Prototype
Atlas Voice
AI systemA voice agent prototype for inbound appointment booking, built to test where automated calls should stop and a person should take over.
- Conversation design
- AI engineering
- Telephony
- Integrations
AI & Automation
AI where it creates leverage. Not everywhere at once.
The useful question is never "how do we add AI". It is "which repeated, expensive, judgement-light task can a model do reliably enough to trust".
Technology
- LLM APIs
- Vector search
- Retrieval pipelines
- Node.js services
- Evaluation tooling
The problem
What usually goes wrong
AI gets added as a feature rather than applied to a cost. A chat box appears on the product, nobody uses it, and the initiative quietly ends.
Elsewhere, models get wired into real workflows without evaluation, so nobody knows how often the output is wrong until a customer finds out.
Our approach
What we do instead
We look for the tasks with volume, repetition and a checkable output, and we build for those first.
Every integration ships with evaluation, guardrails and a human path — so you can measure whether it is working rather than assume it.
Capabilities
AI opportunity assessment
Retrieval over your own documents and data
Internal knowledge assistants
Support and triage automation
Document extraction and processing
Content and drafting workflows
Model selection and cost modelling
Prompt and output evaluation
Guardrails, fallbacks and human escalation
Deliverables
What you actually receive.
Scoped per engagement — this is the shape of it, not a fixed package.
01Opportunity assessment with expected leverage
02Working integration inside your product or tools
03Retrieval and data pipeline where needed
04Evaluation harness and quality baseline
05Cost, latency and failure-mode documentation
06Escalation design
Process
How this work runs.
Six stages, adapted to the shape of this particular engagement.
01
Assess
Find tasks with volume, repetition and checkable output.
02
Prove
Build a narrow prototype and measure quality honestly.
03
Design
Decide where the human stays in the loop.
04
Build
Integrate into the real workflow, with guardrails.
05
Evaluate
Baseline accuracy, cost and latency.
06
Extend
Widen scope only where the evidence supports it.
Related work
Where this shows up.
Internal concepts and prototypes that exercise this capability. Labelled, because they are not client engagements.
Not under the enterprise API terms we build against. Data handling is agreed explicitly before anything is connected.
It will, sometimes. That is why we design the escalation path and measure error rates before anything runs unattended.
We model token and infrastructure cost per task during the prototype, so you know the running cost before committing.
Related capabilities
Next step
Think there is an AI opportunity in your business?
Tell us the situation in a couple of sentences. We will come back with what we would do first, and whether we are the right people for it.