The demo needs to become dependable.
It works in a controlled setting, but quality, latency, cost or ownership is unclear under real use.
Production AI engineering · Worldwide
Turn the AI idea into a system your team can rely on.
Aore is the hands-on engineering partner for product and technology teams moving beyond the demo. We design, build and operate private LLM, knowledge, automation and voice systems—with evaluation, observability and human fallbacks built in.
−40.7%
p95 first-audio latency after optimizing a production voice-agent pipeline
5M+
app installs across the consumer products our engineers have shipped
No lock-in
open-source and self-hosted options where they fit the constraints
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. These are the situations where a focused engineering partner creates the most leverage.
Useful information is scattered or sensitive.
Teams need cited answers across internal sources, with permissions and a deployment model security can approve.
A valuable workflow has too much manual friction.
Documents, calls, reports or decisions move slowly and need 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, data readiness and build versus buy.
Voice agent
A 40.7% reduction in p95 first-audio latency for a production voice agent.
In a voice experience, the pause between turns shapes the entire conversation. We optimized the real-time pipeline to make the agent respond faster while preserving flexibility and reliability.
iGaming · Voice AI · Company withheld
Production benchmark
Faster responses, more natural conversations.
The work focused on reducing the silence between a caller finishing a sentence and the agent beginning its response. Each stage was measured independently, then optimized against the end-to-end customer experience.
LiveKit CloudTransport & egress
DeepgramSpeech to text
GPT-4o-miniLanguage model
CartesiaText to speech
Before and after optimization
p95 latency
Production-path benchmark
| Pipeline measurement | Baseline | Optimized | Change |
|---|---|---|---|
| First audio | 3,361 ms | 1,993 ms | −40.7% |
| LLM time to first token | 2,597 ms | 1,026 ms | −60.5% |
| LLM total | 2,737 ms | 1,318 ms | −51.9% |
Beyond speed
The same end-to-end review also strengthened configuration reliability across the call platform. The result was not only a faster agent, but a clearer and more dependable operating path for future campaigns.
Capabilities
One engineering partner across the AI delivery stack.
Start with the business constraint, not a predefined package. We assemble only the model, data, integration and operating pieces the use case needs.
Private LLM Deployment
Deploy open or proprietary models in your cloud, VPC or on-premise environment. The architecture is shaped around your security, latency, control and cost constraints.
- Model selection, evaluation and sizing
- Kubernetes, vLLM and Ollama serving
- OpenAI-compatible API layer
- Cost modelling and GPU sizing
RAG & Knowledge Systems
Connect models to internal documents, databases and knowledge bases. Give teams cited answers grounded in company data, with retrieval quality measured against real questions.
- Document ingestion pipelines
- Semantic search (pgvector, Qdrant, Elasticsearch)
- Hybrid retrieval & re-ranking
- Hallucination mitigation
Voice AI Agents
Real-time conversational agents on the phone network — outbound campaigns, inbound handling, and live transfer to humans. Sub-second response latency, or it doesn't ship.
- Telephony orchestration (LiveKit, Twilio SIP)
- Streaming STT / LLM / TTS pipelines
- Voicemail & answering-machine detection
- Latency benchmarking & campaign analytics
Model Fine-tuning
Adapt a base model to a narrow domain, tone or task when prompting and retrieval are not enough. We establish the baseline first, then prove whether tuning earns its complexity.
- Domain adaptation (legal, finance, support)
- Dataset design and instruction tuning
- Evaluation and benchmark design
- Quantization for deployment
Process Automation
Identify the workflows bleeding the most time, then redesign them with LLM agents. Document processing, email triage, report generation, internal Q&A.
- AI agent design & orchestration
- Integration with existing tools (Slack, CRM, ERP)
- Human-in-the-loop workflows
- ROI measurement framework
Technical Due Diligence
Investor-grade assessment of an AI or software asset — architecture, code ownership, licensing exposure, and key-person risk. Also runs inward, as an AI readiness audit before you commit budget.
- Repository, LOC & contributor analysis
- Licensing & IP risk register
- Architecture and bus-factor review
- Data readiness & build-vs-buy analysis
Ongoing Support & Ops
AI systems need monitoring, review and iteration after launch. We provide embedded engineering support so performance stays visible and improvements remain evidence-led.
