Chatbots & knowledge assistants
Help employees or customers find answers in approved information. Define scope, reference sources and hand off to a person when the assistant cannot answer reliably.
MLAIA AI Agents & Automation
AI that answers.
Workflows that act.
Connect your knowledge, business systems and AI models. Build useful assistants and automations with clear permissions, measurable quality and human oversight.
A specialist practice of MLAIA Data ScienceResearch, engineering & operational integration
From knowledge to action
Start with the work you want to improve.
Workflow automation & agents
Connect document intake, classification, extraction and follow-up to existing systems. Use deterministic steps where appropriate and explicit approval for consequential actions.
RAG & enterprise search
Retrieve relevant information before generating an answer. Build ingestion, indexing, retrieval and source citations around document permissions and freshness requirements.
Multimodal LLM integration
Connect models that handle text, documents, images or audio. Design the surrounding APIs, structured outputs and evaluation for the actual task.
Prompt engineering & evaluation
Develop prompts, tool instructions and output schemas against representative examples. Track quality, failure cases, latency and cost as the system changes.
Fine-tuning & model adaptation
Evaluate whether model adaptation improves domain language or task behavior beyond a prompt-and-retrieval baseline. Plan training data, held-out evaluation and deployment together.
Connected expertise
Voice, sensitive data and devices.
Speech-to-text & text-to-speech
Connect transcription and speech generation to assistants and workflows. Evaluate domain vocabulary, language coverage, noise and end-to-end delay.
Explore voice systems →Data de-identification
Prepare structured records and free text for approved uses. Define sensitive fields, masking rules, detection methods and residual-risk review before model access.
Explore sensitive-data workflows →Edge & physical AI
Run inference close to the sensor when latency, connectivity or data movement matters. Match the model and processing pipeline to the device.
Explore sensing and on-device AI →A practical delivery path
Prove the value. Then integrate.
Choose a workflow
Define users, inputs, permitted actions and a measurable business outcome.
Build a baseline
Use representative examples to assess retrieval, prompts and available models.
Connect & evaluate
Integrate the required systems and test errors, access controls and handoffs.
Operate & improve
Monitor answer quality, actions, latency and cost as data and models evolve.
AI Agents & Automation
Tell us what
you’re working on.
Share the problem, the data you have and what success would look like. We’ll discuss a practical next step.
Please don’t include confidential datasets, credentials or patient information.