No pressure. Just answers.