The demo needs to become dependable.
It works in a controlled setting. Nobody can say what quality, latency or cost look like under real use.
Production AI engineering · LLM, RAG & voice
Your AI pilot works in the demo. We make it work in production.
Senior engineers who ship private LLM, retrieval and voice systems, measure them against real traffic, and tell you honestly when not to build.
3.4 s → 511 ms
p95 time to first audio on a live voice agent, after we rebuilt the pipeline
Once per frame
the expensive vision model runs only at index time, so every CCTV search costs a few small text calls
2–4 weeks
to build a measured prototype on your own data, after a short assessment
When we are useful
Bring us in when the model is not the only hard part.
We work where data, software, security and human operations meet. If your problem is one of these, we can usually help.
Useful information is scattered or sensitive.
Teams need cited answers across internal sources, with permissions respected and a deployment your security team can approve.
A valuable workflow has too much manual friction.
Documents, calls or decisions move slowly. You want automation without losing review or auditability.
You need an independent technical view.
Before a build, acquisition or investment, you need evidence on architecture, IP risk and build versus buy.
Measured work
Two systems in production, with the numbers.
Client names are withheld under NDA. Methods, architecture and references are available on a call.
iGaming · Voice AI
In production
A phone agent that answers six times faster.
In a voice conversation, the pause before the agent speaks is the whole experience. We measured every stage of a live outbound agent, tuned each one, then restructured the response path so the model output streams straight into speech. p95 time to first audio went from 3.4 seconds to 511 ms with the full agent logic, tool calls and guardrails in place.
LiveKit CloudTransport
DeepgramSpeech to text
GPT-4o-miniLanguage model
CartesiaText to speech
Before and after
p95 latency
Production benchmark
| Stage | Before | After | Change |
|---|---|---|---|
| First audio | 3,361 ms | 511 ms | −84.8% |
Security · Video analytics
In production
Search CCTV archives in plain language, and get an honest "nothing found".
An operator types "truck at a junction" and gets the right seconds of footage back, or an empty result instead of the nearest wrong clip. A vision model describes each frame once at index time, so a search costs only a few small text calls and never touches the vision model. The index covers what the describer is asked to name, so the prompt is the search boundary. Three open models, self-hosted, one config file.
Qwen3-VLDescribes each frame
QdrantVector search
RerankerScores and cuts off
ResultClip, or nothing
Production index
reranker score, top hit
20,000 recordings, 10 million indexed segments
| Operator query | Result | Score |
|---|---|---|
| "truck at a junction" | Correct clip | 0.997 |
| "road accident" | Correct clip | 0.991 |
| "crowd gathering" | Correct clip | 0.739 |
What we build
Three things we are very good at.
Every engagement starts from your constraint, not a package. These are the systems we ship most and measure hardest.
Voice AI agents
Real-time agents on the phone network: outbound campaigns, inbound handling, live transfer to a human. Every stage is measured, and short conversational turns answer in under a second.
- LiveKit and Twilio SIP telephony
- Streaming STT, LLM and TTS pipelines
- Voicemail and answering-machine detection
- Per-stage latency benchmarks
Private LLM & knowledge systems
Open or proprietary models in your cloud or on-premise, connected to your documents and databases. Cited answers, permissions respected, retrieval quality measured on real questions.
- vLLM and Kubernetes serving, OpenAI-compatible API
- Ingestion, hybrid retrieval and reranking
- Vision-language indexing for images and video
- GPU sizing and cost modelling
Evaluation & production hardening
The part most AI projects skip. We build the harness that tells you whether a change actually helped, then add the monitoring and fallbacks that keep it true after launch.
- Latency and cost budgets per pipeline stage
- Regression benchmarks against recorded baselines
- LLM-as-judge scoring with human review loops
- Drift monitoring and prompt versioning
Also: model fine-tuning, workflow automation with human review, technical due diligence for investors, and an on-call engineering retainer once you are live.
Our process
From first call to
production, in steps you can stop at.
We scope one workflow, agree how success is measured, and expand only when the evidence supports it. Most prototypes take two to four weeks.
01
Discovery call
A 45-minute working session with a senior engineer on the workflow, systems and constraints. Free, no commitment.
45 min · free02
Technical assessment
We inspect the data, infrastructure and current process, then write up the best option, its risks and the evidence needed to proceed. Fixed fee.
3–5 days03
Measured prototype
A constrained build on your representative data, with quality, latency and cost baselines agreed up front. You see real numbers before a larger commitment.
2–4 weeks04
Production rollout
Integration with your systems, monitoring and fallbacks, a documented operating model, and handover against acceptance criteria you signed off.
4–12 weeksWhy aore.ai
Engineers who stay through the hard part.
We are a small senior team that has shipped production systems in iGaming, fintech, EdTech and consumer mobile: LLM pipelines, retrieval systems, real-time voice, and the infrastructure underneath. We write the code, run the benchmarks, and stay until the numbers hold.
We will also tell you when AI is not the answer. A slow process is often a broken process, and a model on top of it just makes the breakage faster.
Talk to an engineer6+ yrs
Shipping production systems in regulated industries
5M+
Installs across consumer apps our engineers built
Open
Open models on your own infrastructure wherever they fit
More work
Beyond the two flagship cases.
EdTech · Consumer
Two AI tutors for Estonian, live on subscription
A spoken conversation partner that adapts to the learner's level, and a writing assistant that corrects correspondence with register and etiquette guidance. Estonian has almost no off-the-shelf AI tooling, so both were built from the ground up.
LLMSpeech-to-textTTSMobile
Fintech · Due diligence
Investor-grade technical due diligence
Two reports on a German fintech group for prospective investors: codebase ownership, architecture and data model review, a licensing-risk register and key-person analysis.
Code & IP auditArchitecture reviewLicensing risk
Consumer · Mobile AI
5M+ installs
AI-native consumer apps at scale
A portfolio of shipped mobile products combining on-device models with hosted LLM APIs: planning, messaging and productivity tools on iOS and Android.
SwiftKotlinOn-device ML
Client names are withheld by default. Architecture, benchmark methodology and references are available under a mutual NDA.
Get started
Bring one workflow. Leave with a clearer next step.
In 45 minutes we look at what you want to improve, the systems and data involved, and what would make a pilot worth shipping. Then we tell you whether to audit, prototype, build, or stop.
A senior engineer on the call, not a salesperson
No pitch deck or generic roadmap
A useful next step, even if it is not with us