Ship faster, run reliably
Practical AI for real workflows
AI & Machine Learning Solutions
Generative AI, AI agents, RAG, computer vision and predictive models applied to real business problems, built on the LLMs, frameworks and data platforms that fit your case.
The problem
Every business is being told to adopt AI, but the useful question is where it actually saves time or improves decisions for you.
Teams have data in scattered systems, documents nobody can search, support queues that repeat the same questions and reports built by hand each month. Off-the-shelf AI tools do not know your data, and demos that impress in a meeting often fail on real inputs. You need a partner who can identify the problems worth solving, choose between commercial APIs, open-source models and classical machine learning on merit, and deliver something that runs reliably in production with measurable results.
What we do
- AI use-case discovery and feasibility assessment before committing to a build
- Generative AI assistants and AI agents integrated with your systems and data
- Retrieval-augmented generation (RAG) over your documents, with access controls
- Computer vision, NLP, recommendation and predictive analytics models
- LLM integration using OpenAI, Anthropic, Google or self-hosted open-source models
- MLOps: evaluation, monitoring, cost control and safe model deployment
- Data pipelines and vector databases that keep models fed with current data
How the work runs
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Step 1: Discover the right problem
We review your workflows and data to find where AI adds clear value, set success metrics, and check data quality, privacy and compliance constraints early.
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Step 2: Evaluate models on your data
We test candidate approaches on your real data: commercial LLM APIs, open-source models, classical ML or simple rules, comparing accuracy, latency, cost and data-residency needs.
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Step 3: Recommend with evidence
You get a recommendation backed by evaluation results, not a demo: the proposed architecture, expected running costs, limitations and a plan to reach production.
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Step 4: Build, deploy and monitor
We integrate the solution with your systems, add guardrails, evaluation and monitoring, train your team, and keep improving models as your data and needs change.
Technologies we often use
43 technologies
Pulled straight from our technology directory. It is a starting point, not a limit: we choose the right technology for your project, including tools that are not listed here.
Expertise levels
- Core
- Technologies used regularly in production.
- Experienced
- Technologies delivered with before.
- On request
- Can be taken on, staffed and scoped per project.
- AI Agents On request
- Anthropic Claude On request
- Chroma On request
- Computer Vision On request
- Generative AI On request
- Google Gemini On request
- Hugging Face On request
- Julia On request
- Keras On request
- LangChain On request
- Llama On request
- LlamaIndex On request
- MATLAB On request
- Mistral On request
- MLOps & Model Deployment On request
- Natural Language Processing (NLP) On request
- NumPy On request
- Ollama On request
- OpenAI API On request
- pandas On request
- pgvector On request
- Pinecone On request
- Predictive Analytics On request
- Python On request
- PyTorch On request
- Qdrant On request
- R On request
- Recommendation Systems On request
- Retrieval-Augmented Generation (RAG) On request
- scikit-learn On request
- TensorFlow On request
- vLLM On request
- Weaviate On request
Frequently asked questions
Short answers to the questions we hear most about this service. Anything else, ask us directly at hello@anyqora.com.
We are not tied to one model provider or framework. Depending on accuracy, cost, latency and data-residency requirements we may use OpenAI, Anthropic Claude or Google Gemini APIs, open-source models such as Llama or Mistral, or classical machine learning with PyTorch or scikit-learn. We choose the right technology for your project and show you the evaluation behind it.
No. We design solutions so your data stays within your environment or your own accounts with the model provider, use API settings that exclude training on your inputs where offered, and can deploy open-source models on infrastructure you control when confidentiality or regulation requires it.
Not always. Generative AI and retrieval-based assistants work with the documents and systems you already have, and pre-trained models cover many vision and language tasks without training from scratch. Where a custom model does need data, we assess what you have during discovery and tell you honestly whether it is enough.
We build evaluation sets from real examples before launch, measure accuracy against them, add guardrails and source citations where answers must be verifiable, and monitor behaviour in production. Results are reviewed with you regularly, in calls scheduled around your time zone wherever you are in the world, so you always see the real numbers.
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Have a AI & ML project in mind?
Tell us what you need. We reply [OWNER: within 1 business day] with clarifying questions, a suggested approach and the technologies that fit your project.