- Model drift monitoring
- Prompt versioning & A/B testing
- On-call engineering retainer
- Monthly performance reviews
Evaluation & Benchmarking
The part most AI projects skip. We build the harness that tells you whether a change actually helped — before it reaches your users, and after.
- Latency & cost budgets per pipeline stage
- Regression benchmarks against recorded baselines
- LLM-as-judge scoring & human review loops
- Transcript and output audits at scale
Our process
From first call to
production deployment.
We scope around one decision or workflow, agree how success will be measured, and expand only when the evidence supports it. Most prototypes take two to four weeks.
01
Discovery call
A 45-minute working session to understand the decision, workflow, systems and constraints involved. Free, with no commitment.
45 min02
Technical assessment
We inspect the data, infrastructure and current process, then define the highest-value option, its risks and the evidence needed to proceed.
3–5 days03
Proof of concept
We build a constrained prototype on representative data, with explicit quality, latency and cost baselines before any larger commitment.
2–4 weeks04
Production rollout
We integrate with your systems, add monitoring and fallbacks, document the operating model and hand over against agreed acceptance criteria.
4–12 weeksWhy aore.ai
Engineers who stay through the hard part.
Aore is a small senior engineering team for companies that need delivery, not a strategy-only engagement. We can start with an audit, a constrained prototype or a production rescue.
We're a small team of engineers who have shipped production systems in iGaming, fintech, EdTech and consumer mobile — LLM pipelines, retrieval systems, real-time voice, and the delivery infrastructure underneath them. We write the code, run the benchmarks, and stay until the numbers hold.
And we'll tell you when AI isn't the answer. A slow process is often a broken process; a model on top of it just makes the breakage faster.
Talk to an engineer5M+
App installs across the products we've shipped
6+ yrs
Shipping production systems in regulated industries
No lock-in
Open-source and self-hosted by default
More selected work
Delivery across diligence, knowledge, compliance and consumer AI.
The voice-agent case is our most deeply documented example. These secondary cases show the broader engineering range; client names remain withheld under NDA.
Fintech · Due Diligence
Investor-grade IP & architecture due diligence
Two full technical due-diligence reports on a German fintech group — its payments platform and its media arm. We mapped the codebase and its ownership, reviewed the architecture and data model, and delivered a licensing-risk register and key-person analysis for prospective investors.
Code & IP auditArchitecture reviewLicensing risk
EdTech · Estonian
Estonian AI tutor & writing assistant
Two shipped consumer assistants for Estonian learners. One handles spoken conversation practice with voice in and speech out, adapting to the learner's level; the other drafts and corrects written correspondence with register and etiquette guidance. Estonian has almost no off-the-shelf AI tooling — both were built from the ground up and launched on subscription.
LLMSpeech-to-textTTSMobile
Civic tech · Media
In design
Editorial scoring at news speed
A scoring pipeline for an Estonian civic-tech publisher: incoming news is matched against a large corpus of tracked public commitments, then a model scores each pairing, recommends a status change and cites the supporting sentence. Editors review suggestions inside their existing CMS — the model proposes, a human decides.
Vector searchLLM-as-judgeHuman-in-the-loop
iGaming · Compliance
KYC provider integrations
Gateway build and platform integration for major identity-verification providers across multiple UK-licensed operators — onboarding checks wired into existing compliance frameworks without disrupting live customer flows.
Identity verificationComplianceJVM backend
Consumer · Mobile AI
5M+ installs
AI-native consumer apps
A portfolio of shipped mobile products combining in-house models with hosted LLM APIs — planning, messaging and productivity tools across native iOS and Android, live on both stores.
SwiftKotlinReact NativeOn-device ML
Client names and engagement specifics are withheld by default. We're happy to go deeper — architecture, benchmark methodology, references — under a mutual NDA.
Get started
Bring one workflow. Leave with a clearer next step.
In 45 minutes, we'll look at what you want to improve, the systems and data involved, and what would make a pilot worth shipping. We'll tell you whether to audit, prototype, build—or stop.
No pitch deck or generic roadmap
A senior engineer on the call
A useful next step, even if it is not with us