AI Agents
Agents that do the job, not agents that demo well.
Narrowly scoped agents that read, decide and act inside your systems, with the evaluation and guardrails that keep them trustworthy in production.
The problem
An agent demo is easy. An agent that behaves the same way on the four hundredth run, against messy real data, at three in the morning, is a different engineering problem.
Most agent projects fail not because the model is weak but because nobody defined what correct looks like, so nobody could tell when it drifted.
This is probably you if
- You have a prototype that works in a notebook and nowhere else
- Nobody can answer how often the current system is wrong
- The scope keeps expanding because the agent is supposed to handle everything
- You need an audit trail for decisions the system made on your behalf
What we build
Scoping and evaluation first
We define what correct output looks like before building, then hold the agent to it. Our team built an LLM-as-a-judge evaluation framework at Parallel for exactly this.
Retrieval over your own data
RAG pipelines that ground answers in your documents and records, so the agent cites rather than invents.
Guardrails and human checkpoints
Explicit stop conditions, confidence thresholds and approval gates, placed where the cost of a wrong action is real.
Monitoring after launch
Hallucination detection, drift tracking and quality dashboards, so degradation is something you see rather than something a customer reports.
Typical engagements
- Summarising unstructured clinical or case notes ahead of an appointment
- Research and discovery agents that assemble briefings from many sources
- Support agents that draft responses grounded in your own documentation
- Internal assistants that answer questions about your own operational data
Industries we do this in
- Healthcare
- Professional services
- Technology
- Education
Questions we get
- How do you keep an agent from making things up?
- Ground it in your data through retrieval, constrain what it is allowed to answer, and measure it continuously. We run hallucination detection rather than assuming good behaviour.
- Which model do you use?
- Whichever one survives the evaluation for your task. Model choice is a result of testing, not a starting assumption, and it changes as the field moves.
- Can it act on our systems, not just answer questions?
- Yes, and that is where the value usually is. It also raises the stakes, so actions get tighter scoping and explicit approval gates.
Tell us what is slowing you down.
A short call, no deck. If we are not the right people for it we will say so and point you at who is.