— For product and engineering teams
Applied AI Research, Tested Before It Ships.
We design and run AI experiments around your product, covering voice agents, memory, evaluation and safety. Every run is tracked and documented so your team can repeat it, review it and build on it.
Versioned from day one
Prompts, models, data and
parameters are recorded, so
every result can be traced to
the run that produced it.
Measured against a baseline
The simplest approach is
scored first, so any gain we
report is real.
Checks you can read
Evaluation sets and failure
reviews sit next to the results,
not in someone’s head.
Handed over, documented
Run instructions and notes, so
the work doesn’t depend on us.
— Trusted By Leading Companies
— Where We Can Help
From Experiment to Production AI System
01
Voice AI Research
Design and test real-time voice agents:
speech recognition, turn-taking,
interruptions and handing a call to a person.
02
LLM Experimentation and Evaluation
Run prompt and model experiments with
evaluation sets, run history and cost
tracking, so choices rest on results.
03
AI Memory and Personalisation
Build and test memory that keeps what users
shared over weeks, and updates it when
their situation changes.
04
Grounded Knowledge (RAG)
Connect models to your own documents and
measure how often answers are grounded,
cited and correctly refused.
05
AI Safety and Guardrails
Detect risky inputs, cut false alarms, and
design clear hand-offs to a person when the
AI shouldn’t answer.
06
Not Sure Where Yours Fits?
Answer a few questions about your product,
users and data. We’ll sketch a first
experiment plan for you.
— Example Workflow
Stages of AI Experimentation Process
01 — Frame
Start with a question worth answering.
A
Inputs
Product goal, user problem and constraints.
B
Deliverables
A written hypothesis, success metrics and scope.
C
Checks
Pass/fail thresholds agreed with your team before
anything is built.
02 — Data
Know what your data can support.
A
Inputs
Logs, transcripts, documents and sample conversations
B
Deliverables
A cleaned, versioned evaluation set.
C
Checks
Check coverage, duplicates and personal data. Flag gaps for your team to review.
03 — Experiment
Evaluate Options With Real-World Evidence
A
Inputs
Candidate approaches and the evaluation set.
B
Deliverables
Tracked runs with quality, latency and cost for each.
C
Checks
Same evaluation set for every run. Prompt versions and parameters recorded.
04 — Evaluate
Look past the average score.
A
Inputs
Run results and failure samples.
B
Deliverables
An evaluation report with failure analysis and a recommendation.
C
Checks
A person reviews the failures. Limitations are written down, not glossed over.
05 — Ship
Take what works into production.
A
Inputs
The chosen approach and production constraints.
B
Deliverables
A production service, monitoring and runbooks.
C
Checks
Roll out small first. Compare live metrics with the evaluation results.
01 — Frame
Start with a question worth answering.
A
Inputs
Product goal, user problem and constraints.
B
Deliverables
A written hypothesis, success metrics and scope.
C
Checks
Pass/fail thresholds agreed with your team before
anything is built.
02 — Data
Know what your data can support.
A
Inputs
Logs, transcripts, documents and sample conversations
B
Deliverables
A cleaned, versioned evaluation set.
C
Checks
Check coverage, duplicates and personal data. Flag gaps for your team to review.
03 — Experiment
Evaluate Options With Real-World Evidence
A
Inputs
Candidate approaches and the evaluation set.
B
Deliverables
Tracked runs with quality, latency and cost for each.
C
Checks
Same evaluation set for every run. Prompt versions and parameters recorded.
04 — Evaluate
Look past the average score.
A
Inputs
Run results and failure samples.
B
Deliverables
An evaluation report with failure analysis and a recommendation.
C
Checks
A person reviews the failures. Limitations are written down, not glossed over.
05 — Ship
Take what works into production.
A
Inputs
The chosen approach and production constraints.
B
Deliverables
A production service, monitoring and runbooks.
C
Checks
Roll out small first. Compare live metrics with the evaluation results.
— Example Workflow
What We've Learned, Written Down.
Voice AI
Your voice agent remembers things the user never heard
When a user interrupts, the generated reply and the spoken reply differ. If two components
both save the transcript, the wrong one can win. The fix is a single writer at the end of each
turn.
Telephony
Three reasons an AI call transfer goes to the wrong place
Private network addresses in PBX signalling, extension numbers mangled by phone-number
formatting, and tests that never reached the real API.
AI Memory
Memory that knows when something was true
Storing facts as time-stamped links lets an assistant tell “is anxious” from “was anxious in
week six”, without deleting history.
Cost and Latency
Prompt caching for multi-turn agents: where to put the breakpoints
Minimum prompt sizes, cache lifetimes, pre-warming, and how to measure whether caching
is actually working.
— Working Together
Built Together, Documented and Delivered
01 — Define
A plan you can review
Agree the question, data access, success
metrics and acceptance checks before
anything is built.
02 — Build and test
Results you see early
Share experiment runs and evaluation results
in short increments, using real inputs from
your product.
03 — Document and hand over
Knowledge stays with your team
Receive the code, evaluation sets, run
instructions and a clear plan for ownership
and support.
— Next Step
Talk to the engineers who will
build and manage your solution.
Talk to the engineers who will build and manage your solution.
A 30-minute call about your AI question. We’ll tell you how we’d test it, how long it would take, and whether it’s worth testing at all.