AI features that hold up with real users
Assistants, agents and models built into your product. We focus on the data, guardrails and testing that make answers reliable.
Capabilities
Assistants over your own content
RAG over documents, help centres or catalogs, with cited sources.
AI agents and automations
Agents that carry out multi-step tasks through your APIs, with approval steps.
Chat and voice assistants
Website chat, phone agents and messaging bots, with handover to a person.
Computer vision
Detection and classification models for cameras and images, through to deployment.
Prediction and forecasting
Classic machine learning for demand, risk and anomaly detection.
Evaluation and MLOps
Test sets, logging and monitoring so quality is measured, not guessed.
Good fit if
- People keep asking questions your documents or data already answer
- A repetitive task spans several steps and tools
- An AI prototype works in a demo but not yet in production
Typical use cases
What we use
- Models
- OpenAIAnthropic ClaudeGoogle GeminiLlamaMistralWhisper
- Retrieval and agents
- LangChainLlamaIndexpgvectorFAISSsentence-transformers
- Machine learning
- PyTorchTensorFlowscikit-learnOpenCVHugging Face
- Serving and ops
- FastAPIDockerMLflowAWS SageMakerPostgreSQLRedis
Four steps, in this order
- 01
Collect real questions
- 02
Prototype and score against them
- 03
Build the product around it
- 04
Monitor and improve
Related work
Sarcoma & Brain Health AI
Patient-education AI assistants built on retrieval-augmented generation over clinician-approved content, with cited answers, emergency detection, and voice input.
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Cortex - Autonomous AI Engineering Platform
Turns a ticket on a board into a reviewed, tested pull request using a chain of five AI agents that plan, code, review, QA, and deploy, running locally.
View case study →Aisle - AI Sales & Support Assistant
AI shopping assistant for online stores that combines sales chat, customer support, and a talk-to-shop voice mode in one storefront app, grounded in the live catalog.
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Real-Time Fire and Weapon Detection
A computer vision system that detects fire and visible firearms in camera feeds in real time.
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ViA - AI Chatbot & Voice Assistant Platform
A self-service platform where businesses set up and publish their own AI chatbot and voice assistant without writing code.
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Traffic Monitoring System
A traffic monitoring system that tracks vehicle flow and congestion in real time.
View case study →What AI work do you do most often?
Assistants that answer questions from your own documents or product catalog, voice agents for phone calls, AI agents that complete multi-step tasks in your tools, and classic machine learning such as computer vision and forecasting.
Which models do you use?
Whatever suits the task and your constraints. We regularly use OpenAI, Anthropic Claude and Google Gemini models, and open models such as Llama or Mistral when data has to stay on your own servers. We design the code so the model can be swapped later.
How do you stop an assistant from making things up?
We ground answers in your own content using retrieval, ask the model to cite the source it used, restrict what it is allowed to answer, and test it against a set of real questions before launch. Where the stakes are high, a person reviews the output.
Is our data used to train public models?
No. We use API settings and providers that do not train on your data, and we can run models in your own cloud account or on your servers when that is required.
Have an AI idea to test?
Send us the use case and a few example questions. We will tell you honestly whether AI fits.